Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the “Software”), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED “AS IS”, WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
Convertin
#> Warning: There is 1 article with missing first author surname and first author first#> name.#> Warning: There is 1 article with missing year of publication.
-#> Rows: 107Columns: 2
-#> ──Column specification────────────────────────────────────────────────────────
-#> Delimiter: ";"
-#> chr (2): parameter_type_short, parameter_type_full
-#>
-#> ℹ Use `spec()` to retrieve the full column specification for this data.
-#> ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.#> Warning: Unknown or uninitialised column: `other_delay_start`.#> Warning: Unknown or uninitialised column: `other_delay_end`.#> Note: the params dataframe does not have a covidence_id column
@@ -586,15 +581,8 @@
Entries with probability distrib
have parametric distributions).
ebola_data<-load_epidata("ebola")
-#> ℹ ebola does not have any extracted outbreaks
+#> ℹ ebola does not have any extracted outbreaks#> information. Outbreaks will be set to NULL.
-#> Rows: 107Columns: 2
-#> ──Column specification────────────────────────────────────────────────────────
-#> Delimiter: ";"
-#> chr (2): parameter_type_short, parameter_type_full
-#>
-#> ℹ Use `spec()` to retrieve the full column specification for this data.
-#> ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.#> ✔ Data loaded for ebola
We will again subset the data to just use the epidemiological
parameter table, and select those rows containing a serial interval.
@@ -711,15 +699,8 @@
Specifying the proba
parameters ($params).
ebola_data<-load_epidata("ebola")
-#> ℹ ebola does not have any extracted outbreaks
+#> ℹ ebola does not have any extracted outbreaks#> information. Outbreaks will be set to NULL.
-#> Rows: 107Columns: 2
-#> ──Column specification────────────────────────────────────────────────────────
-#> Delimiter: ";"
-#> chr (2): parameter_type_short, parameter_type_full
-#>
-#> ℹ Use `spec()` to retrieve the full column specification for this data.
-#> ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.#> ✔ Data loaded for ebolaebola_params<-ebola_data$params
Here we will use the serial interval for Ebola reported by Faye et al. (2015). This is stored, over two
diff --git a/articles/data_protocol.html b/articles/data_protocol.html
index a0df63150..19e0610d0 100644
--- a/articles/data_protocol.html
+++ b/articles/data_protocol.html
@@ -101,6 +101,8 @@
+
#> Error in get(paste0(generic, ".", class), envir = get_method_env()) :
+#> object 'type_sum.accel' not found
## Error in get(paste0(generic, ".", class), envir = get_method_env()) :
+## object 'type_sum.accel' not found
## Returning 125 results that match the criteria (100 are parameterised). ## Use subset to filter by entry variables or single_epiparameter to return a single entry. ## To retrieve the citation for each use the 'get_citation' function
@Manual{,
title = {epiparameter: Library of Epidemiological Parameters with Helper Functions and Classes},
author = {Joshua W. Lambert and Adam Kucharski and Carmen Tamayo},
- year = {2024},
+ year = {2025},
doi = {10.5281/zenodo.11110881},
url = {https://epiverse-trace.github.io/epiparameter/},
}
# S3 method for class 'epiparameter'
+lines(x, cumulative =FALSE, ...)
+
+
+
+
Arguments
+
+
+
x
+
An <epiparameter> object.
+
+
+
cumulative
+
A boolean logical, default is FALSE.
+cumulative = TRUE plots the cumulative distribution function (CDF).
+
+
+
...
+
further arguments passed to or from other methods.
+
+
+
+
+
Examples
+
ebola_si<-epiparameter_db(disease ="Ebola", epi_name ="serial")
+#> Returning 4 results that match the criteria (4 are parameterised).
+#> Use subset to filter by entry variables or single_epiparameter to return a single entry.
+#> To retrieve the citation for each use the 'get_citation' function
+plot(ebola_si[[1]])
+lines(ebola_si[[2]])
+
+
+
+
+
+
+
+
+
+
+
+
+
+
diff --git a/reference/plot.multi_epiparameter-1.png b/reference/plot.multi_epiparameter-1.png
new file mode 100644
index 000000000..0fa6c2f93
Binary files /dev/null and b/reference/plot.multi_epiparameter-1.png differ
diff --git a/reference/plot.multi_epiparameter.html b/reference/plot.multi_epiparameter.html
new file mode 100644
index 000000000..5453b57f1
--- /dev/null
+++ b/reference/plot.multi_epiparameter.html
@@ -0,0 +1,136 @@
+
+plot() method for <multi_epiparameter> class — plot.multi_epiparameter • epiparameter
+ Skip to contents
+
+
+
ebola_si<-epiparameter_db(disease ="Ebola", epi_name ="serial")
+#> Returning 4 results that match the criteria (4 are parameterised).
+#> Use subset to filter by entry variables or single_epiparameter to return a single entry.
+#> To retrieve the citation for each use the 'get_citation' function
+plot(ebola_si)
+
+
+
+
+
+
+
+
+
+
+
+
+
+
diff --git a/search.json b/search.json
index 29d51451c..c0cb2c2d4 100644
--- a/search.json
+++ b/search.json
@@ -1 +1 @@
-[{"path":"https://epiverse-trace.github.io/epiparameter/LICENSE.html","id":null,"dir":"","previous_headings":"","what":"MIT License","title":"MIT License","text":"Copyright (c) 2024 epiparameter authors Permission hereby granted, free charge, person obtaining copy software associated documentation files (“Software”), deal Software without restriction, including without limitation rights use, copy, modify, merge, publish, distribute, sublicense, /sell copies Software, permit persons Software furnished , subject following conditions: copyright notice permission notice shall included copies substantial portions Software. SOFTWARE PROVIDED “”, WITHOUT WARRANTY KIND, EXPRESS IMPLIED, INCLUDING LIMITED WARRANTIES MERCHANTABILITY, FITNESS PARTICULAR PURPOSE NONINFRINGEMENT. EVENT SHALL AUTHORS COPYRIGHT HOLDERS LIABLE CLAIM, DAMAGES LIABILITY, WHETHER ACTION CONTRACT, TORT OTHERWISE, ARISING , CONNECTION SOFTWARE USE DEALINGS SOFTWARE.","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/articles/data_from_epireview.html","id":"converting-from-epireview-entries-into-an-epiparameter-object","dir":"Articles","previous_headings":"","what":"Converting from {epireview} entries into an object","title":"Using {epireview} with {epiparameter}","text":"{epireview} package nicely provides epidemiological parameter data systematically reviewing literature, {epiparameter} provides custom data structures working epidemiological data R. Therefore, reading data {epireview} R package converting object provide greatest utility applied outbreak analytics. start simple example reading Marburg data {epireview} converting object using as_epiparameter() function {epiparameter} package. loads list four tables, specifically tibbles, contain bibliographic information ($articles), epidemiological parameters ($params), epidemiological models ($models), outbreak information ($outbreaks). start just using epidemiological parameter table convert information . parameters, subset data keep rows contain incubation periods Marburg. select first entry use first example: can simply pass epidemiological parameter set as_epiparameter() conversion. resulting contain parameterised probability distribution, instead contains range incubation period ($summary_stats), $metadata shows single case South Africa.","code":"marburg_data <- load_epidata(\"marburg\") #> Warning: There is 1 article with missing first author surname. #> Warning: There is 1 article with missing first author surname and first author first #> name. #> Warning: There is 1 article with missing year of publication. #> Rows: 107 Columns: 2 #> ── Column specification ──────────────────────────────────────────────────────── #> Delimiter: \";\" #> chr (2): parameter_type_short, parameter_type_full #> #> ℹ Use `spec()` to retrieve the full column specification for this data. #> ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message. #> Warning: Unknown or uninitialised column: `other_delay_start`. #> Warning: Unknown or uninitialised column: `other_delay_end`. #> Note: the params dataframe does not have a covidence_id column #> Note: the models dataframe does not have a covidence_id column #> Note: the outbreaks dataframe does not have a covidence_id column #> ✔ Data loaded for marburg names(marburg_data) #> [1] \"articles\" \"params\" \"models\" \"outbreaks\" marburg_params <- marburg_data$params marburg_incubation_period <- marburg_params[ marburg_params$parameter_type_short == \"incubation_period\", ] marburg_incubation_period #> # A tibble: 2 × 61 #> parameter_data_id article_id parameter_type parameter_value parameter_unit #> #> 1 c2a35e68034b72580654… 6 Human delay -… NA Days #> 2 0106582cf5ed3c52d5e9… 20 Human delay -… NA Days #> # ℹ 56 more variables: parameter_lower_bound , #> # parameter_upper_bound , parameter_value_type , #> # parameter_uncertainty_single_value , #> # parameter_uncertainty_singe_type , #> # parameter_uncertainty_lower_value , #> # parameter_uncertainty_upper_value , parameter_uncertainty_type , #> # cfr_ifr_numerator , cfr_ifr_denominator , … marburg_incub <- marburg_incubation_period[1, ] marburg_incub #> # A tibble: 1 × 61 #> parameter_data_id article_id parameter_type parameter_value parameter_unit #> #> 1 c2a35e68034b72580654… 6 Human delay -… NA Days #> # ℹ 56 more variables: parameter_lower_bound , #> # parameter_upper_bound , parameter_value_type , #> # parameter_uncertainty_single_value , #> # parameter_uncertainty_singe_type , #> # parameter_uncertainty_lower_value , #> # parameter_uncertainty_upper_value , parameter_uncertainty_type , #> # cfr_ifr_numerator , cfr_ifr_denominator , … marburg_incub_epiparameter <- as_epiparameter(marburg_incub) #> Using Gear (1975). \".\" _<journal not available>_. #> To retrieve the citation use the 'get_citation' function #> Warning: Cannot create full citation for epidemiological parameters without bibliographic information #> see ?as_epiparameter for help. #> No adequate summary statistics available to calculate the parameters of the NA distribution #> Unparameterised <epiparameter> object marburg_incub_epiparameter #> Disease: Marburg Virus Disease #> Pathogen: Marburg virus #> Epi Parameter: human delay incubation period #> Study: Gear (1975). \"<title not available>.\" _<journal not available>_. #> Distribution: NA #> Mean: NA [NA% CI: NA, NA] (Days) #> Median: NA [NA% CI: NA, NA] (Days) #> Range: [c(7, 8)] (Days) marburg_incub_epiparameter$summary_stats #> $mean #> [1] NA #> #> $mean_ci_limits #> [1] NA NA #> #> $mean_ci #> [1] NA #> #> $sd #> [1] NA #> #> $sd_ci_limits #> [1] NA NA #> #> $sd_ci #> [1] NA #> #> $median #> [1] NA #> #> $median_ci_limits #> [1] NA NA #> #> $median_ci #> [1] NA #> #> $dispersion #> [1] NA #> #> $dispersion_ci_limits #> [1] NA NA #> #> $dispersion_ci #> [1] NA #> #> $quantiles #> [1] NA #> #> $range #> [1] 7 8 marburg_incub_epiparameter$metadata #> $units #> [1] \"Days\" #> #> $sample_size #> [1] 1 #> #> $region #> [1] \"Johannesburg, South Africa\" #> #> $transmission_mode #> [1] NA #> #> $vector #> [1] NA #> #> $extrinsic #> [1] FALSE #> #> $inference_method #> [1] NA"},{"path":"https://epiverse-trace.github.io/epiparameter/articles/data_from_epireview.html","id":"creating-an-epiparameter-with-full-citation","dir":"Articles","previous_headings":"","what":"Creating an <epiparameter> with full citation","title":"Using {epireview} with {epiparameter}","text":"last example showed convert epidemiological parameter information, however, may noticed citation created contain information full citation. order provide complete citation <epiparameter> object, highly recommended know provenance parameters can correctly attribute original authors, need also provide bibliographic information {epireview} well epidemiological parameters. article data needs loaded {epireview} using epireview::load_epidata_raw() rather epireview::load_data() load_data() subsets bibliographic information provide: \"id\", \"first_author_surname\", \"year_publication\", \"article_label\" columns. need match entry epidemiological parameter table citation information article table ensure using correct citation parameter set. Thankfully, can easily achieved {epireview} provides unique IDs table link entries. Now can repeat example converting <epiparameter> shown , time pass bibliographic information well epidemiological parameter information create full citation. bibliographic information needs passed article argument. as_epiparameter() function S3 generic. familiar S3 object-oriented programming R, detail important, however, mean article argument explicitly function definition as_epiparameter() (.e. show autocomplete typing function shown read function help page ?as_epiparameter()). Instead, argument specified part ... argument. article argument required converting data {epireview} <epiparameter>, data can converted <epiparameter> objects require argument.","code":"marburg_incub_epiparameter$citation #> Gear (1975). \"<title not available>.\" _<journal not available>_. marburg_articles <- load_epidata_raw( pathogen = \"marburg\", table = \"article\" ) marburg_articles #> # A tibble: 58 × 25 #> article_id pathogen covidence_id first_author_first_n…¹ article_title doi #> <dbl> <chr> <int> <chr> <chr> <chr> #> 1 1 Marburg v… 2059 G A Haemorrhagic… NA #> 2 2 Marburg v… 2042 Christian Antibodies t… NA #> 3 3 Marburg v… 1649 Y The origin a… 10.1… #> 4 4 Marburg v… 1692 D.H. Marburg-Viru… NA #> 5 5 Marburg v… 2597 E. D. Filovirus ac… NA #> 6 6 Marburg v… 3795 JS Outbreak of … 10.1… #> 7 7 Marburg v… 2596 E.D. Haemorrhagic… NA #> 8 8 Marburg v… 1615 O Viral hemorr… 10.4… #> 9 9 Marburg v… 1693 Smiley Suspected Ex… 10.1… #> 10 10 Marburg v… 1692 D Marburg-viru… NA #> # ℹ 48 more rows #> # ℹ abbreviated name: ¹first_author_first_name #> # ℹ 19 more variables: journal <chr>, year_publication <int>, volume <int>, #> # issue <int>, page_first <int>, page_last <int>, paper_copy_only <lgl>, #> # notes <chr>, first_author_surname <chr>, double_extracted <dbl>, #> # qa_m1 <chr>, qa_m2 <chr>, qa_a3 <chr>, qa_a4 <chr>, qa_d5 <chr>, #> # qa_d6 <chr>, qa_d7 <chr>, score <dbl>, id <chr> article_row <- match(marburg_incub$id, marburg_articles$id) article_row #> [1] 6 marburg_incub_article <- marburg_articles[article_row, ] marburg_incub_article #> # A tibble: 1 × 25 #> article_id pathogen covidence_id first_author_first_n…¹ article_title doi #> <dbl> <chr> <int> <chr> <chr> <chr> #> 1 6 Marburg vi… 3795 JS Outbreak of … 10.1… #> # ℹ abbreviated name: ¹first_author_first_name #> # ℹ 19 more variables: journal <chr>, year_publication <int>, volume <int>, #> # issue <int>, page_first <int>, page_last <int>, paper_copy_only <lgl>, #> # notes <chr>, first_author_surname <chr>, double_extracted <dbl>, #> # qa_m1 <chr>, qa_m2 <chr>, qa_a3 <chr>, qa_a4 <chr>, qa_d5 <chr>, #> # qa_d6 <chr>, qa_d7 <chr>, score <dbl>, id <chr> marburg_incub_epiparameter <- as_epiparameter( marburg_incub, article = marburg_incub_article ) #> Using Gear (1975). \"Outbreak of Marburg virus disease in Johannesburg.\" _The #> British Medical Journal_. doi:10.1136/bmj.4.5995.489 #> <https://doi.org/10.1136/bmj.4.5995.489>. #> To retrieve the citation use the 'get_citation' function #> No adequate summary statistics available to calculate the parameters of the NA distribution #> Unparameterised <epiparameter> object marburg_incub_epiparameter #> Disease: Marburg Virus Disease #> Pathogen: Marburg virus #> Epi Parameter: human delay incubation period #> Study: Gear (1975). \"Outbreak of Marburg virus disease in Johannesburg.\" _The #> British Medical Journal_. doi:10.1136/bmj.4.5995.489 #> <https://doi.org/10.1136/bmj.4.5995.489>. #> Distribution: NA #> Mean: NA [NA% CI: NA, NA] (Days) #> Median: NA [NA% CI: NA, NA] (Days) #> Range: [c(7, 8)] (Days) marburg_incub_epiparameter$citation #> Gear (1975). \"Outbreak of Marburg virus disease in Johannesburg.\" _The #> British Medical Journal_. doi:10.1136/bmj.4.5995.489 #> <https://doi.org/10.1136/bmj.4.5995.489>."},{"path":"https://epiverse-trace.github.io/epiparameter/articles/data_from_epireview.html","id":"multi-row-epireview-entries","dir":"Articles","previous_headings":"","what":"Multi-row {epireview} entries","title":"Using {epireview} with {epiparameter}","text":"general, required values parameter represented single entry epireview. cases, e.g. Marburg Virus Disease Ebola Virus Disease (first pathogens PERG team extracted), values captured parameter multiple rows. trying avoid linking entries challenging, still cases linked parameters different rows. provide information limitations section . way {epireview} data stored means epidemiological parameter entries require multiple rows. can , example, contain two summary statistics (e.g. mean standard deviation) kept separate rows. order create <epiparameter> objects contains full information entry multiple rows epidemiological parameters table {epireview} can given as_epiparameter() create single <epiparameter> object. can search entries data multiple rows checking duplicated parameter types IDs. Remember possible convert delay distributions epiparameter objects (.e. known Human delay parameter types {epireview}). case two studies Marburg one entry (row) {epireview} database. studies select mean standard deviation. case, know mean standard deviation chosen rows correspond estimation process read corresponding article. However, currently identifiers {epireview} params database Marburg, Ebola Lassa directly identify two rows mean values correspond standard deviation. {epireview} team currently working rectifying issue. Therefore, encourage readers manually verify data subsets, ensure entries selected indeed multiple rows reported epidemiological parameter. future {epireview} pathogens (excluding SARS) mean standard deviation estimates match form one row $params database. Current software development {epireview} working ensuring compatibility formats. can now convert <epiparameter>.","code":"multi_row_entries <- duplicated(marburg_params$parameter_type) & duplicated(marburg_params$id) multi_row_ids <- marburg_params$id[multi_row_entries] multi_row_marburg_params <- marburg_params[marburg_params$id %in% multi_row_ids, ] multi_row_marburg_params #> # A tibble: 42 × 61 #> parameter_data_id article_id parameter_type parameter_value parameter_unit #> <chr> <int> <chr> <dbl> <chr> #> 1 0106582cf5ed3c52d5e… 20 Human delay -… NA Days #> 2 ce78f707a585d8bb23a… 22 Seroprevalenc… 0 Percentage (%) #> 3 ca720828fd6ccb18844… 22 Seroprevalenc… 0 NA #> 4 61fbb9dfc021abf5bd1… 22 Seroprevalenc… 0 Percentage (%) #> 5 29c8ca74306713a990c… 20 Severity - ca… NA NA #> 6 056a8d6b5f9aee3622d… 27 Human delay -… 9 Days #> 7 ce3976e2e15df3f6fb9… 27 Human delay -… 5.4 Days #> 8 3bf73665fa67a6ba7f7… 27 Human delay -… 7 Days #> 9 ba019f18acac9c5b0b7… 27 Human delay -… 9.3 Days #> 10 71798b4154011dcd008… 27 Human delay -… 9 Days #> # ℹ 32 more rows #> # ℹ 56 more variables: parameter_lower_bound <dbl>, #> # parameter_upper_bound <dbl>, parameter_value_type <chr>, #> # parameter_uncertainty_single_value <dbl>, #> # parameter_uncertainty_singe_type <chr>, #> # parameter_uncertainty_lower_value <dbl>, #> # parameter_uncertainty_upper_value <dbl>, … multi_row_marburg_params$parameter_value_type #> [1] NA NA NA #> [4] NA NA \"Mean\" #> [7] \"Standard Deviation\" \"Median\" \"Mean\" #> [10] \"Median\" \"Mean\" \"Mean\" #> [13] \"Mean\" \"Mean\" \"Mean\" #> [16] NA NA NA #> [19] NA \"Mean\" \"Mean\" #> [22] \"Mean\" \"Mean\" NA #> [25] NA \"Median\" NA #> [28] NA NA NA #> [31] \"Other\" NA NA #> [34] NA \"Other\" \"Other\" #> [37] \"Other\" \"Other\" \"Other\" #> [40] \"Other\" \"Mean\" \"Other\" marburg_gt <- multi_row_marburg_params[ multi_row_marburg_params$parameter_data_id %in% c(\"056a8d6b5f9aee3622d3bd8b715d4296\", \"ce3976e2e15df3f6fb92f6deb2db2a29\"), ] marburg_gt #> # A tibble: 2 × 61 #> parameter_data_id article_id parameter_type parameter_value parameter_unit #> <chr> <int> <chr> <dbl> <chr> #> 1 056a8d6b5f9aee3622d3… 27 Human delay -… 9 Days #> 2 ce3976e2e15df3f6fb92… 27 Human delay -… 5.4 Days #> # ℹ 56 more variables: parameter_lower_bound <dbl>, #> # parameter_upper_bound <dbl>, parameter_value_type <chr>, #> # parameter_uncertainty_single_value <dbl>, #> # parameter_uncertainty_singe_type <chr>, #> # parameter_uncertainty_lower_value <dbl>, #> # parameter_uncertainty_upper_value <dbl>, parameter_uncertainty_type <chr>, #> # cfr_ifr_numerator <int>, cfr_ifr_denominator <int>, … marburg_gt_epiparameter <- as_epiparameter(marburg_gt) #> Using Ajelli (2012). \"<title not available>.\" _<journal not available>_. #> To retrieve the citation use the 'get_citation' function #> Warning: Cannot create full citation for epidemiological parameters without bibliographic information #> see ?as_epiparameter for help. #> No adequate summary statistics available to calculate the parameters of the NA distribution #> Unparameterised <epiparameter> object marburg_gt_epiparameter #> Disease: Marburg Virus Disease #> Pathogen: Marburg virus #> Epi Parameter: human delay generation time #> Study: Ajelli (2012). \"<title not available>.\" _<journal not available>_. #> Distribution: NA #> Mean: 9 [95% CI: 8.2, 10] (Days) #> Median: NA [NA% CI: NA, NA] (Days) #> Range: [c(NA, NA)] (Days) marburg_gt_epiparameter$summary_stats #> $mean #> [1] 9 #> #> $mean_ci_limits #> [1] 8.2 10.0 #> #> $mean_ci #> [1] 95 #> #> $sd #> [1] 5.4 #> #> $sd_ci_limits #> [1] 3.9 8.6 #> #> $sd_ci #> [1] 95 #> #> $median #> [1] NA #> #> $median_ci_limits #> [1] NA NA #> #> $median_ci #> [1] NA #> #> $dispersion #> [1] NA #> #> $dispersion_ci_limits #> [1] NA NA #> #> $dispersion_ci #> [1] NA #> #> $quantiles #> [1] NA #> #> $range #> [1] NA NA"},{"path":"https://epiverse-trace.github.io/epiparameter/articles/data_from_epireview.html","id":"entries-with-probability-distributions","dir":"Articles","previous_headings":"","what":"Entries with probability distributions","title":"Using {epireview} with {epiparameter}","text":"example load Ebola epidemiological parameters {epireview} package (entries Marburg parametric distributions). subset data just use epidemiological parameter table, select rows containing serial interval. select entry estimated reported Weibull distribution: can now convert <epiparameter> object. probability distribution serial interval can utilise <epiparameter> methods. illustrate checking <epiparameter> parameterised, plotting PDF CDF, generating 10 random numbers sampling distribution.","code":"ebola_data <- load_epidata(\"ebola\") #> ℹ ebola does not have any extracted outbreaks #> information. Outbreaks will be set to NULL. #> Rows: 107 Columns: 2 #> ── Column specification ──────────────────────────────────────────────────────── #> Delimiter: \";\" #> chr (2): parameter_type_short, parameter_type_full #> #> ℹ Use `spec()` to retrieve the full column specification for this data. #> ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message. #> ✔ Data loaded for ebola ebola_params <- ebola_data$params ebola_si_rows <- ebola_params[ ebola_params$parameter_type_short == \"serial_interval\", ] ebola_si_rows #> # A tibble: 19 × 78 #> id parameter_data_id covidence_id pathogen parameter_type parameter_value #> <chr> <chr> <int> <chr> <chr> <dbl> #> 1 f49a9… 466f684ff8286fbd… 506 Ebola v… Human delay -… 12 #> 2 c1e68… cb37cc4599953d47… 1471 Ebola v… Human delay -… 19.4 #> 3 08e06… 20eb9e7d7714183c… 1876 Ebola v… Human delay -… 11 #> 4 5a250… 115c169147af31f7… 1891 Ebola v… Human delay -… 11.1 #> 5 54159… 6fca288e3bca7dc0… 3138 Ebola v… Human delay -… 16.1 #> 6 f044b… 89e334ec3622ed27… 3776 Ebola v… Human delay -… 14 #> 7 df908… e62da97ac8648211… 4966 Ebola v… Human delay -… 14.2 #> 8 df908… d46ff8b0c2ff67b7… 4966 Ebola v… Human delay -… 7.1 #> 9 1b9d9… abb8b6aabf43ac86… 5924 Ebola v… Human delay -… 13.7 #> 10 39354… 2b270d400af4fcce… 5939 Ebola v… Human delay -… NA #> 11 39354… 8a18cde4823cf9f7… 5939 Ebola v… Human delay -… NA #> 12 39354… 10f3384f1550a778… 5939 Ebola v… Human delay -… NA #> 13 50dea… 631ec65830a82fbe… 6346 Ebola v… Human delay -… 15.3 #> 14 86e39… 5c8d68c39d1c3b98… 15896 Ebola v… Human delay -… 15.3 #> 15 40a29… 7f4ab651c48511df… 17077 Ebola v… Human delay -… 15.3 #> 16 b76dc… 0c3e02f80addfccc… 17730 Ebola v… Human delay -… 12 #> 17 b76dc… c2e0739d6bc652e9… 17730 Ebola v… Human delay -… 11.7 #> 18 74b62… e2a59f5aa40ddbdf… 18536 Ebola v… Human delay -… 12.3 #> 19 66e1b… 4da557e3c2c22a10… 19083 Ebola v… Human delay -… NA #> # ℹ 72 more variables: exponent <dbl>, parameter_unit <chr>, #> # parameter_lower_bound <dbl>, parameter_upper_bound <dbl>, #> # parameter_value_type <chr>, parameter_uncertainty_single_value <dbl>, #> # parameter_uncertainty_singe_type <chr>, #> # parameter_uncertainty_lower_value <dbl>, #> # parameter_uncertainty_upper_value <dbl>, parameter_uncertainty_type <chr>, #> # cfr_ifr_numerator <int>, cfr_ifr_denominator <int>, … ebola_si <- ebola_si_rows[ ebola_si_rows$parameter_data_id == \"0c3e02f80addfccc1017fa619fba76c5\", ] ebola_si #> # A tibble: 1 × 78 #> id parameter_data_id covidence_id pathogen parameter_type parameter_value #> <chr> <chr> <int> <chr> <chr> <dbl> #> 1 b76dcc… 0c3e02f80addfccc… 17730 Ebola v… Human delay -… 12 #> # ℹ 72 more variables: exponent <dbl>, parameter_unit <chr>, #> # parameter_lower_bound <dbl>, parameter_upper_bound <dbl>, #> # parameter_value_type <chr>, parameter_uncertainty_single_value <dbl>, #> # parameter_uncertainty_singe_type <chr>, #> # parameter_uncertainty_lower_value <dbl>, #> # parameter_uncertainty_upper_value <dbl>, parameter_uncertainty_type <chr>, #> # cfr_ifr_numerator <int>, cfr_ifr_denominator <int>, … ebola_si_epiparameter <- as_epiparameter(ebola_si) #> Using Marziano (2023). \"<title not available>.\" _<journal not available>_. #> To retrieve the citation use the 'get_citation' function #> Warning: Cannot create full citation for epidemiological parameters without bibliographic information #> see ?as_epiparameter for help. ebola_si_epiparameter #> Disease: Ebola Virus Disease #> Pathogen: Ebola virus #> Epi Parameter: human delay serial interval #> Study: Marziano (2023). \"<title not available>.\" _<journal not available>_. #> Distribution: weibull (Days) #> Parameters: #> shape: 1.760 #> scale: 10.140 is_parameterised(ebola_si_epiparameter) #> [1] TRUE plot(ebola_si_epiparameter) generate(ebola_si_epiparameter, times = 10) #> [1] 8.1913357 17.4324678 1.4003823 6.3030243 0.9521153 7.7520633 #> [7] 4.8497066 7.3824799 4.6173973 6.4234107"},{"path":"https://epiverse-trace.github.io/epiparameter/articles/data_from_epireview.html","id":"specifying-the-probability-distribution-if-unknown","dir":"Articles","previous_headings":"","what":"Specifying the probability distribution if unknown","title":"Using {epireview} with {epiparameter}","text":"may instances delay distribution reported literature, either probability distribution fit data, reported probability distribution parameters correspond . Therefore, probability distribution specified {epireview} data. cases, parametric probability distribution required particular epidemiological task assuming probability distribution can useful. Please use feature caution. Assuming incorrect probability distribution applying epidemiological method can lead erroneous results. Additionally, probability distribution specified user overwrite probability distribution specified input data (e.g. {epireview} parameter data) can lead error distribution name supplied parameters input incompatible See ?as_epiparameter details information. Just example load Ebola parameters using epireview::load_epidata() function subset just parameters ($params). use serial interval Ebola reported Faye et al. (2015). stored, two rows {epireview} parameter table, mean standard deviation, probability distribution specified. code chunk subsets Ebola parameter table just return serial interval Faye et al. (2015). supply data as_epiparameter() get unparameterised <epiparameter> object probability distribution stated. Given can convert mean standard deviation parameters probability distribution assume distribution form, can supply data as_epiparameter(). uses parameter conversion functions {epiparameter} (see vignette(\"extract_convert\", package = \"epiparameter\")). Ebola serial interval <epiparameter> can now used various probability distribution methods.","code":"ebola_data <- load_epidata(\"ebola\") #> ℹ ebola does not have any extracted outbreaks #> information. Outbreaks will be set to NULL. #> Rows: 107 Columns: 2 #> ── Column specification ──────────────────────────────────────────────────────── #> Delimiter: \";\" #> chr (2): parameter_type_short, parameter_type_full #> #> ℹ Use `spec()` to retrieve the full column specification for this data. #> ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message. #> ✔ Data loaded for ebola ebola_params <- ebola_data$params ebola_si <- ebola_params[ which( grepl(pattern = \"Faye\", x = ebola_params$article_label, fixed = TRUE) & grepl(pattern = \"serial\", ebola_params$parameter_type, fixed = TRUE) ), ] ebola_si_epiparameter <- as_epiparameter(ebola_si) #> Using Faye (2015). \"<title not available>.\" _<journal not available>_. #> To retrieve the citation use the 'get_citation' function #> Warning: Cannot create full citation for epidemiological parameters without bibliographic information #> see ?as_epiparameter for help. #> No adequate summary statistics available to calculate the parameters of the NA distribution #> Unparameterised <epiparameter> object ebola_si_epiparameter #> Disease: Ebola Virus Disease #> Pathogen: Ebola virus #> Epi Parameter: human delay serial interval #> Study: Faye (2015). \"<title not available>.\" _<journal not available>_. #> Distribution: NA #> Mean: 14.2 [95% CI: 13.1, 15.5] (Days) #> Median: NA [NA% CI: NA, NA] (Days) #> Range: [c(NA, NA)] (Days) is_parameterised(ebola_si_epiparameter) #> [1] FALSE ebola_si_epiparameter <- as_epiparameter(ebola_si, prob_distribution = \"gamma\") #> Using Faye (2015). \"<title not available>.\" _<journal not available>_. #> To retrieve the citation use the 'get_citation' function #> Warning: Cannot create full citation for epidemiological parameters without bibliographic information #> see ?as_epiparameter for help. #> Parameterising the probability distribution with the summary statistics. #> Probability distribution is assumed not to be discretised or truncated. ebola_si_epiparameter #> Disease: Ebola Virus Disease #> Pathogen: Ebola virus #> Epi Parameter: human delay serial interval #> Study: Faye (2015). \"<title not available>.\" _<journal not available>_. #> Distribution: gamma (Days) #> Parameters: #> shape: 4.000 #> scale: 3.550 is_parameterised(ebola_si_epiparameter) #> [1] TRUE get_parameters(ebola_si_epiparameter) #> shape scale #> 4.00 3.55 density(ebola_si_epiparameter, at = 20) #> [1] 0.03001206 plot(ebola_si_epiparameter) cdf(ebola_si_epiparameter, q = 10) #> [1] 0.3118251 plot(ebola_si_epiparameter, cumulative = TRUE) quantile(ebola_si_epiparameter, p = 0.5) #> [1] 13.03582 generate(ebola_si_epiparameter, times = 10) #> [1] 10.462339 11.714033 16.448346 14.659046 14.979571 21.061653 6.958792 #> [8] 20.288048 15.635075 1.645704"},{"path":"https://epiverse-trace.github.io/epiparameter/articles/data_from_epireview.html","id":"limitations","dir":"Articles","previous_headings":"","what":"Limitations","title":"Using {epireview} with {epiparameter}","text":"database schema {epireview} evolved time Imperial PERG team extracted pathogens. list parameter types available {epireview} package important differentiate variability sample (e.g. sample standard deviation) uncertainty estimate (e.g. 95% confidence interval credible interval). database version {epireview} Zika, PERG team explicitly expose remove ambiguity extracted data. Please note Marburg, Lassa, Ebola datasets, may ambiguity variability uncertainty. functionality {epiparameter} {epireview} developed improved coming months.","code":""},{"path":[]},{"path":"https://epiverse-trace.github.io/epiparameter/articles/data_protocol.html","id":"about-the-package","dir":"Articles","previous_headings":"","what":"About the package","title":"Data Collation and Synthesis Protocol","text":"{epiparameter} R package contains library epidemiological parameter data functions read handle data. delay distributions describe time two events epidemiology, example incubation period, serial interval onset--death; offspring distributions describe number secondary infections primary infection disease transmission. library compiled process collecting, reviewing extracting data peer-reviewed literature1, including research articles, systematic reviews meta-analyses. epiparameter package act ‘living systematic review’ (sensu Elliott et al. (2014)) actively updated maintained provide reliable source data epidemiological distributions. prevent bias collection assessment data, well-defined methodology searching refining required. document aims provide transparency methodology used epiparameter maintainers outlining steps taken stage data handling. can also serve guide contributors wanting search provide epidemiological parameters currently missing library. protocol also facilitate reproducibility searches, results appraisal steps. large body work methods best conduct literature searches data collection part systematic reviews meta-analyses2, use basis protocol. sources : Cochrane Handbook (Higgins et al. 2022) PRISMA (Page et al. 2021)","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/articles/data_protocol.html","id":"objective-of-epiparameter","dir":"Articles","previous_headings":"","what":"Objective of {epiparameter}","title":"Data Collation and Synthesis Protocol","text":"defined PRISMA guidelines, clearly stated objective helps refine goal project. epiparameter’s objective provide information collection distributions range infectious diseases accurate, unbiased comprehensive possible. distributions enable outbreak analysts easily access distributions routine analysis. example, delay distributions necessary : calculating case fatality rates adjusting delay outcome, quantifying implications different screening measures quarantine periods, estimating reproduction numbers, scenario modelling using transmission dynamic models.","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/articles/data_protocol.html","id":"contributing-to-the-package","dir":"Articles","previous_headings":"","what":"Contributing to the package","title":"Data Collation and Synthesis Protocol","text":"contribute epiparameter library epidemiological parameter information, added data google sheet. integrated epiparameter library package maintainers, information accessible epiparameter package users.","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/articles/data_protocol.html","id":"scope-of-package","dir":"Articles","previous_headings":"","what":"Scope of package","title":"Data Collation and Synthesis Protocol","text":"epiparameter package spans range infectious diseases, including several distributions disease available. pathogens diseases currently systematically searched included package library : distributions currently included literature search pathogen/disease :","code":"#> Returning 125 results that match the criteria (100 are parameterised). #> Use subset to filter by entry variables or single_epiparameter to return a single entry. #> To retrieve the citation for each use the 'get_citation' function #> Returning 125 results that match the criteria (100 are parameterised). #> Use subset to filter by entry variables or single_epiparameter to return a single entry. #> To retrieve the citation for each use the 'get_citation' function"},{"path":"https://epiverse-trace.github.io/epiparameter/articles/data_protocol.html","id":"guide-to-identifying-distributions-in-the-literature","dir":"Articles","previous_headings":"","what":"Guide to identifying distributions in the literature","title":"Data Collation and Synthesis Protocol","text":"Key word searches: searching literature, use specific search phrases ensure correct literature procured required. use search schema includes searching pathogen disease, desired distribution. search phrase can optionally include specific variant/strain/subtype. search constrained based year publication. Examples searches: “SARS-CoV-2 incubation period” “ebola serial interval” “influenza H7N9 onset admission” However, simple search phrases can return large number irrelevant papers. Using specific search schema depending search engine used. example, using Google Scholar schema like: (“Middle East Respiratory Syndrome” MERS) “onset death” (estimation inference calculation) (ebola EVD) “onset death” (estimation inference calculation) Web Science used: (“Middle East Respiratory Syndrome” MERS) “onset death” estimat* (ebola EVD) “onset death” estimat* refine results suitable set literature. Literature search engines: using selection search engines prevent one source potentially omitting papers. Suggested search sites : Google Scholar, Web Science, PubMed, Scopus. Adding papers: addition database entries papers identified literature search, entries can supplemented recommendations (.e. community) cited paper literature search. Papers may recommended experts research public health communities. plan use two methods community engagement. Firstly open-access Google sheet allows people add distribution data reviewed one epiparameter maintainers incorporated meets quality checks. second method - yet implemented - involves community members uploading data zenodo, can read loaded R using epiparameter checked. Language restrictions: papers English Spanish currently supported epiparameter. Papers written another language verified expert can also included database. However, evaluated review process described result flagged user loaded epiparameter.","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/articles/data_protocol.html","id":"guide-to-data-refinement-once-sources-identified","dir":"Articles","previous_headings":"","what":"Guide to data refinement once sources identified","title":"Data Collation and Synthesis Protocol","text":"Removing duplicates: library parameters contain duplicates studies, multiple entries per study can included paper reports multiple results (e.g. full data set subset data). Studies use data, subsets supersets data used papers library included. Abstract methods screening: number unique sources identified, reviewed suitability reviewing abstract searching words phrases paper indicate reports parameters summary statistics distribution, can include searching methods section words types distributions (e.g. lognormal), fitting procedures (e.g. maximum likelihood bayesian), searching results parameter estimates. epiparameter library includes entries parameters summary statistics reported distribution specified, entries distribution specified parameters reported. database unsuitable papers kept remind maintainers papers included aids updating database (see ) preventing redundant reviewing previously rejected paper. Stopping criteria: many searches, number results far larger reasonably evaluated outside full systematic review. refining papers contain required information (abstract methods screening), around 10 papers per pathogen screened search (per search round, see updating section details). number papers pass abstract methods screening fewer 10, suitable papers reviewed. Full paper screening: abstract methods screening, papers excluded reviewed full verify indeed contain required information distribution parameters information methodology used. acceptable include secondary source contains information delay distribution primary source unavailable report distribution. inference delay distribution primary subject research article, example inferred used estimation R0R_0 can still included database. Additionally, distribution parameters based illustrative values use simulations - rather inferred data - considered unsuitable excluded. , papers excluded stage recorded database unsuitable sources reasoning prevent reassess updating database. Post hoc removal: epiparameter parameters later identified inappropriate can removed database. cases unlikely limitations can appended onto data entries make users aware limitations (e.g. around assumptions used infer distribution), extreme cases data completely removed database. Note: systematic reviews focusing effect sizes can subject publication bias (e.g. positive significant results literature). However, distribution inference focus significance testing effect sizes, bias considered collection process.","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/articles/data_protocol.html","id":"guide-to-extracting-parameters","dir":"Articles","previous_headings":"","what":"Guide to extracting parameters","title":"Data Collation and Synthesis Protocol","text":"Extracting parameters: underlying distributions (e.g. gamma, lognormal), parameters (e.g. shape/scale, meanlog/sdlog), summary statistics (e.g. mean, standard deviation, median, range quantiles) given paper, values recorded verbatim paper database. read R using epiparameter package, aspects distribution automatically calculated available. example mean standard deviation gamma distribution reported serial interval values stored database. R, shape scale parameters gamma distribution automatically reconstructed resulting distribution available use. epiparameter library exactly reflects literature. mean information present paper imputed prior knowledge (e.g. vector disease known stated), performing calculating reported values. prevents issue clear provenance data library. requirements entry database defined data dictionary. outline minimal dataset required included epiparameter library : Name disease Type distribution Citation information (author(s) paper, year publication, publication title journal, DOI) Whether distribution extrinsic (e.g. extrinsic incubation period). disease vector-borne NA. Whether distribution fitted discretised, boolean (true false). information database entry non-essential. See data dictionary included epiparameter database fields description range possible values field can take.","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/articles/data_protocol.html","id":"data-quality-assessment-in-epiparameter","dir":"Articles","previous_headings":"","what":"Data quality assessment in {epiparameter}","title":"Data Collation and Synthesis Protocol","text":"inference parameters delay distribution often requires methodological adjustments correct factors otherwise bias estimates. includes accounting interval-censoring data timing event (e.g. exposure pathogen) know certainty, rather within time window. adjusting phase bias distribution estimated growing shrinking stage epidemic. aim epiparameter make judgement parameters ‘better’ others, notify warn user potential limitations data. aspects assessed : 1) whether method includes single double interval-censoring exposure onset times known certainty (.e. single day); 2) method adjust phase bias outbreak ascending descending phase. indicated boolean values indicate whether reported paper users recommended refer back paper determine whether estimates biased.","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/articles/data_protocol.html","id":"guide-to-the-epiparameter-review-process","dir":"Articles","previous_headings":"","what":"Guide to the {epiparameter} review process","title":"Data Collation and Synthesis Protocol","text":"set parameters included database must pass abstract methods screening full screening subsequently review one epiparameter maintainers. process involves running diagnostic checks cross-referencing reported parameters paper ensure match exactly results plot PDF/CDF/PMF matches anything plotted paper, available. prevents possible misinterpretation (e.g. serial interval incubation period). check also includes making sure unique identifiers paper match author’s name, publication year data recorded database.","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/articles/data_protocol.html","id":"updating-parameters-in-the-database","dir":"Articles","previous_headings":"","what":"Updating parameters in the database","title":"Data Collation and Synthesis Protocol","text":"search review stages time consuming continuously carried , aim keep epiparameter library --date living data library conducting regular searches (.e. every 3-4 months) fill missing papers new publication since last search. epidemiological literature can expand rapidly, especially new outbreak. Therefore can optionally include new studies use epidemiological community regular updates. small additions still subject data quality assessment diagnostics ensure accuracy, likely picked subsequent literature searches. likely existing pathogens major increase incidence since last update new papers reporting delay distributions. cases papers previously reviewed due limited reviewing time round updates now checked. particularly value community contributions database, everyone can benefit analysis already conducted, duplicated effort reduced.","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/articles/data_protocol.html","id":"database-of-excluded-papers","dir":"Articles","previous_headings":"","what":"Database of excluded papers","title":"Data Collation and Synthesis Protocol","text":"papers returned search results suitable, either stage abstract screening, reviewing entirety paper, recorded database following information: First author’s last name Unique identifier, ideally DOI Journal, pre-print server, host website One several reasons deemed unsuitable Date recording","code":""},{"path":[]},{"path":"https://epiverse-trace.github.io/epiparameter/articles/design_principles.html","id":"scope","dir":"Articles","previous_headings":"","what":"Scope","title":"Design Principles for {epiparameter}","text":"{epiparameter} R package library epidemiological parameters, provides class (.e. data structure) helper functions working epidemiological parameters distributions. <epiparameter> class main functional object working epidemiological parameters can hold information delay distributions (e.g. incubation period, serial interval, onset--death distribution) offspring distributions. class number methods, including allowing user easily calculate PDF, CDF, quantile, generate random numbers, calculate distribution mean, plot distribution. <epiparameter> object can created constructor function epiparameter(), uncertain whether object <epiparameter>, can validated assert_epiparameter(). package also converts distribution parameters summary statistics, vice versa. achieved either conversion extraction methods functions used explained Parameter extraction conversion {epiparameter} vignette.","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/articles/design_principles.html","id":"output","dir":"Articles","previous_headings":"","what":"Output","title":"Design Principles for {epiparameter}","text":"output epiparameter() constructor function <epiparameter> object. list nine elements, element either single type (e.g. character), non-nested list another class. Classes <epiparameter> elements used existing well developed infrastructure handling certain data types. $prod_dist element uses distribution class – parameterised distribution available – using either <distribution> class {distributional} <distcrete> class {distcrete}. $citation handled using <bibentry> class {utils} package (included part base R recommended packages). functions return simplest type possible, may atomic vector (including single element vectors), un-nested lists.","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/articles/design_principles.html","id":"package-architecture","dir":"Articles","previous_headings":"","what":"Package architecture","title":"Design Principles for {epiparameter}","text":"Much {epiparameter} package centred around <epiparameter> class. diagram showing class ’s S3 methods (diagram interactive can adjusted labels overlapping).","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/articles/design_principles.html","id":"design-decisions","dir":"Articles","previous_headings":"","what":"Design decisions","title":"Design Principles for {epiparameter}","text":"<epiparameter> class designed core unit working epidemiological parameters. designed parallel epidemiological data structures <contactmatrix> class {contactmatrix} R package. design principles <epiparameter> class aligned <contactmatrix> design principles. include: new_*<class>() constructor assert_<class>() test_<class>() is_<class>() checker determine object given class (without checking validity class) Coercion generic as_<class>(). conversion functions (convert_*) S3 generic functions methods provided {epiparameter} character <epiparameter> input. follows design pattern packages, {dplyr}, export key data transformation functions S3 generics allow developers extend conversions data objects. conversion functions designed single function exported user summary statistics parameters, another function exported parameters summary statistics. functions use switch() dispatch internal conversion functions. provides minimal number conversion functions package namespace compared exporting conversion function every distribution. large number entries returned reading epidemiological parameters library using epiparameter_db() function, can flood console, due default list printing R. reasoning <multi_epiparameter> object minimal class enable cleaner descriptive printing large list <epiparameter> objects. print.multi_epiparameter() prints header metadata number <epiparameter> objects number diseases epidemiological distributions list. also lists diseases epidemiological parameters returned. footer print() function states number <epiparameter> objects shown, guides use print(n = ...) parameter_tbl() link online database vignette (database.Rmd). Information header footer considered metadata advice prefixed #. package uses S3 classes S3 dispatch exported functions, switch() .call() dispatching internal functions. easier develop debug internal functions use S3 dispatch avoids ensure S3 methods registered. Examples S3 dispatch exported functions get_parameters() convert_summary_stats_to_params(). Examples internal dispatch using switch() .call() clean_params() convert_params_to_summary_stats.character(). function naming convention internal functions dot (.) prefix (e.g. .convert_params_lnorm()). function breaks convention new_epiparameter() advanced users package may want call internal low-level constructor, adding dot prefix function may make harder users find.","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/articles/design_principles.html","id":"dependencies","dir":"Articles","previous_headings":"","what":"Dependencies","title":"Design Principles for {epiparameter}","text":"aim restrict number dependencies minimal required set ease maintenance. current hard dependencies : {checkmate} {distributional} {distcrete} {stats} {utils} {stats} {utils} distributed R language viewed lightweight dependencies, already installed user’s machine R. {checkmate} input checking package widely used across Epiverse-TRACE packages. {distributional} {distcrete} used import S3 classes handling working distributions. required {distcrete} can handle discretised distributions. Currently {epiparameter} deviates Epiverse policy number previous R versions supports. {epiparameter} package requires R version >= 4.1.0 includes current version last three minor R versions rather policy four minor versions, September 2024. reasons change enable usage base R pipe (|>).","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/articles/design_principles.html","id":"contribute","dir":"Articles","previous_headings":"","what":"Contribute","title":"Design Principles for {epiparameter}","text":"addition package contributing guide, please refer {epiparameter} specific contributing guidelines adding epidemiological parameter package library.","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/articles/epiparameter.html","id":"use-case","dir":"Articles","previous_headings":"","what":"Use case","title":"Getting Started with {epiparameter}","text":"outbreak known potentially novel pathogen detected key parameters delay distributions (e.g. incubation period serial interval) required interpret early data. {epiparameter} can provide distributions selection published sources, past analysis similar pathogen, order provide relevant epidemiological parameters new analysis.","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/articles/epiparameter.html","id":"library-of-epidemiological-parameters","dir":"Articles","previous_headings":"","what":"Library of epidemiological parameters","title":"Getting Started with {epiparameter}","text":"First, introduce library, database, epidemiological parameters available {epiparameter}. library stored internally can read R using epiparameter_db() function. default entries library returned. output list <epiparameter> objects, element list corresponds entry parameter database. see full list diseases distributions stored library use parameter_tbl() function. show first six rows output. parameter_tbl() can also subset database supplied function. details data collation library parameters can found Data Collation Synthesis Protocol vignette.","code":"db <- epiparameter_db() #> Returning 125 results that match the criteria (100 are parameterised). #> Use subset to filter by entry variables or single_epiparameter to return a single entry. #> To retrieve the citation for each use the 'get_citation' function db #> # List of 125 <epiparameter> objects #> Number of diseases: 23 #> ❯ Adenovirus ❯ Chikungunya ❯ COVID-19 ❯ Dengue ❯ Ebola Virus Disease ❯ Hantavirus Pulmonary Syndrome ❯ Human Coronavirus ❯ Influenza ❯ Japanese Encephalitis ❯ Marburg Virus Disease ❯ Measles ❯ MERS ❯ Mpox ❯ Parainfluenza ❯ Pneumonic Plague ❯ Rhinovirus ❯ Rift Valley Fever ❯ RSV ❯ SARS ❯ Smallpox ❯ West Nile Fever ❯ Yellow Fever ❯ Zika Virus Disease #> Number of epi parameters: 13 #> ❯ case fatality risk ❯ generation time ❯ hospitalisation to death ❯ hospitalisation to discharge ❯ incubation period ❯ notification to death ❯ notification to discharge ❯ offspring distribution ❯ onset to death ❯ onset to discharge ❯ onset to hospitalisation ❯ onset to ventilation ❯ serial interval #> [[1]] #> Disease: Adenovirus #> Pathogen: Adenovirus #> Epi Parameter: incubation period #> Study: Lessler J, Reich N, Brookmeyer R, Perl T, Nelson K, Cummings D (2009). #> \"Incubation periods of acute respiratory viral infections: a systematic #> review.\" _The Lancet Infectious Diseases_. #> doi:10.1016/S1473-3099(09)70069-6 #> <https://doi.org/10.1016/S1473-3099%2809%2970069-6>. #> Distribution: lnorm (days) #> Parameters: #> meanlog: 1.723 #> sdlog: 0.231 #> #> [[2]] #> Disease: Human Coronavirus #> Pathogen: Human_Cov #> Epi Parameter: incubation period #> Study: Lessler J, Reich N, Brookmeyer R, Perl T, Nelson K, Cummings D (2009). #> \"Incubation periods of acute respiratory viral infections: a systematic #> review.\" _The Lancet Infectious Diseases_. #> doi:10.1016/S1473-3099(09)70069-7 #> <https://doi.org/10.1016/S1473-3099%2809%2970069-7>. #> Distribution: lnorm (days) #> Parameters: #> meanlog: 1.163 #> sdlog: 0.140 #> #> [[3]] #> Disease: SARS #> Pathogen: SARS-Cov-1 #> Epi Parameter: incubation period #> Study: Lessler J, Reich N, Brookmeyer R, Perl T, Nelson K, Cummings D (2009). #> \"Incubation periods of acute respiratory viral infections: a systematic #> review.\" _The Lancet Infectious Diseases_. #> doi:10.1016/S1473-3099(09)70069-8 #> <https://doi.org/10.1016/S1473-3099%2809%2970069-8>. #> Distribution: lnorm (days) #> Parameters: #> meanlog: 1.386 #> sdlog: 0.593 #> #> # ℹ 122 more elements #> # ℹ Use `print(n = ...)` to see more elements. #> # ℹ Use `parameter_tbl()` to see a summary table of the parameters. #> # ℹ Explore database online at: https://epiverse-trace.github.io/epiparameter/articles/database.html parameter_tbl(multi_epiparameter = db) #> # Parameter table: #> # A data frame: 125 × 7 #> disease pathogen epi_name prob_distribution author year sample_size #> <chr> <chr> <chr> <chr> <chr> <dbl> <dbl> #> 1 Adenovirus Adenovi… incubat… lnorm Lessl… 2009 14 #> 2 Human Coronavir… Human_C… incubat… lnorm Lessl… 2009 13 #> 3 SARS SARS-Co… incubat… lnorm Lessl… 2009 157 #> 4 Influenza Influen… incubat… lnorm Lessl… 2009 151 #> 5 Influenza Influen… incubat… lnorm Lessl… 2009 90 #> 6 Influenza Influen… incubat… lnorm Lessl… 2009 78 #> 7 Measles Measles… incubat… lnorm Lessl… 2009 55 #> 8 Parainfluenza Parainf… incubat… lnorm Lessl… 2009 11 #> 9 RSV RSV incubat… lnorm Lessl… 2009 24 #> 10 Rhinovirus Rhinovi… incubat… lnorm Lessl… 2009 28 #> # ℹ 115 more rows parameter_tbl(multi_epiparameter = db, disease = \"Ebola\") #> # Parameter table: #> # A data frame: 17 × 7 #> disease pathogen epi_name prob_distribution author year sample_size #> <chr> <chr> <chr> <chr> <chr> <dbl> <dbl> #> 1 Ebola Virus Dis… Ebola V… offspri… nbinom Lloyd… 2005 13 #> 2 Ebola Virus Dis… Ebola V… incubat… lnorm Eichn… 2011 196 #> 3 Ebola Virus Dis… Ebola V… onset t… gamma The E… 2018 14 #> 4 Ebola Virus Dis… Ebola V… incubat… gamma WHO E… 2015 1798 #> 5 Ebola Virus Dis… Ebola V… incubat… gamma WHO E… 2015 49 #> 6 Ebola Virus Dis… Ebola V… incubat… gamma WHO E… 2015 957 #> 7 Ebola Virus Dis… Ebola V… incubat… gamma WHO E… 2015 792 #> 8 Ebola Virus Dis… Ebola V… serial … gamma WHO E… 2015 305 #> 9 Ebola Virus Dis… Ebola V… serial … gamma WHO E… 2015 37 #> 10 Ebola Virus Dis… Ebola V… serial … gamma WHO E… 2015 147 #> 11 Ebola Virus Dis… Ebola V… serial … gamma WHO E… 2015 112 #> 12 Ebola Virus Dis… Ebola V… hospita… gamma WHO E… 2015 1167 #> 13 Ebola Virus Dis… Ebola V… hospita… gamma WHO E… 2015 1004 #> 14 Ebola Virus Dis… Ebola V… notific… gamma WHO E… 2015 2536 #> 15 Ebola Virus Dis… Ebola V… notific… gamma WHO E… 2015 1324 #> 16 Ebola Virus Dis… Ebola V… onset t… gamma WHO E… 2015 2741 #> 17 Ebola Virus Dis… Ebola V… onset t… gamma WHO E… 2015 1335"},{"path":"https://epiverse-trace.github.io/epiparameter/articles/epiparameter.html","id":"single-set-of-epidemiological-parameters","dir":"Articles","previous_headings":"","what":"Single set of epidemiological parameters","title":"Getting Started with {epiparameter}","text":"{epiparameter} introduces new class working epidemiological parameters R: <epiparameter>, contains name disease, name epidemiological distribution, parameters (available) citation information parameter source, well information. core data structure {epiparameter} package holds single set epidemiological parameters. <epiparameter> object can : Pulled database (epiparameter_db()) Created manually (using class constructor function: epiparameter()) arguments specified example using class constructor (epiparameter()) , example metadata parameter uncertainty (uncertainty) provided. See help documentation epiparameter() function using ?epiparameter see argument. Also see documentation <epiparameter> helper functions, e.g., ?create_citation(). Manually creating <epiparameter> objects can especially useful new parameter estimates become available yet incorporated {epiparameter} library. seen examples vignette, <epiparameter> class custom printing method shows disease, pathogen (known), epidemiological distribution, citation study parameters probability distribution parameter distribution (available).","code":"# <epiparameter> from database # fetch <epiparameter> for COVID-19 incubation period from database # return only a single <epiparameter> covid_incubation <- epiparameter_db( disease = \"COVID-19\", epi_name = \"incubation period\", single_epiparameter = TRUE ) #> Using Linton N, Kobayashi T, Yang Y, Hayashi K, Akhmetzhanov A, Jung S, Yuan #> B, Kinoshita R, Nishiura H (2020). \"Incubation Period and Other #> Epidemiological Characteristics of 2019 Novel Coronavirus Infections #> with Right Truncation: A Statistical Analysis of Publicly Available #> Case Data.\" _Journal of Clinical Medicine_. doi:10.3390/jcm9020538 #> <https://doi.org/10.3390/jcm9020538>.. #> To retrieve the citation use the 'get_citation' function covid_incubation #> Disease: COVID-19 #> Pathogen: SARS-CoV-2 #> Epi Parameter: incubation period #> Study: Linton N, Kobayashi T, Yang Y, Hayashi K, Akhmetzhanov A, Jung S, Yuan #> B, Kinoshita R, Nishiura H (2020). \"Incubation Period and Other #> Epidemiological Characteristics of 2019 Novel Coronavirus Infections #> with Right Truncation: A Statistical Analysis of Publicly Available #> Case Data.\" _Journal of Clinical Medicine_. doi:10.3390/jcm9020538 #> <https://doi.org/10.3390/jcm9020538>. #> Distribution: lnorm (days) #> Parameters: #> meanlog: 1.525 #> sdlog: 0.629 # <epiparameter> using constructor function covid_incubation <- epiparameter( disease = \"COVID-19\", pathogen = \"SARS-CoV-2\", epi_name = \"incubation period\", prob_distribution = create_prob_distribution( prob_distribution = \"gamma\", prob_distribution_params = c(shape = 2, scale = 1) ), summary_stats = create_summary_stats(mean = 2), citation = create_citation( author = person( given = list(\"John\", \"Amy\"), family = list(\"Smith\", \"Jones\") ), year = 2022, title = \"COVID Incubation Period\", journal = \"Epi Journal\", doi = \"10.27861182.x\" ) ) #> Using Smith J, Jones A (2022). \"COVID Incubation Period.\" _Epi Journal_. #> doi:10.27861182.x <https://doi.org/10.27861182.x>. #> To retrieve the citation use the 'get_citation' function covid_incubation #> Disease: COVID-19 #> Pathogen: SARS-CoV-2 #> Epi Parameter: incubation period #> Study: Smith J, Jones A (2022). \"COVID Incubation Period.\" _Epi Journal_. #> doi:10.27861182.x <https://doi.org/10.27861182.x>. #> Distribution: gamma (NA) #> Parameters: #> shape: 2.000 #> scale: 1.000"},{"path":"https://epiverse-trace.github.io/epiparameter/articles/epiparameter.html","id":"benefit-of-epiparameter","dir":"Articles","previous_headings":"","what":"Benefit of <epiparameter>","title":"Getting Started with {epiparameter}","text":"providing consistent robust object store epidemiological parameters, <epiparameter> objects can applied epidemiological pipelines, example {episoap}. data contained within object (e.g. parameter values, pathogen type, etc.) can modified pipeline continue operate class unchanged.","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/articles/epiparameter.html","id":"subsetting-database","dir":"Articles","previous_headings":"","what":"Subsetting database","title":"Getting Started with {epiparameter}","text":"database can subset directly epiparameter_db(). results can subset author. recommended use family name first author instead full name. first author matched entry source multiple authors. results can subset using subset argument, example subset = sample_size > 100 return entries sample size greater 100. See ?epiparameter_db() details use argument subset database entries get returned. single <epiparameter> required single_epiparameter argument can set TRUE return single set epidemiological parameters (.e. one delay distribution), available. multiple entries parameter library match search criteria (e.g. disease type) entries parameterised (.e. distribution parameters known), account right truncation inferred, estimated largest sample size preferentially selected (order).","code":"epiparameter_db( disease = \"COVID-19\", epi_name = \"incubation period\", author = \"Linton\" ) #> Returning 3 results that match the criteria (3 are parameterised). #> Use subset to filter by entry variables or single_epiparameter to return a single entry. #> To retrieve the citation for each use the 'get_citation' function #> # List of 3 <epiparameter> objects #> Number of diseases: 1 #> ❯ COVID-19 #> Number of epi parameters: 1 #> ❯ incubation period #> [[1]] #> Disease: COVID-19 #> Pathogen: SARS-CoV-2 #> Epi Parameter: incubation period #> Study: Linton N, Kobayashi T, Yang Y, Hayashi K, Akhmetzhanov A, Jung S, Yuan #> B, Kinoshita R, Nishiura H (2020). \"Incubation Period and Other #> Epidemiological Characteristics of 2019 Novel Coronavirus Infections #> with Right Truncation: A Statistical Analysis of Publicly Available #> Case Data.\" _Journal of Clinical Medicine_. doi:10.3390/jcm9020538 #> <https://doi.org/10.3390/jcm9020538>. #> Distribution: lnorm (days) #> Parameters: #> meanlog: 1.456 #> sdlog: 0.555 #> #> [[2]] #> Disease: COVID-19 #> Pathogen: SARS-CoV-2 #> Epi Parameter: incubation period #> Study: Linton N, Kobayashi T, Yang Y, Hayashi K, Akhmetzhanov A, Jung S, Yuan #> B, Kinoshita R, Nishiura H (2020). \"Incubation Period and Other #> Epidemiological Characteristics of 2019 Novel Coronavirus Infections #> with Right Truncation: A Statistical Analysis of Publicly Available #> Case Data.\" _Journal of Clinical Medicine_. doi:10.3390/jcm9020538 #> <https://doi.org/10.3390/jcm9020538>. #> Distribution: lnorm (days) #> Parameters: #> meanlog: 1.611 #> sdlog: 0.472 #> #> [[3]] #> Disease: COVID-19 #> Pathogen: SARS-CoV-2 #> Epi Parameter: incubation period #> Study: Linton N, Kobayashi T, Yang Y, Hayashi K, Akhmetzhanov A, Jung S, Yuan #> B, Kinoshita R, Nishiura H (2020). \"Incubation Period and Other #> Epidemiological Characteristics of 2019 Novel Coronavirus Infections #> with Right Truncation: A Statistical Analysis of Publicly Available #> Case Data.\" _Journal of Clinical Medicine_. doi:10.3390/jcm9020538 #> <https://doi.org/10.3390/jcm9020538>. #> Distribution: lnorm (days) #> Parameters: #> meanlog: 1.525 #> sdlog: 0.629 #> #> # ℹ Use `parameter_tbl()` to see a summary table of the parameters. #> # ℹ Explore database online at: https://epiverse-trace.github.io/epiparameter/articles/database.html epiparameter_db(disease = \"SARS\", single_epiparameter = TRUE) #> Using Lessler J, Reich N, Brookmeyer R, Perl T, Nelson K, Cummings D (2009). #> \"Incubation periods of acute respiratory viral infections: a systematic #> review.\" _The Lancet Infectious Diseases_. #> doi:10.1016/S1473-3099(09)70069-8 #> <https://doi.org/10.1016/S1473-3099%2809%2970069-8>.. #> To retrieve the citation use the 'get_citation' function #> Disease: SARS #> Pathogen: SARS-Cov-1 #> Epi Parameter: incubation period #> Study: Lessler J, Reich N, Brookmeyer R, Perl T, Nelson K, Cummings D (2009). #> \"Incubation periods of acute respiratory viral infections: a systematic #> review.\" _The Lancet Infectious Diseases_. #> doi:10.1016/S1473-3099(09)70069-8 #> <https://doi.org/10.1016/S1473-3099%2809%2970069-8>. #> Distribution: lnorm (days) #> Parameters: #> meanlog: 1.386 #> sdlog: 0.593"},{"path":"https://epiverse-trace.github.io/epiparameter/articles/epiparameter.html","id":"distribution-functions","dir":"Articles","previous_headings":"","what":"Distribution functions","title":"Getting Started with {epiparameter}","text":"<epiparameter> objects store distributions, mathematical functions distribution can easily extracted directly . often useful access probability density function, cumulative distribution function, quantiles distribution, generate random numbers distribution <epiparameter> object. distribution functions {epiparameter} allow users easily use .","code":"ebola_incubation <- epiparameter_db( disease = \"Ebola\", epi_name = \"incubation period\", single_epiparameter = TRUE ) #> Using WHO Ebola Response Team, Agua-Agum J, Ariyarajah A, Aylward B, Blake I, #> Brennan R, Cori A, Donnelly C, Dorigatti I, Dye C, Eckmanns T, Ferguson #> N, Formenty P, Fraser C, Garcia E, Garske T, Hinsley W, Holmes D, #> Hugonnet S, Iyengar S, Jombart T, Krishnan R, Meijers S, Mills H, #> Mohamed Y, Nedjati-Gilani G, Newton E, Nouvellet P, Pelletier L, #> Perkins D, Riley S, Sagrado M, Schnitzler J, Schumacher D, Shah A, Van #> Kerkhove M, Varsaneux O, Kannangarage N (2015). \"West African Ebola #> Epidemic after One Year — Slowing but Not Yet under Control.\" _The New #> England Journal of Medicine_. doi:10.1056/NEJMc1414992 #> <https://doi.org/10.1056/NEJMc1414992>.. #> To retrieve the citation use the 'get_citation' function density(ebola_incubation, at = 0.5) #> [1] 0.03608013 cdf(ebola_incubation, q = 0.5) #> [1] 0.01178094 quantile(ebola_incubation, p = 0.5) #> [1] 8.224347 generate(ebola_incubation, times = 10) #> [1] 7.089471 25.516370 29.272107 12.667909 13.194056 5.102120 6.318984 #> [8] 11.372114 9.391204 9.740407"},{"path":"https://epiverse-trace.github.io/epiparameter/articles/epiparameter.html","id":"plotting-epidemiological-distributions","dir":"Articles","previous_headings":"","what":"Plotting epidemiological distributions","title":"Getting Started with {epiparameter}","text":"<epiparameter> objects can easily plotted see PDF CDF distribution. default plotting range time since infection zero 99th quantile distribution. can altered specifying xlim argument plotting <epiparameter> object. plotting function can useful visually comparing epidemiological distributions different publications disease. addition, plotting distribution manually creating <epiparameter> help check parameters sensible produce expected distribution.","code":"plot(ebola_incubation) plot(ebola_incubation, xlim = c(1, 25))"},{"path":"https://epiverse-trace.github.io/epiparameter/articles/epiparameter.html","id":"accessors","dir":"Articles","previous_headings":"Plotting epidemiological distributions","what":"Accessors","title":"Getting Started with {epiparameter}","text":"<epiparameter> class also accessor functions can help access elements object standardised format.","code":"get_parameters(ebola_incubation) #> shape scale #> 1.577781 6.528155 get_citation(ebola_incubation) #> WHO Ebola Response Team, Agua-Agum J, Ariyarajah A, Aylward B, Blake I, #> Brennan R, Cori A, Donnelly C, Dorigatti I, Dye C, Eckmanns T, Ferguson #> N, Formenty P, Fraser C, Garcia E, Garske T, Hinsley W, Holmes D, #> Hugonnet S, Iyengar S, Jombart T, Krishnan R, Meijers S, Mills H, #> Mohamed Y, Nedjati-Gilani G, Newton E, Nouvellet P, Pelletier L, #> Perkins D, Riley S, Sagrado M, Schnitzler J, Schumacher D, Shah A, Van #> Kerkhove M, Varsaneux O, Kannangarage N (2015). \"West African Ebola #> Epidemic after One Year — Slowing but Not Yet under Control.\" _The New #> England Journal of Medicine_. doi:10.1056/NEJMc1414992 #> <https://doi.org/10.1056/NEJMc1414992>."},{"path":[]},{"path":"https://epiverse-trace.github.io/epiparameter/articles/epiparameter.html","id":"conversion","dir":"Articles","previous_headings":"Parameter conversion and extraction","what":"Conversion","title":"Getting Started with {epiparameter}","text":"Parameters often reported literature mean standard deviation (variance). summary statistics can often (analytically) converted parameters distribution using conversion function package (convert_summary_stats_to_params()). also provide conversion functions opposite direction, parameters summary statistics (convert_params_to_summary_stats()).","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/articles/epiparameter.html","id":"extraction","dir":"Articles","previous_headings":"Parameter conversion and extraction","what":"Extraction","title":"Getting Started with {epiparameter}","text":"functions extract_param() handles extraction parameter estimates summary statistics. two extractions currently supported {epiparameter} percentiles median range.","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/articles/epiparameter.html","id":"adding-library-entries-and-contributing-to-epiparameter","dir":"Articles","previous_headings":"","what":"Adding library entries and contributing to {epiparameter}","title":"Getting Started with {epiparameter}","text":"set epidemiological parameter inferred known user yet incorporated {epiparameter} database, parameters can manually added library. Note adds parameters library environment, save database file package. Hence, restart R session, lose changes. library epidemiological parameters living database, new studies published hope incorporate . Searching recording parameters database extremely time-consuming, welcome contributions new parameters either making pull request package adding information contributing spreadsheet. incorporated database package maintainers contributions acknowledged. See Data Collation Synthesis Protocol vignette information contributing library epidemiological parameters.","code":"# wrap <epiparameter> in list to append to database new_db <- append(db, covid_incubation) tail(new_db, n = 3) #> [[1]] #> Disease: Chikungunya #> Pathogen: Chikungunya Virus #> Epi Parameter: generation time #> Study: Guzzetta G, Vairo F, Mammone A, Lanini S, Poletti P, Manica M, Rosa R, #> Caputo B, Solimini A, della Torre A, Scognamiglio P, Zumla A, Ippolito #> G, Merler S (2020). \"Spatial modes for transmission of chikungunya #> virus during a large chikungunya outbreak in Italy: a modeling #> analysis.\" _BMC Medicine_. doi:10.1186/s12916-020-01674-y #> <https://doi.org/10.1186/s12916-020-01674-y>. #> Distribution: gamma (days) #> Parameters: #> shape: 8.633 #> scale: 1.447 #> #> [[2]] #> Disease: Chikungunya #> Pathogen: Chikungunya Virus #> Epi Parameter: case fatality risk #> Study: de Souza W, de Lima S, Mello L, Candido D, Buss L, Whittaker C, Claro #> I, Chandradeva N, Granja F, de Jesus R, Lemos P, Toledo-Teixeira D, #> Barbosa P, Firmino A, Amorim M, Duarte L, Pessoa Jr I, Forato J, #> Vasconcelos I, Maximo A, Araújo E, Mello L, Sabino E, Proença-Módena J, #> Faria N, Weaver S (2023). \"Spatiotemporal dynamics and recurrence of #> chikungunya virus in Brazil: an epidemiological study.\" _The Lancet #> Microbe_. doi:10.1016/S2666-5247(23)00033-2 #> <https://doi.org/10.1016/S2666-5247%2823%2900033-2>. #> Parameters: <no parameters> #> Mean: 1.3 (deaths per 1000 cases) #> #> [[3]] #> Disease: COVID-19 #> Pathogen: SARS-CoV-2 #> Epi Parameter: incubation period #> Study: Smith J, Jones A (2022). \"COVID Incubation Period.\" _Epi Journal_. #> doi:10.27861182.x <https://doi.org/10.27861182.x>. #> Distribution: gamma (NA) #> Parameters: #> shape: 2.000 #> scale: 1.000"},{"path":[]},{"path":"https://epiverse-trace.github.io/epiparameter/articles/extract-bias.html","id":"extraction-by-percentiles","dir":"Articles","previous_headings":"Extraction Bias","what":"Extraction by percentiles","title":"{epiparameter} Extraction Bias Analysis","text":"First explore extraction percentiles. study reports percentiles distribution, usually symmetrical (e.g. 5th 95th, 2.5th 97.5th). However, instances, asymmetrical percentiles available. test whether asymmetry varying degrees influences bias parameter extraction distributions. set parameter space explore: Now can run extraction point parameter space. set seed control stochasticity estimating parameters, however changing removing seed drastically change results interpretation. extract_param() function re-runs optimisation convergence set tolerance achieved (maximum number iterations reached) reliably return global optimum. theory, help minimise bias instability parameter estimation. See function documentation (?extract_param()) Conversion Extraction vignette details. extraction bias can explored:","code":"distributions <- c(\"gamma\", \"lnorm\", \"weibull\") dist_parameters <- seq(0.5, 2, 0.5) lower_percentiles <- c(2.5, 5, 25, 40) upper_percentiles <- c(60, 95, 97.5) parameters_perc <- expand.grid( dist = distributions, param_1 = dist_parameters, param_2 = dist_parameters, lower = lower_percentiles, upper = upper_percentiles ) # calculate the degree of asymmetry for each percentile combination lw_interval_diff <- abs(0 - parameters_perc$lower) up_interval_diff <- abs(100 - parameters_perc$upper) deg_asym <- abs(lw_interval_diff - up_interval_diff) # add degree of asymmetry to percentiles parameters_perc <- cbind(parameters_perc, deg_asym) # divide percentiles by 100 to make them probabilities for quantile functions parameters_perc$lower <- parameters_perc$lower / 100 parameters_perc$upper <- parameters_perc$upper / 100 set.seed(1) estim_params <- vector(\"list\", nrow(parameters_perc)) # Loop through parameter space estimating parameters for (params_idx in seq_len(nrow(parameters_perc))) { dist <- as.character(parameters_perc[params_idx, \"dist\"]) percen <- unname(unlist(parameters_perc[params_idx, c(\"lower\", \"upper\")])) if (dist == \"lnorm\") { true_values <- do.call( paste0(\"q\", dist), list( p = percen, meanlog = parameters_perc[params_idx, \"param_1\"], sdlog = parameters_perc[params_idx, \"param_2\"] ) ) } else { true_values <- do.call( paste0(\"q\", dist), list( p = percen, shape = parameters_perc[params_idx, \"param_1\"], scale = parameters_perc[params_idx, \"param_2\"] ) ) } # message about stochastic optimisation suppressed estim_params[[params_idx]] <- suppressMessages( extract_param( type = \"percentiles\", values = true_values, distribution = dist, percentiles = percen ) ) } # combine results results <- cbind(parameters_perc, do.call(rbind, estim_params)) colnames(results) <- c( \"dist\", \"param_1\", \"param_2\", \"lower\", \"upper\", \"deg_asym\", \"estim_param_1\", \"estim_param_2\" ) # calculate absolute difference between true parameter and estimated value results <- cbind( results, diff_param_1 = abs(results$param_1 - results$estim_param_1), diff_param_2 = abs(results$param_2 - results$estim_param_2) ) # plot differences by distribution ggplot(data = results) + geom_point(mapping = aes( x = diff_param_1, y = diff_param_2, colour = deg_asym )) + scale_x_continuous(name = \"Parameter 1 Difference (|true - estimated|)\") + scale_y_continuous(name = \"Parameter 2 Difference (|true - estimated|)\") + labs(colour = \"Percentile Asym.\") + theme_bw() + scale_color_viridis_c() + facet_wrap(facets = vars(dist), scales = \"free\") + theme( strip.background = element_blank(), axis.text.x = element_text(angle = 30, vjust = 0.5) )"},{"path":"https://epiverse-trace.github.io/epiparameter/articles/extract-bias.html","id":"extraction-by-median-and-range","dir":"Articles","previous_headings":"Extraction Bias","what":"Extraction by median and range","title":"{epiparameter} Extraction Bias Analysis","text":"analysis can repeated, time using summary statistic possibly reported studies: median range data. extraction number samples used infer distribution required can impact possible range exhibited data. Set parameter space: Plot results:","code":"n_samples <- c(10, 50, 100) parameters_range <- expand.grid( dist = distributions, # same as above param_1 = dist_parameters, # same as above param_2 = dist_parameters, # same as above n_samples = n_samples ) estim_params <- vector(\"list\", nrow(parameters_range)) # Loop through parameter space estimating parameters for (params_idx in seq_len(nrow(parameters_range))) { dist <- as.character(parameters_range[params_idx, \"dist\"]) n_samples <- parameters_range[params_idx, \"n_samples\"] # while loop to ensure values are min < median < max resample_values <- TRUE while (resample_values) { if (dist == \"lnorm\") { true_median <- do.call( paste0(\"q\", dist), list( p = 0.5, meanlog = parameters_range[params_idx, \"param_1\"], sdlog = parameters_range[params_idx, \"param_2\"] ) ) true_range <- do.call( paste0(\"r\", dist), list( n = n_samples, meanlog = parameters_range[params_idx, \"param_1\"], sdlog = parameters_range[params_idx, \"param_2\"] ) ) true_range <- c(min(true_range), max(true_range)) } else { true_median <- do.call( paste0(\"q\", dist), list( p = 0.5, shape = parameters_range[params_idx, \"param_1\"], scale = parameters_range[params_idx, \"param_2\"] ) ) true_range <- do.call( paste0(\"r\", dist), list( n = n_samples, shape = parameters_range[params_idx, \"param_1\"], scale = parameters_range[params_idx, \"param_2\"] ) ) true_range <- c(min(true_range), max(true_range)) } true_values <- c(true_median, true_range) if (true_values[2] < true_values[1] && true_values[1] < true_values[3]) { resample_values <- FALSE } } # message about stochastic optimisation suppressed estim_params[[params_idx]] <- suppressMessages( expr = extract_param( type = \"range\", values = true_values, distribution = dist, samples = n_samples ) ) } #> Warning: Maximum optimisation iterations reached, returning result early. #> Result may not be reliable. # combine results results <- cbind(parameters_range, do.call(rbind, estim_params)) colnames(results) <- c( \"dist\", \"param_1\", \"param_2\", \"n_samples\", \"estim_param_1\", \"estim_param_2\" ) # calculate absolute difference between true parameter and estimated value results <- cbind( results, diff_param_1 = abs(results$param_1 - results$estim_param_1), diff_param_2 = abs(results$param_2 - results$estim_param_2) ) # plot differences by distribution ggplot(data = results) + geom_point( mapping = aes( x = diff_param_1, y = diff_param_2, colour = n_samples ) ) + scale_x_continuous(name = \"Parameter 1 Difference (|true - estimated|)\") + scale_y_continuous(name = \"Parameter 2 Difference (|true - estimated|)\") + labs(colour = \"No. Samples\") + theme_bw() + scale_color_viridis_c() + facet_wrap(facets = vars(dist), scales = \"free\") + theme( strip.background = element_blank(), axis.text.x = element_text(angle = 30, vjust = 0.5) )"},{"path":[]},{"path":"https://epiverse-trace.github.io/epiparameter/articles/extract-bias.html","id":"extraction-by-percentiles-1","dir":"Articles","previous_headings":"Extraction precision","what":"Extraction by percentiles","title":"{epiparameter} Extraction Bias Analysis","text":"two analyses used single extraction (replicate), however, may estimation parameters unstable given set percentiles median range. Therefore, finish test whether repeated extraction parameters single percentile large variance indicate parameter extraction unstable, imprecise, potentially untrustworthy. use parameter space percentiles defined (parameters_perc). Now can run extraction set replicates compute variance parameter estimates replicates.","code":"estim_param_var <- vector(\"list\", nrow(parameters_perc)) # Loop through parameter space estimating parameters for (params_idx in seq_len(nrow(parameters_perc))) { dist <- as.character(parameters_perc[params_idx, \"dist\"]) percen <- unname(unlist(parameters_perc[params_idx, c(\"lower\", \"upper\")])) if (dist == \"lnorm\") { true_values <- do.call( paste0(\"q\", dist), list( p = percen, meanlog = parameters_perc[params_idx, \"param_1\"], sdlog = parameters_perc[params_idx, \"param_2\"] ) ) } else { true_values <- do.call( paste0(\"q\", dist), list( p = percen, shape = parameters_perc[params_idx, \"param_1\"], scale = parameters_perc[params_idx, \"param_2\"] ) ) } # message about stochastic optimisation suppressed estim <- suppressMessages( replicate( n = 5, expr = extract_param( type = \"percentiles\", values = true_values, distribution = dist, percentiles = percen ) ) ) estim_param_var[[params_idx]] <- apply(estim, MARGIN = 1, FUN = var) } # combine results results <- cbind(parameters_perc, do.call(rbind, estim_param_var)) colnames(results) <- c( \"dist\", \"param_1\", \"param_2\", \"lower\", \"upper\", \"deg_asym\", \"estim_param_1_var\", \"estim_param_2_var\" ) ggplot(data = results) + geom_point(mapping = aes( x = estim_param_1_var, y = estim_param_2_var, colour = deg_asym )) + scale_x_continuous(name = \"Parameter 1 Variance\") + scale_y_continuous(name = \"Parameter 2 Variance\") + labs(colour = \"Percentile Asym.\") + theme_bw() + scale_color_viridis_c() + facet_wrap(facets = vars(dist), scales = \"free\") + theme( strip.background = element_blank(), axis.text.x = element_text(angle = 30, vjust = 0.5) )"},{"path":"https://epiverse-trace.github.io/epiparameter/articles/extract-bias.html","id":"extraction-by-median-and-range-1","dir":"Articles","previous_headings":"Extraction precision","what":"Extraction by median and range","title":"{epiparameter} Extraction Bias Analysis","text":"test estimation precision can performed extraction median range. plots vignette, bias low precision high extracting parameters gamma, lognormal Weibull distributions percentiles distribution median range data set. asymmetry percentiles sample size data noticeably influence bias parameter extraction. However, ensure reliable extract use cases extract_param() function recommend checking output spurious results.","code":"estim_param_var <- vector(\"list\", nrow(parameters_range)) # Loop through parameter space estimating parameters for (params_idx in seq_len(nrow(parameters_range))) { dist <- as.character(parameters_range[params_idx, \"dist\"]) n_samples <- parameters_range[params_idx, \"n_samples\"] # while loop to ensure values are min < median < max resample_values <- TRUE while (resample_values) { if (dist == \"lnorm\") { true_median <- do.call( paste0(\"q\", dist), list( p = 0.5, meanlog = parameters_range[params_idx, \"param_1\"], sdlog = parameters_range[params_idx, \"param_2\"] ) ) true_range <- do.call( paste0(\"r\", dist), list( n = n_samples, meanlog = parameters_range[params_idx, \"param_1\"], sdlog = parameters_range[params_idx, \"param_2\"] ) ) true_range <- c(min(true_range), max(true_range)) } else { true_median <- do.call( paste0(\"q\", dist), list( p = 0.5, shape = parameters_range[params_idx, \"param_1\"], scale = parameters_range[params_idx, \"param_2\"] ) ) true_range <- do.call( paste0(\"r\", dist), list( n = n_samples, shape = parameters_range[params_idx, \"param_1\"], scale = parameters_range[params_idx, \"param_2\"] ) ) true_range <- c(min(true_range), max(true_range)) } true_values <- c(true_median, true_range) if (true_values[2] < true_values[1] && true_values[1] < true_values[3]) { resample_values <- FALSE } } # message about stochastic optimisation suppressed estim <- suppressMessages( replicate( n = 5, expr = extract_param( type = \"range\", values = true_values, distribution = dist, samples = n_samples ) ) ) estim_param_var[[params_idx]] <- apply(estim, MARGIN = 1, FUN = var) } #> Warning: Maximum optimisation iterations reached, returning result early. #> Result may not be reliable. # combine results results <- cbind(parameters_range, do.call(rbind, estim_param_var)) colnames(results) <- c( \"dist\", \"param_1\", \"param_2\", \"n_samples\", \"estim_param_1_var\", \"estim_param_2_var\" ) ggplot(data = results) + geom_point(mapping = aes( x = estim_param_1_var, y = estim_param_2_var, colour = n_samples )) + scale_x_continuous(name = \"Parameter 1 Variance\") + scale_y_continuous(name = \"Parameter 2 Variance\") + labs(colour = \"No. Samples\") + theme_bw() + scale_color_viridis_c() + facet_wrap(facets = vars(dist), scales = \"free\") + theme( strip.background = element_blank(), axis.text.x = element_text(angle = 30, vjust = 0.5) )"},{"path":"https://epiverse-trace.github.io/epiparameter/articles/extract_convert.html","id":"conversion-versus-extraction","dir":"Articles","previous_headings":"","what":"Conversion versus extraction","title":"Parameter extraction and conversion in {epiparameter}","text":"Use conversion possible extraction avoid possible limitations associated numerical optimisation used extraction function extract_param().","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/articles/extract_convert.html","id":"conversions","dir":"Articles","previous_headings":"","what":"Conversions","title":"Parameter extraction and conversion in {epiparameter}","text":"two conversion functions {epiparameter}: convert_params_to_summary_stats() convert_summary_stats_to_params(). convert_params_to_summary_stats() converts one set statistical distribution parameters common summary statistics, convert_summary_stats_to_params() converts summary statistics set parameters.","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/articles/extract_convert.html","id":"conversion-functions","dir":"Articles","previous_headings":"Conversions","what":"Conversion functions","title":"Parameter extraction and conversion in {epiparameter}","text":"conversion functions can take two types inputs first argument: character string distribution <epiparameter> object. conversion functions two arguments. first (x) defines distribution want use second (...) lets put many named parameters summary statistics required. arguments passed ... matched name, therefore need match exactly names expected. See function documentation (?convert_params_to_summary_stats ?convert_summary_stats_to_params names). case <epiparameter> object supplied, parameters summary statistics required conversion nothing needs given extra arguments (.e. ...). currently supported summary statistic conversions {epiparameter} given distribution.","code":""},{"path":[]},{"path":"https://epiverse-trace.github.io/epiparameter/articles/extract_convert.html","id":"using-a-character-string-to-name-distribution","dir":"Articles","previous_headings":"Conversions > Conversion functions","what":"Using a character string to name distribution","title":"Parameter extraction and conversion in {epiparameter}","text":"","code":"convert_params_to_summary_stats(\"gamma\", shape = 2.5, scale = 1.5) #> $mean #> [1] 3.75 #> #> $median #> [1] 1.450487 #> #> $mode #> [1] 2.25 #> #> $var #> [1] 5.625 #> #> $sd #> [1] 2.371708 #> #> $cv #> [1] 0.6324555 #> #> $skewness #> [1] 1.264911 #> #> $ex_kurtosis #> [1] 2.4 convert_summary_stats_to_params(\"gamma\", mean = 2, sd = 2) #> $shape #> [1] 1 #> #> $scale #> [1] 2 convert_summary_stats_to_params(\"gamma\", mean = 2, var = 2) #> $shape #> [1] 2 #> #> $scale #> [1] 1 convert_summary_stats_to_params(\"gamma\", mean = 2, cv = 2) #> $shape #> [1] 0.25 #> #> $scale #> [1] 8"},{"path":"https://epiverse-trace.github.io/epiparameter/articles/extract_convert.html","id":"using-an-epiparameter","dir":"Articles","previous_headings":"Conversions > Conversion functions","what":"Using an <epiparameter>","title":"Parameter extraction and conversion in {epiparameter}","text":"example parameters provided <epiparameter> example <epiparameter> missing parameters supplied ... example summary statistics provided <epiparameter> example <epiparameter> missing summary statistics supplied ... usage <epiparameter> repeated every distribution conversion available {epiparameter}. conversions shown distribution also available using <epiparameter> object, either parameters summary statistics stored <epiparameter> supplied via named arguments.","code":"ep <- epiparameter( disease = \"<Disease name>\", pathogen = \"<Pathogen name>\", epi_name = \"<Epidemilogical Distribution name>\", prob_distribution = create_prob_distribution( prob_distribution = \"gamma\", prob_distribution_params = c(shape = 2.5, scale = 1.5) ) ) #> Citation cannot be created as author, year, journal or title is missing convert_params_to_summary_stats(ep) #> $mean #> [1] 3.75 #> #> $median #> [1] 1.450487 #> #> $mode #> [1] 2.25 #> #> $var #> [1] 5.625 #> #> $sd #> [1] 2.371708 #> #> $cv #> [1] 0.6324555 #> #> $skewness #> [1] 1.264911 #> #> $ex_kurtosis #> [1] 2.4 ep <- epiparameter( disease = \"<Disease name>\", pathogen = \"<Pathogen name>\", epi_name = \"<Epidemilogical Distribution name>\", prob_distribution = \"gamma\" ) #> Citation cannot be created as author, year, journal or title is missing #> No adequate summary statistics available to calculate the parameters of the gamma distribution #> Unparameterised <epiparameter> object convert_params_to_summary_stats(ep, shape = 2.5, scale = 1.5) #> $mean #> [1] 3.75 #> #> $median #> [1] 1.450487 #> #> $mode #> [1] 2.25 #> #> $var #> [1] 5.625 #> #> $sd #> [1] 2.371708 #> #> $cv #> [1] 0.6324555 #> #> $skewness #> [1] 1.264911 #> #> $ex_kurtosis #> [1] 2.4 ep <- epiparameter( disease = \"<Disease name>\", pathogen = \"<Pathogen name>\", epi_name = \"<Epidemilogical Distribution name>\", prob_distribution = \"gamma\", summary_stats = create_summary_stats(mean = 3.75, sd = 2.37) ) #> Citation cannot be created as author, year, journal or title is missing #> Parameterising the probability distribution with the summary statistics. #> Probability distribution is assumed not to be discretised or truncated. convert_summary_stats_to_params(ep) #> $shape #> [1] 2.503605 #> #> $scale #> [1] 1.49784 ep <- epiparameter( disease = \"<Disease name>\", pathogen = \"<Pathogen name>\", epi_name = \"<Epidemilogical Distribution name>\", prob_distribution = \"gamma\" ) #> Citation cannot be created as author, year, journal or title is missing #> No adequate summary statistics available to calculate the parameters of the gamma distribution #> Unparameterised <epiparameter> object convert_summary_stats_to_params(ep, mean = 3.75, sd = 2.37) #> $shape #> [1] 2.503605 #> #> $scale #> [1] 1.49784"},{"path":"https://epiverse-trace.github.io/epiparameter/articles/extract_convert.html","id":"lognormal-distribution","dir":"Articles","previous_headings":"Conversions > Conversion functions","what":"Lognormal distribution","title":"Parameter extraction and conversion in {epiparameter}","text":"","code":"convert_params_to_summary_stats(\"lnorm\", meanlog = 2.5, sdlog = 1.5) #> $mean #> [1] 37.52472 #> #> $median #> [1] 12.18249 #> #> $mode #> [1] 1.284025 #> #> $var #> [1] 11951.62 #> #> $sd #> [1] 109.3235 #> #> $cv #> [1] 2.913372 #> #> $skewness #> [1] 33.46805 #> #> $ex_kurtosis #> [1] 10075.25 convert_summary_stats_to_params(\"lnorm\", mean = 2, sd = 2) #> $meanlog #> [1] 0.3465736 #> #> $sdlog #> [1] 0.8325546 convert_summary_stats_to_params(\"lnorm\", mean = 2, var = 2) #> $meanlog #> [1] 0.4904146 #> #> $sdlog #> [1] 0.6367614 convert_summary_stats_to_params(\"lnorm\", mean = 2, cv = 2) #> $meanlog #> [1] -0.1115718 #> #> $sdlog #> [1] 1.268636 convert_summary_stats_to_params(\"lnorm\", median = 2, sd = 2) #> $meanlog #> [1] 0.3465736 #> #> $sdlog #> [1] 0.8325546 convert_summary_stats_to_params(\"lnorm\", median = 2, var = 2) #> $meanlog #> [1] 0.4904146 #> #> $sdlog #> [1] 0.6367614"},{"path":"https://epiverse-trace.github.io/epiparameter/articles/extract_convert.html","id":"weibull-distribution","dir":"Articles","previous_headings":"Conversions > Conversion functions","what":"Weibull distribution","title":"Parameter extraction and conversion in {epiparameter}","text":"","code":"convert_params_to_summary_stats(\"weibull\", shape = 2.5, scale = 1.5) #> $mean #> [1] 1.330896 #> #> $median #> [1] 1.295452 #> #> $mode #> [1] 1.22279 #> #> $var #> [1] 0.3243301 #> #> $sd #> [1] 0.5694998 #> #> $cv #> [1] 0.4279072 #> #> $skewness #> [1] 0.3586318 #> #> $ex_kurtosis #> [1] -0.1432169 convert_summary_stats_to_params(\"weibull\", mean = 2, sd = 2) #> Numerical approximation used, results may be unreliable. #> $shape #> [1] 1.000016 #> #> $scale #> [1] 2.000014 convert_summary_stats_to_params(\"weibull\", mean = 2, var = 2) #> Numerical approximation used, results may be unreliable. #> $shape #> [1] 1.435521 #> #> $scale #> [1] 2.202641 convert_summary_stats_to_params(\"weibull\", mean = 2, cv = 2) #> Numerical approximation used, results may be unreliable. #> $shape #> [1] 0.5427068 #> #> $scale #> [1] 1.150547"},{"path":"https://epiverse-trace.github.io/epiparameter/articles/extract_convert.html","id":"negative-binomial-distribution","dir":"Articles","previous_headings":"Conversions > Conversion functions","what":"Negative binomial distribution","title":"Parameter extraction and conversion in {epiparameter}","text":"","code":"convert_params_to_summary_stats(\"nbinom\", prob = 0.5, dispersion = 0.5) #> $mean #> [1] 0.5 #> #> $median #> [1] 0 #> #> $mode #> [1] 0 #> #> $var #> [1] 1 #> #> $sd #> [1] 1 #> #> $cv #> [1] 2 #> #> $skewness #> [1] 3 #> #> $ex_kurtosis #> [1] 12.25 convert_summary_stats_to_params(\"nbinom\", mean = 1, sd = 1) #> $prob #> [1] 1 #> #> $dispersion #> [1] Inf convert_summary_stats_to_params(\"nbinom\", mean = 1, var = 1) #> $prob #> [1] 1 #> #> $dispersion #> [1] Inf convert_summary_stats_to_params(\"nbinom\", mean = 1, cv = 1) #> $prob #> [1] 1 #> #> $dispersion #> [1] Inf"},{"path":"https://epiverse-trace.github.io/epiparameter/articles/extract_convert.html","id":"geometric-distribution","dir":"Articles","previous_headings":"Conversions > Conversion functions","what":"Geometric distribution","title":"Parameter extraction and conversion in {epiparameter}","text":"","code":"convert_params_to_summary_stats(\"geom\", prob = 0.5) #> $mean #> [1] 1 #> #> $median #> [1] 0 #> #> $mode #> [1] 0 #> #> $var #> [1] 2 #> #> $sd #> [1] 1.414214 #> #> $cv #> [1] 1.414214 #> #> $skewness #> [1] 2.12132 #> #> $ex_kurtosis #> [1] 6.5 convert_summary_stats_to_params(\"geom\", mean = 1) #> $prob #> [1] 0.5"},{"path":"https://epiverse-trace.github.io/epiparameter/articles/extract_convert.html","id":"extraction","dir":"Articles","previous_headings":"","what":"Extraction","title":"Parameter extraction and conversion in {epiparameter}","text":"two methods extraction implemented {epiparameter}. One estimate parameters given values two percentiles, estimate parameters given median range data. extractions implemented extract_param() function. demonstrate extraction using percentiles. type \"percentiles\", values values reported percentiles, given vector. percentiles, given 0 1, specified vector percentiles. example uses values 1 10 2.5th 97.5th percentile, respectively. example estimate parameters gamma distribution, extraction also implemented lognormal, normal Weibull distributions, specifying \"lnorm\", \"norm\" \"weibull\". message shown running extract_param() make user aware estimates completely reliable due use numerical optimisation. Rerunning function finding parameters returned indicates successfully converged. issue mostly overcome internal setup extract_param() function searches convergence consistent parameter estimates returning user. alternative extraction, median range, can achieved specifying type = \"range\" using samples argument instead percentiles argument. using type = \"percentiles\" samples argument ignored using type = \"range\" percentiles argument ignored. section mentioned extract_param() internal mechanism check parameters consistently converged estimates several optimisation iterations. tolerance convergence number times optimisation can repeated specified control argument extract_param(). set default (tolerance = 1e-5 max_iter = 1000), thus need specified user (shown examples). case maximum number optimisation iterations reached, calculation terminates returning recent optimisation result user along warning message. reasoning default maximum number iterations limit computation time prevent function cycling optimisation routines without converging consistent answer. runtime important parameter accuracy paramount maximum number iterations can increased tolerance decreased. control settings work identically extracting percentiles median range. Donnelly et al. (2003) provides mean variance gamma distribution incubation period SARS. conversion can achieved using general conversion function (convert_summary_stats_to_params()).","code":"extract_param( type = \"percentiles\", values = c(1, 10), distribution = \"gamma\", percentiles = c(0.025, 0.975) ) #> Stochastic numerical optimisation used. #> Rerun function multiple times to check global optimum is found #> shape scale #> 3.358202 1.284186 extract_param( type = \"range\", values = c(10, 5, 15), distribution = \"lnorm\", samples = 25 ) #> Stochastic numerical optimisation used. #> Rerun function multiple times to check global optimum is found #> meanlog sdlog #> 2.302584 3.939920 # set seed to ensure warning is produced set.seed(1) # lower maximum iteration to show warning extract_param( type = \"range\", values = c(10, 1, 25), distribution = \"lnorm\", samples = 100, control = list(max_iter = 100) ) #> Warning: Maximum optimisation iterations reached, returning result early. #> Result may not be reliable. #> Stochastic numerical optimisation used. #> Rerun function multiple times to check global optimum is found #> meanlog sdlog #> 2.3025851 0.7942061 # SARS gamma mean and var to shape and scale convert_summary_stats_to_params(\"gamma\", mean = 6.37, var = 16.7) #> $shape #> [1] 2.429754 #> #> $scale #> [1] 2.621664"},{"path":"https://epiverse-trace.github.io/epiparameter/articles/extract_convert.html","id":"use-cases","dir":"Articles","previous_headings":"Extraction","what":"Use cases","title":"Parameter extraction and conversion in {epiparameter}","text":"present examples published epidemiological parameters distributions functions outlined can applied get parameters distribution. 75th percentiles reported lognormal distribution Nolen et al. (2016) incubation period mpox (monkeypox). median range provided Thornhill et al. (2022) mpox, want calculate parameters lognormal distribution.","code":"# Mpox lnorm from 75th percentiles in WHO data extract_param( type = \"percentiles\", values = c(6, 13), distribution = \"lnorm\", percentiles = c(0.125, 0.875) ) #> Stochastic numerical optimisation used. #> Rerun function multiple times to check global optimum is found #> meanlog sdlog #> 2.1783544 0.3360684 # Mpox lnorm from median and range in 2022: extract_param( type = \"range\", values = c(7, 3, 20), distribution = \"lnorm\", samples = 23 ) #> Stochastic numerical optimisation used. #> Rerun function multiple times to check global optimum is found #> meanlog sdlog #> 1.945910 4.735285"},{"path":"https://epiverse-trace.github.io/epiparameter/articles/extract_convert.html","id":"assuming-distributions","dir":"Articles","previous_headings":"Extraction","what":"Assuming distributions","title":"Parameter extraction and conversion in {epiparameter}","text":"can case study report summary statistics unspecified distribution just raw data. cases parameterised distribution required downstream analysis functional, parametric, form may assumed. distribution delay distribution (.e. serial interval incubation period) can often sensible assume right-skewed distribution : gamma, lognormal Weibull distributions. also commonly fit distributions epidemiological analysis delay distributions. However, one take care assuming distribution may drastically influence interpretation application epidemiological parameters.","code":""},{"path":[]},{"path":"https://epiverse-trace.github.io/epiparameter/authors.html","id":null,"dir":"","previous_headings":"","what":"Authors","title":"Authors and Citation","text":"Joshua W. Lambert. Author, maintainer, copyright holder. Adam Kucharski. Author, copyright holder. Carmen Tamayo. Author. Hugo Gruson. Contributor, reviewer. Sebastian Funk. Contributor. Pratik Gupte. Reviewer. James M. Azam. Reviewer. Chris Hartgerink. Reviewer. Tim Taylor. Reviewer.","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/authors.html","id":"citation","dir":"","previous_headings":"","what":"Citation","title":"Authors and Citation","text":"Lambert J, Kucharski , Tamayo C (2024). epiparameter: Library Epidemiological Parameters Helper Functions Classes. doi:10.5281/zenodo.11110881, https://epiverse-trace.github.io/epiparameter/.","code":"@Manual{, title = {epiparameter: Library of Epidemiological Parameters with Helper Functions and Classes}, author = {Joshua W. Lambert and Adam Kucharski and Carmen Tamayo}, year = {2024}, doi = {10.5281/zenodo.11110881}, url = {https://epiverse-trace.github.io/epiparameter/}, }"},{"path":"https://epiverse-trace.github.io/epiparameter/index.html","id":"epiparameter-","dir":"","previous_headings":"","what":"Library of Epidemiological Parameters with Helper Functions and Classes","title":"Library of Epidemiological Parameters with Helper Functions and Classes","text":"epiparameter R package contains library epidemiological parameters infectious diseases well classes helper functions work data. also includes functions extract convert parameters reported summary statistics. epiparameter developed Centre Mathematical Modelling Infectious Diseases London School Hygiene Tropical Medicine part Epiverse-TRACE.","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/index.html","id":"installation","dir":"","previous_headings":"","what":"Installation","title":"Library of Epidemiological Parameters with Helper Functions and Classes","text":"development version epiparameter can installed GitHub using pak package: Alternatively, install pre-compiled binaries Epiverse TRACE R-universe","code":"# check whether {pak} is installed if(!require(\"pak\")) install.packages(\"pak\") pak::pak(\"epiverse-trace/epiparameter\") install.packages(\"epiparameter\", repos = c(\"https://epiverse-trace.r-universe.dev\", \"https://cloud.r-project.org\"))"},{"path":"https://epiverse-trace.github.io/epiparameter/index.html","id":"quick-start","dir":"","previous_headings":"","what":"Quick start","title":"Library of Epidemiological Parameters with Helper Functions and Classes","text":"load library epidemiological parameters R: results list database entries. entry library <epiparameter> object. Alternatively, library epiparameters can viewed vignette locally (vignette(\"database\", package = \"epiparameter\")) {epiparameter} website. results can filtered disease epidemiological distribution. set single_epiparameter = TRUE want single database entry returned, default (single_epiparameter = FALSE) return database entries match disease (disease) epidemiological parameter (epi_name). quickly view list epidemiological distributions returned epiparameter_db() table, parameter_tbl() gives summary data, offers ability subset data disease, pathogen epidemiological parameter (epi_name). <epiparameter> object can plotted. CDF can also plotted setting cumulative = TRUE.","code":"library(epiparameter) epiparameters <- epiparameter_db() #> Returning 125 results that match the criteria (100 are parameterised). #> Use subset to filter by entry variables or single_epiparameter to return a single entry. #> To retrieve the citation for each use the 'get_citation' function epiparameters #> # List of 125 <epiparameter> objects #> Number of diseases: 23 #> ❯ Adenovirus ❯ Chikungunya ❯ COVID-19 ❯ Dengue ❯ Ebola Virus Disease ❯ Hantavirus Pulmonary Syndrome ❯ Human Coronavirus ❯ Influenza ❯ Japanese Encephalitis ❯ Marburg Virus Disease ❯ Measles ❯ MERS ❯ Mpox ❯ Parainfluenza ❯ Pneumonic Plague ❯ Rhinovirus ❯ Rift Valley Fever ❯ RSV ❯ SARS ❯ Smallpox ❯ West Nile Fever ❯ Yellow Fever ❯ Zika Virus Disease #> Number of epi parameters: 13 #> ❯ case fatality risk ❯ generation time ❯ hospitalisation to death ❯ hospitalisation to discharge ❯ incubation period ❯ notification to death ❯ notification to discharge ❯ offspring distribution ❯ onset to death ❯ onset to discharge ❯ onset to hospitalisation ❯ onset to ventilation ❯ serial interval #> [[1]] #> Disease: Adenovirus #> Pathogen: Adenovirus #> Epi Parameter: incubation period #> Study: Lessler J, Reich N, Brookmeyer R, Perl T, Nelson K, Cummings D (2009). #> \"Incubation periods of acute respiratory viral infections: a systematic #> review.\" _The Lancet Infectious Diseases_. #> doi:10.1016/S1473-3099(09)70069-6 #> <https://doi.org/10.1016/S1473-3099%2809%2970069-6>. #> Distribution: lnorm #> Parameters: #> meanlog: 1.723 #> sdlog: 0.231 #> #> [[2]] #> Disease: Human Coronavirus #> Pathogen: Human_Cov #> Epi Parameter: incubation period #> Study: Lessler J, Reich N, Brookmeyer R, Perl T, Nelson K, Cummings D (2009). #> \"Incubation periods of acute respiratory viral infections: a systematic #> review.\" _The Lancet Infectious Diseases_. #> doi:10.1016/S1473-3099(09)70069-7 #> <https://doi.org/10.1016/S1473-3099%2809%2970069-7>. #> Distribution: lnorm #> Parameters: #> meanlog: 1.163 #> sdlog: 0.140 #> #> [[3]] #> Disease: SARS #> Pathogen: SARS-Cov-1 #> Epi Parameter: incubation period #> Study: Lessler J, Reich N, Brookmeyer R, Perl T, Nelson K, Cummings D (2009). #> \"Incubation periods of acute respiratory viral infections: a systematic #> review.\" _The Lancet Infectious Diseases_. #> doi:10.1016/S1473-3099(09)70069-8 #> <https://doi.org/10.1016/S1473-3099%2809%2970069-8>. #> Distribution: lnorm #> Parameters: #> meanlog: 1.386 #> sdlog: 0.593 #> #> # ℹ 122 more elements #> # ℹ Use `print(n = ...)` to see more elements. #> # ℹ Use `parameter_tbl()` to see a summary table of the parameters. #> # ℹ Explore database online at: https://epiverse-trace.github.io/epiparameter/articles/database.html influenza_incubation <- epiparameter_db( disease = \"influenza\", epi_name = \"incubation period\", single_epiparameter = TRUE ) #> Using Virlogeux V, Li M, Tsang T, Feng L, Fang V, Jiang H, Wu P, Zheng J, Lau #> E, Cao Y, Qin Y, Liao Q, Yu H, Cowling B (2015). \"Estimating the #> Distribution of the Incubation Periods of Human Avian Influenza A(H7N9) #> Virus Infections.\" _American Journal of Epidemiology_. #> doi:10.1093/aje/kwv115 <https://doi.org/10.1093/aje/kwv115>.. #> To retrieve the citation use the 'get_citation' function influenza_incubation #> Disease: Influenza #> Pathogen: Influenza-A-H7N9 #> Epi Parameter: incubation period #> Study: Virlogeux V, Li M, Tsang T, Feng L, Fang V, Jiang H, Wu P, Zheng J, Lau #> E, Cao Y, Qin Y, Liao Q, Yu H, Cowling B (2015). \"Estimating the #> Distribution of the Incubation Periods of Human Avian Influenza A(H7N9) #> Virus Infections.\" _American Journal of Epidemiology_. #> doi:10.1093/aje/kwv115 <https://doi.org/10.1093/aje/kwv115>. #> Distribution: weibull #> Parameters: #> shape: 2.101 #> scale: 3.839 parameter_tbl(epiparameters) #> # Parameter table: #> # A data frame: 125 × 7 #> disease pathogen epi_name prob_distribution author year sample_size #> <chr> <chr> <chr> <chr> <chr> <dbl> <dbl> #> 1 Adenovirus Adenovi… incubat… lnorm Lessl… 2009 14 #> 2 Human Coronavir… Human_C… incubat… lnorm Lessl… 2009 13 #> 3 SARS SARS-Co… incubat… lnorm Lessl… 2009 157 #> 4 Influenza Influen… incubat… lnorm Lessl… 2009 151 #> 5 Influenza Influen… incubat… lnorm Lessl… 2009 90 #> 6 Influenza Influen… incubat… lnorm Lessl… 2009 78 #> 7 Measles Measles… incubat… lnorm Lessl… 2009 55 #> 8 Parainfluenza Parainf… incubat… lnorm Lessl… 2009 11 #> 9 RSV RSV incubat… lnorm Lessl… 2009 24 #> 10 Rhinovirus Rhinovi… incubat… lnorm Lessl… 2009 28 #> # ℹ 115 more rows parameter_tbl( epiparameters, epi_name = \"onset to hospitalisation\" ) #> # Parameter table: #> # A data frame: 5 × 7 #> disease pathogen epi_name prob_distribution author year sample_size #> <chr> <chr> <chr> <chr> <chr> <dbl> <dbl> #> 1 MERS MERS-Cov onset to hospi… <NA> Assir… 2013 23 #> 2 COVID-19 SARS-CoV-2 onset to hospi… gamma Linto… 2020 155 #> 3 COVID-19 SARS-CoV-2 onset to hospi… gamma Linto… 2020 34 #> 4 COVID-19 SARS-CoV-2 onset to hospi… lnorm Linto… 2020 155 #> 5 COVID-19 SARS-CoV-2 onset to hospi… lnorm Linto… 2020 34 plot(influenza_incubation) plot(influenza_incubation, cumulative = TRUE)"},{"path":"https://epiverse-trace.github.io/epiparameter/index.html","id":"parameter-conversion-and-extraction","dir":"","previous_headings":"Quick start","what":"Parameter conversion and extraction","title":"Library of Epidemiological Parameters with Helper Functions and Classes","text":"parameters distribution can converted mean standard deviation. epiparameter implement variety distributions: gamma lognormal Weibull negative binomial geometric parameters probability distribution can also extracted summary statistics, example, percentiles distribution, median range data. can done : gamma lognormal Weibull normal","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/index.html","id":"contributing-to-library-of-epidemiological-parameters","dir":"","previous_headings":"","what":"Contributing to library of epidemiological parameters","title":"Library of Epidemiological Parameters with Helper Functions and Classes","text":"like contribute different epidemiological parameters stored epiparameter package, can add data public google sheet. spreadsheet contains two example entries guide fields can accept. monitoring sheet new entries subsequently included package. Alternatively, parameters can added JSON file holding data base directly via Pull Request. can find explanation accepted entries column data dictionary.","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/index.html","id":"help","dir":"","previous_headings":"","what":"Help","title":"Library of Epidemiological Parameters with Helper Functions and Classes","text":"report bug please open issue","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/index.html","id":"contribute","dir":"","previous_headings":"","what":"Contribute","title":"Library of Epidemiological Parameters with Helper Functions and Classes","text":"Contributions epiparameter welcomed. package contributing guide.","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/index.html","id":"code-of-conduct","dir":"","previous_headings":"","what":"Code of Conduct","title":"Library of Epidemiological Parameters with Helper Functions and Classes","text":"Please note epiparameter project released Contributor Code Conduct. contributing project, agree abide terms.","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/index.html","id":"citing-this-package","dir":"","previous_headings":"","what":"Citing this package","title":"Library of Epidemiological Parameters with Helper Functions and Classes","text":"","code":"citation(\"epiparameter\") #> To cite package 'epiparameter' in publications use: #> #> Lambert J, Kucharski A, Tamayo C (2024). _epiparameter: Library of #> Epidemiological Parameters with Helper Functions and Classes_. #> doi:10.5281/zenodo.11110881 #> <https://doi.org/10.5281/zenodo.11110881>, #> <https://epiverse-trace.github.io/epiparameter/>. #> #> A BibTeX entry for LaTeX users is #> #> @Manual{, #> title = {epiparameter: Library of Epidemiological Parameters with Helper Functions and Classes}, #> author = {Joshua W. Lambert and Adam Kucharski and Carmen Tamayo}, #> year = {2024}, #> doi = {10.5281/zenodo.11110881}, #> url = {https://epiverse-trace.github.io/epiparameter/}, #> }"},{"path":"https://epiverse-trace.github.io/epiparameter/reference/aggregate.multi_epiparameter.html","id":null,"dir":"Reference","previous_headings":"","what":"Aggregate multiple <epiparameter> objects into a single <epiparameter> object. — aggregate.multi_epiparameter","title":"Aggregate multiple <epiparameter> objects into a single <epiparameter> object. — aggregate.multi_epiparameter","text":"Combine list <epiparameter> objects single <epiparameter> mixture distribution [see distributional::dist_mixture()]. aggregated <epiparameter> returned aggregate() can used density(), cdf(), quantile() generate() methods combined distributions.","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/aggregate.multi_epiparameter.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Aggregate multiple <epiparameter> objects into a single <epiparameter> object. — aggregate.multi_epiparameter","text":"","code":"# S3 method for class 'multi_epiparameter' aggregate(x, weighting = c(\"equal\", \"sample_size\", \"custom\"), ..., weights)"},{"path":"https://epiverse-trace.github.io/epiparameter/reference/aggregate.multi_epiparameter.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Aggregate multiple <epiparameter> objects into a single <epiparameter> object. — aggregate.multi_epiparameter","text":"x <multi_epiparameter> object. weighting character string type weighting use create mixture distribution. Options : \"equal\" equal weighting across distributions, \"sample_size\" using sample size <epiparameter> object weight distribution (sample sizes normalised), \"custom\" allows vector weights passed weights argument custom weighting. ... dots used, warn extra arguments passed function. weights numeric vector equal length number <epiparameter> objects passed x. weights required weighting = \"custom\".","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/aggregate.multi_epiparameter.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Aggregate multiple <epiparameter> objects into a single <epiparameter> object. — aggregate.multi_epiparameter","text":"<epiparameter> object","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/aggregate.multi_epiparameter.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Aggregate multiple <epiparameter> objects into a single <epiparameter> object. — aggregate.multi_epiparameter","text":"aggregate() method requires <epiparameter> objects parameterised <distribution> objects (distributional package). means unparameterised (see is_parameterised()) discretised (see discretise()) distributions aggregated function error.","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/aggregate.multi_epiparameter.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Aggregate multiple <epiparameter> objects into a single <epiparameter> object. — aggregate.multi_epiparameter","text":"","code":"ebola_si <- epiparameter_db(epi_name = \"serial interval\", disease = \"ebola\") #> Returning 4 results that match the criteria (4 are parameterised). #> Use subset to filter by entry variables or single_epiparameter to return a single entry. #> To retrieve the citation for each use the 'get_citation' function aggregate(ebola_si) #> Disease: Ebola Virus Disease #> Pathogen: Ebola Virus #> Epi Parameter: serial interval #> Study: WHO Ebola Response Team, Agua-Agum J, Ariyarajah A, Aylward B, Blake I, #> Brennan R, Cori A, Donnelly C, Dorigatti I, Dye C, Eckmanns T, Ferguson #> N, Formenty P, Fraser C, Garcia E, Garske T, Hinsley W, Holmes D, #> Hugonnet S, Iyengar S, Jombart T, Krishnan R, Meijers S, Mills H, #> Mohamed Y, Nedjati-Gilani G, Newton E, Nouvellet P, Pelletier L, #> Perkins D, Riley S, Sagrado M, Schnitzler J, Schumacher D, Shah A, Van #> Kerkhove M, Varsaneux O, Kannangarage N (2015). “West African Ebola #> Epidemic after One Year — Slowing but Not Yet under Control.” _The New #> England Journal of Medicine_. doi:10.1056/NEJMc1414992 #> <https://doi.org/10.1056/NEJMc1414992>. #> Distribution: mixture: gamma, gamma, gamma, gamma (days) #> Distribution: mixture: gamma, gamma, gamma, gamma (days) #> Distribution: mixture: gamma, gamma, gamma, gamma (days) #> Distribution: mixture: gamma, gamma, gamma, gamma (days) #> Parameters: #> dist.shape: 2.188 #> dist.rate: 0.154 #> dist.shape: 4.903 #> dist.rate: 0.316 #> dist.shape: 2.068 #> dist.rate: 0.137 #> dist.shape: 1.898 #> dist.rate: 0.153 #> w1: 0.250 #> w2: 0.250 #> w3: 0.250 #> w4: 0.250"},{"path":"https://epiverse-trace.github.io/epiparameter/reference/as.data.frame.epiparameter.html","id":null,"dir":"Reference","previous_headings":"","what":"as.data.frame() method for <epiparameter> class — as.data.frame.epiparameter","title":"as.data.frame() method for <epiparameter> class — as.data.frame.epiparameter","text":".data.frame() method <epiparameter> class","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/as.data.frame.epiparameter.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"as.data.frame() method for <epiparameter> class — as.data.frame.epiparameter","text":"","code":"# S3 method for class 'epiparameter' as.data.frame(x, ...)"},{"path":"https://epiverse-trace.github.io/epiparameter/reference/as.data.frame.epiparameter.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"as.data.frame() method for <epiparameter> class — as.data.frame.epiparameter","text":"x <epiparameter> object. ... dots used, extra arguments supplied cause warning.","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/as.data.frame.epiparameter.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"as.data.frame() method for <epiparameter> class — as.data.frame.epiparameter","text":"<data.frame> single row.","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/as.data.frame.epiparameter.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"as.data.frame() method for <epiparameter> class — as.data.frame.epiparameter","text":"<data.frame> returned contain atomic columns (.e. one object per row), columns lists (.e. multiple objects per row). list columns can contain lists S3 objects (e.g. <bibentry> object citation column).","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/as.data.frame.epiparameter.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"as.data.frame() method for <epiparameter> class — as.data.frame.epiparameter","text":"","code":"ep <- epiparameter_db(single_epiparameter = TRUE) #> Using Linton N, Kobayashi T, Yang Y, Hayashi K, Akhmetzhanov A, Jung S, Yuan #> B, Kinoshita R, Nishiura H (2020). “Incubation Period and Other #> Epidemiological Characteristics of 2019 Novel Coronavirus Infections #> with Right Truncation: A Statistical Analysis of Publicly Available #> Case Data.” _Journal of Clinical Medicine_. doi:10.3390/jcm9020538 #> <https://doi.org/10.3390/jcm9020538>.. #> To retrieve the citation use the 'get_citation' function as.data.frame(ep) #> disease pathogen epi_name prob_distribution uncertainty #> 1 COVID-19 SARS-CoV-2 onset to hospitalisation lN(0.95,.... list(unc.... #> summary_stats citation metadata method_assess #> 1 9.7, c(5.... list(aut.... days, 15.... TRUE, TR.... #> notes #> 1 This dataset includes only surviving patients. This method applies right-truncation but only fits a lognormal distribution."},{"path":"https://epiverse-trace.github.io/epiparameter/reference/as.data.frame.multi_epiparameter.html","id":null,"dir":"Reference","previous_headings":"","what":"as.data.frame() method for <multi_epiparameter> class — as.data.frame.multi_epiparameter","title":"as.data.frame() method for <multi_epiparameter> class — as.data.frame.multi_epiparameter","text":".data.frame() method <multi_epiparameter> class","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/as.data.frame.multi_epiparameter.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"as.data.frame() method for <multi_epiparameter> class — as.data.frame.multi_epiparameter","text":"","code":"# S3 method for class 'multi_epiparameter' as.data.frame(x, ...)"},{"path":"https://epiverse-trace.github.io/epiparameter/reference/as.data.frame.multi_epiparameter.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"as.data.frame() method for <multi_epiparameter> class — as.data.frame.multi_epiparameter","text":"x <multi_epiparameter> object. ... dots used, extra arguments supplied cause warning.","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/as.data.frame.multi_epiparameter.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"as.data.frame() method for <multi_epiparameter> class — as.data.frame.multi_epiparameter","text":"<data.frame> many rows length input list.","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/as.data.frame.multi_epiparameter.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"as.data.frame() method for <multi_epiparameter> class — as.data.frame.multi_epiparameter","text":"<data.frame> returned contain atomic columns (.e. one object per row), columns lists (.e. multiple objects per row). list columns can contain lists S3 objects (e.g. <bibentry> object citation column).","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/as.data.frame.multi_epiparameter.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"as.data.frame() method for <multi_epiparameter> class — as.data.frame.multi_epiparameter","text":"","code":"db <- epiparameter_db() #> Returning 125 results that match the criteria (100 are parameterised). #> Use subset to filter by entry variables or single_epiparameter to return a single entry. #> To retrieve the citation for each use the 'get_citation' function as.data.frame(db) #> disease pathogen #> 1 Adenovirus Adenovirus #> 2 Human Coronavirus Human_Cov #> 3 SARS SARS-Cov-1 #> 4 Influenza Influenza-A #> 5 Influenza Influenza-A #> 6 Influenza Influenza-B #> 7 Measles Measles Virus #> 8 Parainfluenza Parainfluenza Virus #> 9 RSV RSV #> 10 Rhinovirus Rhinovirus #> 11 Influenza Influenza-A #> 12 Influenza Influenza-A #> 13 RSV RSV #> 14 RSV RSV #> 15 Influenza Influenza-A-H1N1 #> 16 Influenza Influenza-A-H1N1 #> 17 Influenza Influenza-A-H7N9 #> 18 Influenza Influenza-A-H7N9 #> 19 Influenza Influenza-A-H7N9 #> 20 Influenza Influenza-A-H7N9 #> 21 Influenza Influenza-A-H7N9 #> 22 Influenza Influenza-A-H1N1 #> 23 Influenza Influenza-A-H1N1Pdm #> 24 Influenza Influenza-A-H1N1Pdm #> 25 Influenza Influenza-A-H1N1 #> 26 Influenza Influenza-A-H1N1 #> 27 Marburg Virus Disease Marburg Virus #> 28 Marburg Virus Disease Marburg Virus #> 29 Marburg Virus Disease Marburg Virus #> 30 Marburg Virus Disease Marburg Virus #> 31 Marburg Virus Disease Marburg Virus #> 32 SARS SARS-Cov-1 #> 33 SARS SARS-Cov-1 #> 34 Smallpox Smallpox-Variola-Major #> 35 Smallpox Smallpox-Variola-Major #> 36 Smallpox Smallpox-Variola-Minor #> 37 Smallpox Smallpox-Variola-Minor #> 38 Mpox Monkeypox Virus #> 39 Pneumonic Plague Yersinia Pestis #> 40 Hantavirus Pulmonary Syndrome Hantavirus (Andes Virus) #> 41 Ebola Virus Disease Ebola Virus #> 42 Dengue Dengue Virus #> 43 Dengue Dengue Virus #> 44 Dengue Dengue Virus #> 45 Zika Virus Disease Zika Virus #> 46 Chikungunya Chikungunya Virus #> 47 Dengue Dengue Virus #> 48 Dengue Dengue Virus #> 49 Japanese Encephalitis Japanese Encephalitis Virus #> 50 Rift Valley Fever Rift Valley Fever Virus #> 51 West Nile Fever West Nile Virus #> 52 West Nile Fever West Nile Virus #> 53 West Nile Fever West Nile Virus #> 54 Yellow Fever Yellow Fever Viruses #> 55 Yellow Fever Yellow Fever Viruses #> 56 Mpox Mpox Virus #> 57 Mpox Mpox Virus #> 58 Mpox Mpox Virus #> 59 Mpox Mpox Virus #> 60 Mpox Mpox Virus #> 61 Mpox Mpox Virus #> 62 Mpox Mpox Virus #> 63 Ebola Virus Disease Ebola Virus-Zaire Subtype #> 64 Ebola Virus Disease Ebola Virus-Zaire Subtype #> 65 Ebola Virus Disease Ebola Virus #> 66 Ebola Virus Disease Ebola Virus #> 67 Ebola Virus Disease Ebola Virus #> 68 Ebola Virus Disease Ebola Virus #> 69 Ebola Virus Disease Ebola Virus #> 70 Ebola Virus Disease Ebola Virus #> 71 Ebola Virus Disease Ebola Virus #> 72 Ebola Virus Disease Ebola Virus #> 73 Ebola Virus Disease Ebola Virus #> 74 Ebola Virus Disease Ebola Virus #> 75 Ebola Virus Disease Ebola Virus #> 76 Ebola Virus Disease Ebola Virus #> 77 Ebola Virus Disease Ebola Virus #> 78 Ebola Virus Disease Ebola Virus #> 79 MERS MERS-Cov #> 80 MERS MERS-Cov #> 81 MERS MERS-Cov #> 82 MERS MERS-Cov #> 83 MERS MERS-Cov #> 84 MERS MERS-Cov #> 85 MERS MERS-Cov #> 86 MERS MERS-Cov #> 87 COVID-19 SARS-CoV-2 #> 88 COVID-19 SARS-CoV-2 #> 89 COVID-19 SARS-CoV-2 #> 90 COVID-19 SARS-CoV-2 #> 91 COVID-19 SARS-CoV-2 #> 92 COVID-19 SARS-CoV-2 #> 93 COVID-19 SARS-CoV-2 #> 94 COVID-19 SARS-CoV-2 #> 95 COVID-19 SARS-CoV-2 #> 96 COVID-19 SARS-CoV-2 #> 97 COVID-19 SARS-CoV-2 #> 98 COVID-19 SARS-CoV-2 #> 99 COVID-19 SARS-CoV-2 #> 100 COVID-19 SARS-CoV-2 #> 101 COVID-19 SARS-CoV-2 #> 102 COVID-19 SARS-CoV-2 #> 103 COVID-19 SARS-CoV-2 #> 104 COVID-19 SARS-CoV-2 #> 105 COVID-19 SARS-CoV-2 #> 106 COVID-19 SARS-CoV-2 #> 107 COVID-19 SARS-CoV-2 #> 108 COVID-19 SARS-CoV-2 #> 109 COVID-19 SARS-CoV-2 #> 110 COVID-19 SARS-CoV-2 #> 111 COVID-19 SARS-CoV-2 #> 112 COVID-19 SARS-CoV-2 #> 113 COVID-19 SARS-CoV-2 #> 114 Mpox Mpox Virus #> 115 Mpox Mpox Virus Clade I #> 116 Mpox Mpox Virus #> 117 Mpox Mpox Virus Clade I #> 118 Mpox Mpox Virus Clade IIa #> 119 Mpox Mpox Virus Clade IIb #> 120 Mpox Mpox Virus #> 121 Mpox Mpox Virus #> 122 Mpox Mpox Virus #> 123 Chikungunya Chikungunya Virus #> 124 Chikungunya Chikungunya Virus #> 125 Chikungunya Chikungunya Virus #> epi_name prob_distribution uncertainty summary_stats #> 1 incubation period lN(1.7, .... list(unc.... c(`25` =.... #> 2 incubation period lN(1.2, .... list(unc.... c(`25` =.... #> 3 incubation period lN(1.4, .... list(unc.... c(`5` = .... #> 4 incubation period lN(0.34,.... list(unc.... c(`5` = .... #> 5 incubation period lN(0.64,.... list(unc.... c(`5` = .... #> 6 incubation period lN(-0.51.... list(unc.... c(`5` = .... #> 7 incubation period lN(2.5, .... list(unc.... c(`5` = .... #> 8 incubation period lN(0.96,.... list(unc.... c(`25` =.... #> 9 incubation period lN(1.5, .... list(unc.... c(`5` = .... #> 10 incubation period lN(0.64,.... list(unc.... c(`5` = .... #> 11 incubation period lN(0.38,.... list(unc.... c(`5` = .... #> 12 incubation period lN(0.36,.... list(unc.... c(`5` = .... #> 13 incubation period lN(1.5, .... list(unc.... c(`5` = .... #> 14 incubation period lN(1.5, .... list(unc.... c(`5` = .... #> 15 incubation period Γ(3.3, 2) list(ci_.... c(`95` =.... #> 16 incubation period Weibull(.... list(ci_.... c(`95` =.... #> 17 incubation period Weibull(.... list(unc.... 3.4, c(3.... #> 18 incubation period Γ(1.9, 0.41) list(unc.... 4.5, c(2.... #> 19 incubation period weibull list(ci_.... 3.5, c(3.... #> 20 incubation period Weibull(.... list(ci_.... 3.7, c(3.... #> 21 incubation period Weibull(.... list(ci_.... 3.3, c(2.... #> 22 incubation period lnorm list(ci_.... 4.3, c(2.... #> 23 incubation period Γ(18, 8.5) list(unc.... 2.05, 0.49 #> 24 serial interval Γ(2.6, 1) list(unc.... 2.51, 1.55 #> 25 incubation period lN(0.34,.... list(unc.... c(`5` = .... #> 26 generation time Weibull(.... list(ci_.... c(`5` = .... #> 27 incubation period NA list(ci_.... 2, 26 #> 28 incubation period NA list(ci_.... 7, 2, 13 #> 29 serial interval NA list(ci_.... c(`25` =.... #> 30 onset to death NA list(ci_.... 8, 2, 16 #> 31 serial interval Γ(2.8, 0.31) list(unc.... 9, c(8.2.... #> 32 offspring distribution NB(0.16,.... list(ci_.... #> 33 offspring distribution NB(0.17,.... list(ci_.... #> 34 offspring distribution NB(0.37,.... list(ci_.... #> 35 offspring distribution NB(0.32,.... list(ci_.... #> 36 offspring distribution NB(0.65,.... list(ci_.... #> 37 offspring distribution NB(0.72,.... list(ci_.... #> 38 offspring distribution NB(0.58,.... list(ci_.... #> 39 offspring distribution NB(1.4, .... list(ci_.... #> 40 offspring distribution NB(1.7, 0.7) list(ci_.... #> 41 offspring distribution NB(5.1, .... list(ci_.... #> 42 incubation period lN(2.6, .... list(unc.... 15, c(10.... #> 43 incubation period lN(1.8, .... list(unc.... 6.5, c(4.... #> 44 incubation period lN(1.8, .... list(ci_.... 5.97, c(.... #> 45 incubation period lN(1.8, .... list(unc.... c(`5` = .... #> 46 incubation period lN(1.1, .... list(unc.... c(`25` =.... #> 47 incubation period lN(1.7, .... list(unc.... c(`25` =.... #> 48 incubation period lN(1.7, .... list(unc.... c(`25` =.... #> 49 incubation period lN(2.1, .... list(unc.... c(`25` =.... #> 50 incubation period lN(1.4, .... list(unc.... c(`25` =.... #> 51 incubation period lN(0.96,.... list(unc.... c(`5` = .... #> 52 incubation period lN(1.1, .... list(unc.... c(`25` =.... #> 53 incubation period lN(2.4, .... list(unc.... c(`25` =.... #> 54 incubation period lN(1.5, .... list(unc.... c(`5` = .... #> 55 incubation period lN(1.5, .... list(unc.... c(`5` = .... #> 56 incubation period lN(2.1, .... list(unc.... 9, c(6.6.... #> 57 incubation period lN(2, 0.055) list(unc.... 7.6, c(6.... #> 58 incubation period Γ(2.4, 0.27) list(ci_.... 9.1, c(6.... #> 59 incubation period lN(1.8, .... list(ci_.... 7.5, c(6.... #> 60 incubation period lN(1.5, .... list(ci_.... 5.6, c(4.... #> 61 serial interval Γ(2.9, 0.34) list(ci_.... 8.5, c(7.... #> 62 serial interval Γ(2.8, 0.4) list(ci_.... 7, c(5.8.... #> 63 incubation period lN(2.5, .... list(unc.... 12.7, 4.31 #> 64 onset to death Γ(2.4, 0.3) list(ci_.... 9.3, c(6.... #> 65 incubation period Γ(1.6, 0.15) list(unc.... 10.3, c(.... #> 66 incubation period Γ(0.93, .... list(unc.... 12.6, c(.... #> 67 incubation period Γ(1.7, 0.17) list(unc.... 10, c(9..... #> 68 incubation period Γ(1.5, 0.14) list(unc.... 10.4, c(.... #> 69 serial interval Γ(2.2, 0.15) list(unc.... 14.2, c(.... #> 70 serial interval Γ(4.9, 0.32) list(unc.... 15.5, c(.... #> 71 serial interval Γ(2.1, 0.14) list(unc.... 15.1, c(.... #> 72 serial interval Γ(1.9, 0.15) list(unc.... 12.4, c(.... #> 73 hospitalisation to death Γ(1.2, 0.27) list(unc.... 4.3, c(4.... #> 74 hospitalisation to discharge Γ(2.4, 0.22) list(unc.... 11.2, c(.... #> 75 notification to death Γ(0.49, .... list(unc.... 3.5, c(3.... #> 76 notification to discharge Γ(1.8, 0.16) list(unc.... 10.9, c(.... #> 77 onset to death Γ(1.6, 0.2) list(unc.... 8.2, c(7.... #> 78 onset to discharge Γ(2.9, 0.19) list(unc.... 15.1, c(.... #> 79 incubation period lN(1.7, .... list(unc.... c(`5` = .... #> 80 serial interval lN(2, 0.32) list(unc.... c(`5` = .... #> 81 onset to hospitalisation NA list(ci_.... 5, 1, 10 #> 82 onset to death NA list(ci_.... 11, 5, 27 #> 83 onset to ventilation NA list(ci_.... 7, 3, 11 #> 84 onset to death Γ(2, 0.13) list(unc.... 14.6, c(.... #> 85 incubation period gamma list(ci_.... 6.7, c(6.... #> 86 serial interval Γ(20, 1.6) list(unc.... 12.6, c(.... #> 87 incubation period NA list(ci_.... 5.84, c(.... #> 88 incubation period NA list(ci_.... 5.74, c(.... #> 89 incubation period NA list(ci_.... 6.5, c(5.... #> 90 serial interval NA list(ci_.... 5.2, c(4.... #> 91 serial interval lN(1.4, .... list(unc.... 4.7, c(3.... #> 92 serial interval Weibull(.... list(unc.... 4.8, c(3.... #> 93 incubation period Weibull(.... list(unc.... c(`2.5` .... #> 94 serial interval N(4.6, 19) list(ci_.... c(`95` =.... #> 95 incubation period NA list(ci_.... 6.38, c(.... #> 96 incubation period Weibull(.... list(unc.... 6.4, c(4.... #> 97 incubation period lN(1.7, .... list(ci_.... #> 98 incubation period lN(1.6, .... list(ci_.... 5.8, c(5.... #> 99 incubation period lN(1.5, .... list(unc.... 5, c(4.2.... #> 100 incubation period lN(1.6, .... list(unc.... 5.6, c(5.... #> 101 onset to hospitalisation Γ(0.62, .... list(unc.... 3.3, c(2.... #> 102 onset to hospitalisation Γ(2.3, 0.35) list(unc.... 6.5, c(5.... #> 103 onset to death lN(2.6, .... list(unc.... 14.5, c(.... #> 104 hospitalisation to death Weibull(.... list(unc.... 8.9, c(7.... #> 105 incubation period lN(1.5, 0.4) list(unc.... 5.6, c(4.... #> 106 onset to hospitalisation lN(0.95,.... list(unc.... 9.7, c(5.... #> 107 onset to hospitalisation lN(1.7, .... list(unc.... 6.6, c(5.... #> 108 onset to death lN(2.9, .... list(unc.... 20.2, c(.... #> 109 hospitalisation to death lN(2.2, .... list(unc.... 13, c(8..... #> 110 incubation period lN(1.6, .... list(unc.... 5.5, c(`.... #> 111 incubation period lN(1.7, .... list(unc.... c(`2.5` .... #> 112 incubation period lN(1.7, .... list(unc.... c(`2.5` .... #> 113 incubation period lN(1.6, .... list(unc.... c(`2.5` .... #> 114 serial interval Γ(14, 2.5) list(unc.... 5.6, c(1.... #> 115 serial interval NA list(ci_.... c(`25` =.... #> 116 serial interval NA list(ci_.... c(`25` =.... #> 117 incubation period NA list(ci_.... c(`25` =.... #> 118 incubation period NA list(ci_.... c(`25` =.... #> 119 incubation period NA list(ci_.... 8.26, c(.... #> 120 incubation period NA list(ci_.... 8.13, c(.... #> 121 incubation period NA list(ci_.... 8.08, c(.... #> 122 incubation period NA list(ci_.... 8.23, c(.... #> 123 generation time NA list(ci_.... 14, 6.2 #> 124 generation time Γ(8.6, 0.69) list(unc.... 12.4, c(.... #> 125 case fatality risk NA list(ci_.... 1.3 #> citation metadata method_assess #> 1 list(aut.... days, 14.... TRUE, FA.... #> 2 list(aut.... days, 13.... TRUE, FA.... #> 3 list(aut.... days, 15.... TRUE, FA.... #> 4 list(aut.... days, 15.... TRUE, FA.... #> 5 list(aut.... days, 90.... TRUE, FA.... #> 6 list(aut.... days, 78.... TRUE, FA.... #> 7 list(aut.... days, 55.... TRUE, FA.... #> 8 list(aut.... days, 11.... TRUE, FA.... #> 9 list(aut.... days, 24.... TRUE, FA.... #> 10 list(aut.... days, 28.... TRUE, FA.... #> 11 list(aut.... days, 15.... TRUE, FA.... #> 12 list(aut.... days, 15.... TRUE, FA.... #> 13 list(aut.... days, 24.... TRUE, FA.... #> 14 list(aut.... days, 24.... TRUE, FA.... #> 15 list(aut.... days, 72.... TRUE, FA.... #> 16 list(aut.... days, 72.... TRUE, FA.... #> 17 list(aut.... days, 22.... TRUE, FA.... #> 18 list(aut.... days, 22.... TRUE, FA.... #> 19 list(aut.... days, 39.... TRUE, FA.... #> 20 list(aut.... days, 17.... TRUE, FA.... #> 21 list(aut.... days, 22.... TRUE, FA.... #> 22 list(aut.... days, 31.... FALSE, F.... #> 23 list(aut.... days, 16.... TRUE, FA.... #> 24 list(aut.... days, 58.... TRUE, FA.... #> 25 list(aut.... days, 12.... TRUE, FA.... #> 26 list(aut.... days, 16.... TRUE, FA.... #> 27 list(aut.... days, 76.... FALSE, F.... #> 28 list(aut.... days, 18.... FALSE, F.... #> 29 list(aut.... days, 38.... FALSE, F.... #> 30 list(aut.... days, 77.... FALSE, F.... #> 31 list(aut.... days, 37.... FALSE, F.... #> 32 list(aut.... No units.... There is.... #> 33 list(aut.... No units.... There is.... #> 34 list(aut.... No units.... There is.... #> 35 list(aut.... No units.... There is.... #> 36 list(aut.... No units.... There is.... #> 37 list(aut.... No units.... There is.... #> 38 list(aut.... No units.... There is.... #> 39 list(aut.... No units.... There is.... #> 40 list(aut.... No units.... There is.... #> 41 list(aut.... No units.... There is.... #> 42 list(aut.... days, 14.... TRUE, FA.... #> 43 list(aut.... days, 14.... TRUE, FA.... #> 44 list(aut.... days, 15.... TRUE, FA.... #> 45 list(aut.... days, 25.... TRUE, FA.... #> 46 list(aut.... days, 21.... TRUE, FA.... #> 47 list(aut.... days, 16.... TRUE, FA.... #> 48 list(aut.... days, 12.... TRUE, FA.... #> 49 list(aut.... days, 6,.... TRUE, FA.... #> 50 list(aut.... days, 23.... TRUE, FA.... #> 51 list(aut.... days, 18.... TRUE, FA.... #> 52 list(aut.... days, 8,.... TRUE, FA.... #> 53 list(aut.... days, 6,.... TRUE, FA.... #> 54 list(aut.... days, 91.... TRUE, FA.... #> 55 list(aut.... days, 80.... TRUE, FA.... #> 56 list(aut.... days, 18.... FALSE, F.... #> 57 list(aut.... days, 22.... TRUE, FA.... #> 58 list(aut.... days, 30.... FALSE, F.... #> 59 list(aut.... days, 35.... FALSE, F.... #> 60 list(aut.... days, 36.... FALSE, F.... #> 61 list(aut.... days, 57.... FALSE, F.... #> 62 list(aut.... days, 40.... FALSE, F.... #> 63 list(aut.... days, 19.... FALSE, F.... #> 64 list(aut.... days, 14.... TRUE, FA.... #> 65 list(aut.... days, 17.... TRUE, FA.... #> 66 list(aut.... days, 49.... TRUE, FA.... #> 67 list(aut.... days, 95.... TRUE, FA.... #> 68 list(aut.... days, 79.... TRUE, FA.... #> 69 list(aut.... days, 30.... FALSE, F.... #> 70 list(aut.... days, 37.... FALSE, F.... #> 71 list(aut.... days, 14.... FALSE, F.... #> 72 list(aut.... days, 11.... FALSE, F.... #> 73 list(aut.... days, 11.... FALSE, F.... #> 74 list(aut.... days, 10.... FALSE, F.... #> 75 list(aut.... days, 25.... FALSE, F.... #> 76 list(aut.... days, 13.... FALSE, F.... #> 77 list(aut.... days, 27.... FALSE, F.... #> 78 list(aut.... days, 13.... FALSE, F.... #> 79 list(aut.... days, 23.... TRUE, FA.... #> 80 list(aut.... days, 23.... TRUE, FA.... #> 81 list(aut.... days, 23.... FALSE, F.... #> 82 list(aut.... days, 23.... FALSE, F.... #> 83 list(aut.... days, 23.... FALSE, F.... #> 84 list(aut.... days, 18.... FALSE, F.... #> 85 list(aut.... days, 16.... TRUE, FA.... #> 86 list(aut.... days, 99.... TRUE, FA.... #> 87 list(aut.... days, 59.... FALSE, F.... #> 88 list(aut.... days, 62.... FALSE, F.... #> 89 list(aut.... days, 14.... FALSE, F.... #> 90 list(aut.... days, 39.... FALSE, F.... #> 91 list(aut.... days, 28.... TRUE, TR.... #> 92 list(aut.... days, 18.... TRUE, TR.... #> 93 list(aut.... days, 17.... TRUE, FA.... #> 94 list(aut.... days, 13.... TRUE, FA.... #> 95 list(aut.... days, 28.... FALSE, F.... #> 96 list(aut.... days, 19.... TRUE, FA.... #> 97 list(aut.... days, 13.... FALSE, F.... #> 98 list(aut.... days, 12.... FALSE, F.... #> 99 list(aut.... days, 52.... TRUE, FA.... #> 100 list(aut.... days, 15.... TRUE, FA.... #> 101 list(aut.... days, 15.... TRUE, FA.... #> 102 list(aut.... days, 34.... TRUE, FA.... #> 103 list(aut.... days, 34.... TRUE, FA.... #> 104 list(aut.... days, 39.... TRUE, FA.... #> 105 list(aut.... days, 52.... TRUE, TR.... #> 106 list(aut.... days, 15.... TRUE, TR.... #> 107 list(aut.... days, 34.... TRUE, TR.... #> 108 list(aut.... days, 34.... TRUE, TR.... #> 109 list(aut.... days, 39.... TRUE, TR.... #> 110 list(aut.... days, 18.... TRUE, FA.... #> 111 list(aut.... days, 99.... TRUE, FA.... #> 112 list(aut.... days, 10.... TRUE, FA.... #> 113 list(aut.... days, 73.... TRUE, FA.... #> 114 list(aut.... days, 42.... FALSE, T.... #> 115 list(aut.... days, 16.... FALSE, F.... #> 116 list(aut.... days, 34.... FALSE, F.... #> 117 list(aut.... days, 16.... FALSE, F.... #> 118 list(aut.... days, 27.... FALSE, F.... #> 119 list(aut.... days, 11.... FALSE, F.... #> 120 list(aut.... days, NA.... FALSE, F.... #> 121 list(aut.... days, NA.... FALSE, F.... #> 122 list(aut.... days, NA.... FALSE, F.... #> 123 list(aut.... days, NA.... FALSE, F.... #> 124 list(aut.... days, 41.... FALSE, F.... #> 125 list(aut.... deaths p.... There is.... #> notes #> 1 Analysis on data from Commission on Acute Respiratory Disease. Experimental transmission of minor respiratory illness to human volunteers by filter-passing agents. I. Demonstration of two types of illness characterized by long and short incubation periods and diff erent clinical features. J Clin Invest 1947; 26: 957–82. #> 2 Analysis on data from Bradburne AF, Bynoe ML, Tyrrell DA. Eff ects of a “new” human respiratory virus in volunteers. Br Med J 1967; 3: 767–69. #> 3 Pooled analysis on several data sets, see Lessler et al. 2009 for references of datasets #> 4 Pooled analysis on several data sets, see Lessler et al. 2009 for references of datasets #> 5 These estimates for the incubation period of influenza A from Lessler et al. 2009 are different from the estimates from the complete data, as they remove Henle et al. 1945 J Immunol, as it is an outlier in the dataset (n=61). Values found at the bottom Table 3. #> 6 Pooled analysis on several data sets, see Lessler et al. 2009 for references of datasets #> 7 Pooled analysis on several data sets, see Lessler et al. 2009 for references of datasets #> 8 Pooled analysis on several data sets, see Lessler et al. 2009 for references of datasets #> 9 Pooled analysis on several data sets, see Lessler et al. 2009 for references of datasets #> 10 Pooled analysis on several data sets, see Lessler et al. 2009 for references of datasets #> 11 Data from Lessler et al 2009 using double interval-censored analysis. Not open source #> 12 Data from Lessler et al 2009 using single interval-censored analysis. Not open source #> 13 Data from Lessler et al 2009 using double interval-censored analysis. Not open source #> 14 Data from Lessler et al 2009 using single interval-censored analysis. Not open source #> 15 Gamma and weibull distributions had equally good fit to the data. This entry is the gamma distribution. Gamma, exponential. Not open source. #> 16 Gamma and weibull distributions had equally good fit to the data. This entry is the weibull distribution. Weibull, exponential #> 17 This study used an original data set and a modified data set. This weibull distribution was fitted to the modified data set and it is recommended to use this one. #> 18 This study used an original data set and a modified data set. This gamma distribution was fitted to the original data set and it is recommended to use the weibull distribution that was fitted to the modified data set. #> 19 This study fit the weibull distribution to estimate the parameters for the complete data set, those who had a fatal outcome and those with a non-fatal outcome. This is the distribution fit to the complete unpartitioned data. #> 20 This study fit the weibull distribution to estimate the parameters for the complete data set, those who had a fatal outcome and those with a non-fatal outcome. This is the distribution fit to the fatal outcome data. #> 21 This study fit the weibull distribution to estimate the parameters for the complete data set, those who had a fatal outcome and those with a non-fatal outcome. This is the distribution fit to the non-fatal outcome data. #> 22 The mid-point of the exposure time was used to approximate an exact exposure time instead of interval-censoring. This can lead to a possible bias (overestimation) in incubation times. It was ambiguously reported whether the mean is the mean of the distribution or the meanlog parameter of the lognormal distribution. #> 23 No additional notes #> 24 No additional notes #> 25 No additional notes. #> 26 The parameters of the weibull are stated without reporting the uncertainty around them. The parameter estimates and sample size is reported in the supplementary appendix. #> 27 This paper did not fit a distribution to the incubation period data and only reported a lower and upper range of the data. This is present in the database as there are no other studies that report the incubation period for Marburg virus. There is another incubation period reported from the same paper for a subset of the data which report the median and interquartile range but again do not fit a distribution to the data. #> 28 This paper did not fit a distribution to the incubation period data and only reported a median and range for a subset of the data. This is present in the database as there are no other studies that report the incubation period for Marburg virus. This paper also reports the maximum and minimum for the complete data set. #> 29 This paper did not fit a distribution to the serial interval data and only reported a median and interquartile range. This is present in the database as there are no other studies that report the serial interval for Marburg virus. #> 30 This paper reports the median and range of the symptom onset to death delay but did not fit a parametric distribution to the data. This is included in the database as it is the only reported symptom onset to death reported in the literature #> 31 The generation time is estimated from non-human viral load data. This paper reports the generation time but assumes the generation time and serial interval are the same it is classified as serial interval here based on Van Kerkove et al. 2015 <10.1038/sdata.2015.19>. The sample size is take from Van Kerkove et al. 2015. #> 32 Parameter estimates are retrieved from the supplementary tables. #> 33 No additional notes #> 34 No additional notes #> 35 No additional notes #> 36 No additional notes #> 37 Estimate of R0 taken from original study and CI of dispersion calculated mean of Z and proportion of zeros known #> 38 In the model comparison the geometric model was the better fit to the monkeypox data, however, only the parameters of the negative binomial were reported as so are stored in the database. #> 39 In the model comparison the geometric model was the better fit to the Pneumonic Plague data, however, only the parameters of the negative binomial were reported as so are stored in the database. #> 40 In the model comparison the geometric model was the better fit to the Hantavirus data, however, only the parameters of the negative binomial were reported as so are stored in the database. The uncertainty for the dispersion parameter is currently not stored in the database as the upper bound for the confidence interval is infinite, and currently infinite values are not supported. #> 41 In the model comparison the poisson model was the better fit to the Ebola data, however, only the parameters of the negative binomial were reported as so are stored in the database. The uncertainty for the dispersion parameter is currently not stored in the database as the upper bound for the confidence interval is infinite, and currently infinite values are not supported. #> 42 Extrinsic incubation period for data at 25 degrees celcius #> 43 Extrinsic incubation period for data at 30 degrees celcius #> 44 Standard deviation, meanlog and sdlog is taken from Siraj et al. 2017 <10.1371/journal.pntd.0005797> #> 45 Pooled analysis on several data sets, see Lessler et al. 2016 for references of datasets #> 46 Pooled analysis on several data sets, see Rudolph et al 2014 for references of datasets #> 47 Pooled analysis on several data sets, see Rudolph et al 2014 for references of datasets #> 48 Pooled analysis on several data sets, see Rudolph et al 2014 for references of datasets. This is a subset of data containing only mosquito-transmitted infections #> 49 Pooled analysis on several data sets, see Rudolph et al 2014 for references of datasets #> 50 Pooled analysis on several data sets, see Rudolph et al 2014 for references of datasets. Of the 18 samples at least 17 of them are not trasmitted by mosquitoes #> 51 Pooled analysis on several data sets, see Rudolph et al 2014 for references of datasets #> 52 Pooled analysis on several data sets, see Rudolph et al 2014 for references of datasets. This is a subset of data containing only mosquito-transmitted infections #> 53 Pooled analysis on several data sets, see Rudolph et al 2014 for references of datasets. This is a subset of data containing only tramsission by transplant or transfusion. #> 54 Pooled analysis on several data sets, see Rudolph et al 2014 for references of datasets #> 55 Pooled analysis on several data sets, see Rudolph et al 2014 for references of datasets. This is a subset of data containing only mosquito-transmitted infections #> 56 No additional notes #> 57 Uses the methods described by Lessler (10.2471/BLT.16.174540) and Reich (10.1002/sim.3659). Estimated from time from exposure to first symptom onset #> 58 No additional notes #> 59 Meanlog, sdlog, and fitted distribution from supplementary material. Uses cases from Charniga 2022 + extra cases. Incubation period as exposure to rash onset. #> 60 Meanlog, sdlog, and fitted distribution from supplementary material. Uses cases from Charniga 2022 + extra cases. Incubation period as exposure to symptom onset. #> 61 Shape and scale from supp. material. Serial interval as exposure to symptom onset #> 62 Shape and scale from supp. material. Serial interval as exposure to rash onset #> 63 The paper reports lower and upper supported ranges for the mean and standard deviation but it is not clear if these are confidence intervals or not so are not included in the database #> 64 Data extracted from Appendix. The mean, sd, shape and scale are taken from the paper, the conversion between the two does not match exactly. The data used to estimate the onset-to-death distribution is not from the DRC outbreak but from the west african outbreak. #> 65 Data extracted from Appendix. This data comes from the entire period of the west africa ebola outbreak up to the point the paper was published (Dec 2013 - 25 Nov 2014). The methods fitting are reported in another paper: DOI: 10.1056/NEJMoa1411100 #> 66 This data comes from the entire period of the Guinea ebola outbreak up to the point the paper was published (Dec 2013 - 25 Nov 2014). The methods fitting are reported in another paper: DOI: 10.1056/NEJMoa1411100 #> 67 This data comes from the entire period of the Liberia ebola outbreak up to the point the paper was published (Dec 2013 - 25 Nov 2014). The methods fitting are reported in another paper: DOI: 10.1056/NEJMoa1411100 #> 68 This data comes from the entire period of the Seirra Leone ebola outbreak up to the point the paper was published (Dec 2013 - 25 Nov 2014). The methods fitting are reported in another paper: DOI: 10.1056/NEJMoa1411100 #> 69 This data comes from the entire period of the west africa ebola outbreak up to the point the paper was published (Dec 2013 - 25 Nov 2014). The methods fitting are reported in another paper: DOI: 10.1056/NEJMoa1411100 #> 70 This data comes from the entire period of the Guinea ebola outbreak up to the point the paper was published (Dec 2013 - 25 Nov 2014). The methods fitting are reported in another paper: DOI: 10.1056/NEJMoa1411100 #> 71 This data comes from the entire period of the Liberia ebola outbreak up to the point the paper was published (Dec 2013 - 25 Nov 2014). The methods fitting are reported in another paper: DOI: 10.1056/NEJMoa1411100 #> 72 This data comes from the entire period of the Sierra Leone ebola outbreak up to the point the paper was published (Dec 2013 - 25 Nov 2014). The methods fitting are reported in another paper: DOI: 10.1056/NEJMoa1411100 #> 73 This data comes from the entire period of the west africa ebola outbreak up to the point the paper was published (Dec 2013 - 25 Nov 2014). The methods fitting are reported in another paper: DOI: 10.1056/NEJMoa1411100 #> 74 This data comes from the entire period of the west africa ebola outbreak up to the point the paper was published (Dec 2013 - 25 Nov 2014). The methods fitting are reported in another paper: DOI: 10.1056/NEJMoa1411100 #> 75 This data comes from the entire period of the west africa ebola outbreak up to the point the paper was published (Dec 2013 - 25 Nov 2014). The methods fitting are reported in another paper: DOI: 10.1056/NEJMoa1411100 #> 76 This data comes from the entire period of the west africa ebola outbreak up to the point the paper was published (Dec 2013 - 25 Nov 2014). The methods fitting are reported in another paper: DOI: 10.1056/NEJMoa1411100 #> 77 This data comes from the entire period of the west africa ebola outbreak up to the point the paper was published (Dec 2013 - 25 Nov 2014). The methods fitting are reported in another paper: DOI: 10.1056/NEJMoa1411100 #> 78 This data comes from the entire period of the west africa ebola outbreak up to the point the paper was published (Dec 2013 - 25 Nov 2014). The methods fitting are reported in another paper: DOI: 10.1056/NEJMoa1411100 #> 79 The sample size is not explicitly stated. The number of confirmed cases is 23 and there are 2 suspected cases, therefore it is not clear whether the 2 suspected cases were included in the estimation, the sample size is assumed to be 23. #> 80 The sample size is not explicitly stated. The number of confirmed cases is 23 and there are 2 suspected cases, therefore it is not clear whether the 2 suspected cases were included in the estimation, the sample size is assumed to be 23. #> 81 A distribution was not fitted to the data, instead the median and range observed are reported. The sample size is not explicitly stated. The number of confirmed cases is 23 and there are 2 suspected cases, therefore it is not clear whether the 2 suspected cases were included in the estimation, the sample size is assumed to be 23. #> 82 A distribution was not fitted to the data, instead the median and range observed are reported. The sample size is not explicitly stated. The number of confirmed cases is 23 and there are 2 suspected cases, therefore it is not clear whether the 2 suspected cases were included in the estimation, the sample size is assumed to be 23. #> 83 A distribution was not fitted to the data, instead the median and range observed are reported. The sample size is not explicitly stated. The number of confirmed cases is 23 and there are 2 suspected cases, therefore it is not clear whether the 2 suspected cases were included in the estimation, the sample size is assumed to be 23. #> 84 The distribution parameters were jointly inferred with the risk factors of mortality. #> 85 No additional notes #> 86 No additional notes #> 87 The estimate of the incubation period is from a non-parametric bootstrap approach that does not fit a parametric distribution. #> 88 This estimated mean incubation period is from a meta-analysis of 15 other incubation period estimates. Only the mean is reported and a distribution cannot be specified as the meta-mean is estimated from a random-effects model. #> 89 This estimated mean incubation period is from a meta-analysis of 14 other incubation period estimates. Only the mean is reported and a distribution cannot be specified as the meta-mean is estimated from a random-effects model. #> 90 This estimated mean serial interval is from a meta-analysis of 23 other serial interval estimates. Only the mean is reported and a distribution cannot be specified as the meta-mean is estimated from a random-effects model. #> 91 These estimates are from fitting to the entire dataset of contact pairs, including pairs that are uncertain. #> 92 These estimates are from fitting to a subset of the dataset of contact pairs, only including pairs that are the most certain. #> 93 No additional notes. #> 94 No additional notes. #> 95 This estimated mean incubation period is from a meta-analysis of 99 other incubation period estimates. Only the mean is reported and a distribution cannot be specified as the meta-mean is estimated from a random-effects model. #> 96 No additional notes #> 97 The incubation period parameters are estimated from a meta-analysis of other studies that estimated the incubation period using a lognormal distribution. This is the full set of data (N=9). #> 98 The incubation period parameters are estimated from a meta-analysis of other studies that estimated the incubation period using a lognormal distribution. This is the data set with Backer removed as they did not have a defined exposure window (N=8). #> 99 This dataset excludes Wuhan residents (to have a more precise exposure interval). This method does not apply right-truncation, but does compare the gamma, weibull and lognormal distributions. #> 100 This dataset includes Wuhan residents (which have a less precise exposure interval). This method does not apply right-truncation, but does compare the gamma, weibull and lognormal distributions. #> 101 This method does not apply right-truncation, but does compare the gamma, weibull and lognormal distributions. #> 102 This method does not apply right-truncation, but does compare the gamma, weibull and lognormal distributions. #> 103 This method does not apply right-truncation, but does compare the gamma, weibull and lognormal distributions. #> 104 This method does not apply right-truncation, but does compare the gamma, weibull and lognormal distributions. #> 105 This is excluding Wuhan residents from the dataset as this provides a more precise exposure interval. This method applies right-truncation but only fits a lognormal distribution. #> 106 This dataset includes only surviving patients. This method applies right-truncation but only fits a lognormal distribution. #> 107 This dataset includes only deceased patients. This method applies right-truncation but only fits a lognormal distribution. #> 108 This method applies right-truncation but only fits a lognormal distribution. #> 109 This method applies right-truncation but only fits a lognormal distribution. #> 110 This is the complete data set. #> 111 This is a subset of the data, including only those cases with a known onset of fever to be sure that the onset of symptoms is not from another pathogen. #> 112 This is a subset of the data, including only cases that are detected outside of mainland China. #> 113 This is a subset of the data, including only cases that are detected inside mainland China. #> 114 Data from Kraemer et al 10.1016/S1473-3099(22)00359-0 #> 115 Systematic review #> 116 Systematic review #> 117 Systematic review #> 118 Systematic review #> 119 Systematic review #> 120 SEIR model from 10.1016/j.mbs.2008.06.005 where the IP is assumed to follow a gamma distribution #> 121 SEIR model from 10.1016/j.mbs.2008.06.005 where the IP is assumed to follow a gamma distribution #> 122 SEIR model from 10.1016/j.mbs.2008.06.005 where the IP is assumed to follow a gamma distribution #> 123 Database entry per communication K. Charniga, Z. Cucunubá & Laura Gomez Bermeo. #> 124 No additional notes. #> 125 Case fatality risk is given in deaths per 1,000 cases. It was calculated as a cumulative case fatality ratio. CFR is a population-wide estimate. Odds of chikungunya-related death were not significantly different between males and females. Odds of chikungunya-related death were significantly higher for 55-74 years old, and >75 years old compared to <18."},{"path":"https://epiverse-trace.github.io/epiparameter/reference/as.function.epiparameter.html","id":null,"dir":"Reference","previous_headings":"","what":"as.function() method for <epiparameter> class — as.function.epiparameter","title":"as.function() method for <epiparameter> class — as.function.epiparameter","text":"Converts <epiparameter> object distribution function (see epiparameter_distribution_functions), either probability density/mass function, (density), cumulative distribution function (cdf), random number generator (generate), quantile (quantile).","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/as.function.epiparameter.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"as.function() method for <epiparameter> class — as.function.epiparameter","text":"","code":"# S3 method for class 'epiparameter' as.function(x, func_type = c(\"density\", \"cdf\", \"generate\", \"quantile\"), ...)"},{"path":"https://epiverse-trace.github.io/epiparameter/reference/as.function.epiparameter.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"as.function() method for <epiparameter> class — as.function.epiparameter","text":"x <epiparameter> object. func_type single character string specifying distribution convert <epiparameter> object . Default \"density\". options \"cdf\", \"generate\", \"quantile\". ... dots Extra arguments passed method.","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/as.function.epiparameter.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"as.function() method for <epiparameter> class — as.function.epiparameter","text":"function object.","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/as.function.epiparameter.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"as.function() method for <epiparameter> class — as.function.epiparameter","text":"function returned takes single required argument x.","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/as.function.epiparameter.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"as.function() method for <epiparameter> class — as.function.epiparameter","text":"","code":"ep <- epiparameter_db(single_epiparameter = TRUE) #> Using Linton N, Kobayashi T, Yang Y, Hayashi K, Akhmetzhanov A, Jung S, Yuan #> B, Kinoshita R, Nishiura H (2020). “Incubation Period and Other #> Epidemiological Characteristics of 2019 Novel Coronavirus Infections #> with Right Truncation: A Statistical Analysis of Publicly Available #> Case Data.” _Journal of Clinical Medicine_. doi:10.3390/jcm9020538 #> <https://doi.org/10.3390/jcm9020538>.. #> To retrieve the citation use the 'get_citation' function # by default it will convert to a density function f <- as.function(ep) # use function f(10) #> [1] 0.01732193 f <- as.function(ep, func_type = \"cdf\") f(10) #> [1] 0.7975232"},{"path":"https://epiverse-trace.github.io/epiparameter/reference/as_epiparameter.data.frame.html","id":null,"dir":"Reference","previous_headings":"","what":"Convert <data.frame> to an <epiparameter> object — as_epiparameter.data.frame","title":"Convert <data.frame> to an <epiparameter> object — as_epiparameter.data.frame","text":"Convert tabular information <data.frame> <epiparameter>. information <data.frame> converted <epiparameter> function error.","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/as_epiparameter.data.frame.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Convert <data.frame> to an <epiparameter> object — as_epiparameter.data.frame","text":"","code":"# S3 method for class 'data.frame' as_epiparameter(x, ...)"},{"path":"https://epiverse-trace.github.io/epiparameter/reference/as_epiparameter.data.frame.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Convert <data.frame> to an <epiparameter> object — as_epiparameter.data.frame","text":"x <data.frame>. ... dots used, extra arguments supplied cause warning.","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/as_epiparameter.data.frame.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Convert <data.frame> to an <epiparameter> object — as_epiparameter.data.frame","text":"<epiparameter> object list <epiparameter> objects.","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/as_epiparameter.data.frame.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Convert <data.frame> to an <epiparameter> object — as_epiparameter.data.frame","text":"","code":"ep <- epiparameter_db(single_epiparameter = TRUE) #> Using Linton N, Kobayashi T, Yang Y, Hayashi K, Akhmetzhanov A, Jung S, Yuan #> B, Kinoshita R, Nishiura H (2020). “Incubation Period and Other #> Epidemiological Characteristics of 2019 Novel Coronavirus Infections #> with Right Truncation: A Statistical Analysis of Publicly Available #> Case Data.” _Journal of Clinical Medicine_. doi:10.3390/jcm9020538 #> <https://doi.org/10.3390/jcm9020538>.. #> To retrieve the citation use the 'get_citation' function df <- as.data.frame(ep) ep <- as_epiparameter(df)"},{"path":"https://epiverse-trace.github.io/epiparameter/reference/as_epiparameter.html","id":null,"dir":"Reference","previous_headings":"","what":"Convert to an <epiparameter> object — as_epiparameter","title":"Convert to an <epiparameter> object — as_epiparameter","text":"Convert R object <epiparameter> object. conversion possible function error.","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/as_epiparameter.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Convert to an <epiparameter> object — as_epiparameter","text":"","code":"as_epiparameter(x, ...)"},{"path":"https://epiverse-trace.github.io/epiparameter/reference/as_epiparameter.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Convert to an <epiparameter> object — as_epiparameter","text":"x object used select method. ... dots Extra arguments passed method.","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/as_epiparameter.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Convert to an <epiparameter> object — as_epiparameter","text":"<epiparameter> object list <epiparameter> objects.","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/as_epiparameter.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Convert to an <epiparameter> object — as_epiparameter","text":"create full citation information article table epireview package corresponding entry need passed function via ... argument. argument called article, matched name $. specify probability distribution pass character string function via ... argument. argument called prob_distribution. example, specify gamma distribution: as_epiparameter(x, prob_distribution = \"gamma\"). Warning: distributions specified via prob_dist argument overwrite probability distribution specified x argument. example, probability distribution given epireview entry prob_dist argument specified function may error return unparameterised <epiparameter> parameterisation becomes incompatible. Valid probability distributions : \"gamma\", \"lnorm\", \"weibull\", \"nbinom\", \"geom\", \"pois\", \"norm\", \"exp\".","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/assert_epiparameter.html","id":null,"dir":"Reference","previous_headings":"","what":"Assert an object is a valid <epiparameter> object — assert_epiparameter","title":"Assert an object is a valid <epiparameter> object — assert_epiparameter","text":"Assert object valid <epiparameter> object","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/assert_epiparameter.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Assert an object is a valid <epiparameter> object — assert_epiparameter","text":"","code":"assert_epiparameter(x)"},{"path":"https://epiverse-trace.github.io/epiparameter/reference/assert_epiparameter.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Assert an object is a valid <epiparameter> object — assert_epiparameter","text":"x R object.","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/assert_epiparameter.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Assert an object is a valid <epiparameter> object — assert_epiparameter","text":"Invisibly returns <epiparameter>. Called side-effects (errors invalid <epiparameter> object provided).","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/assert_epiparameter.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Assert an object is a valid <epiparameter> object — assert_epiparameter","text":"","code":"ep <- epiparameter_db(single_epiparameter = TRUE) #> Using Linton N, Kobayashi T, Yang Y, Hayashi K, Akhmetzhanov A, Jung S, Yuan #> B, Kinoshita R, Nishiura H (2020). “Incubation Period and Other #> Epidemiological Characteristics of 2019 Novel Coronavirus Infections #> with Right Truncation: A Statistical Analysis of Publicly Available #> Case Data.” _Journal of Clinical Medicine_. doi:10.3390/jcm9020538 #> <https://doi.org/10.3390/jcm9020538>.. #> To retrieve the citation use the 'get_citation' function assert_epiparameter(ep) # example with invalid <epiparameter> ep$disease <- NULL try(assert_epiparameter(ep)) #> Error : <epiparameter> is invalid due to: #> - <epiparameter> must contain $disease. #> - <epiparameter> must contain one disease. #>"},{"path":"https://epiverse-trace.github.io/epiparameter/reference/c.epiparameter.html","id":null,"dir":"Reference","previous_headings":"","what":"c() method for <epiparameter> class — c.epiparameter","title":"c() method for <epiparameter> class — c.epiparameter","text":"c() method <epiparameter> class","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/c.epiparameter.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"c() method for <epiparameter> class — c.epiparameter","text":"","code":"# S3 method for class 'epiparameter' c(...) # S3 method for class 'multi_epiparameter' c(...)"},{"path":"https://epiverse-trace.github.io/epiparameter/reference/c.epiparameter.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"c() method for <epiparameter> class — c.epiparameter","text":"... dots Objects concatenated.","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/c.epiparameter.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"c() method for <epiparameter> class — c.epiparameter","text":"<epiparameter> list <epiparameter> objects.","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/c.epiparameter.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"c() method for <epiparameter> class — c.epiparameter","text":"","code":"db <- epiparameter_db() #> Returning 125 results that match the criteria (100 are parameterised). #> Use subset to filter by entry variables or single_epiparameter to return a single entry. #> To retrieve the citation for each use the 'get_citation' function # combine two <epiparameter> objects into a list c(db[[1]], db[[2]]) #> # List of 2 <epiparameter> objects #> Number of diseases: 2 #> ❯ Adenovirus ❯ Human Coronavirus #> Number of epi parameters: 1 #> ❯ incubation period #> [[1]] #> Disease: Adenovirus #> Pathogen: Adenovirus #> Epi Parameter: incubation period #> Study: Lessler J, Reich N, Brookmeyer R, Perl T, Nelson K, Cummings D (2009). #> “Incubation periods of acute respiratory viral infections: a systematic #> review.” _The Lancet Infectious Diseases_. #> doi:10.1016/S1473-3099(09)70069-6 #> <https://doi.org/10.1016/S1473-3099%2809%2970069-6>. #> Distribution: lnorm (days) #> Parameters: #> meanlog: 1.723 #> sdlog: 0.231 #> #> [[2]] #> Disease: Human Coronavirus #> Pathogen: Human_Cov #> Epi Parameter: incubation period #> Study: Lessler J, Reich N, Brookmeyer R, Perl T, Nelson K, Cummings D (2009). #> “Incubation periods of acute respiratory viral infections: a systematic #> review.” _The Lancet Infectious Diseases_. #> doi:10.1016/S1473-3099(09)70069-7 #> <https://doi.org/10.1016/S1473-3099%2809%2970069-7>. #> Distribution: lnorm (days) #> Parameters: #> meanlog: 1.163 #> sdlog: 0.140 #> #> # ℹ Use `parameter_tbl()` to see a summary table of the parameters. #> # ℹ Explore database online at: https://epiverse-trace.github.io/epiparameter/articles/database.html # combine a list of <epiparameter> objects and a single <epiparameter> object c(db, db[[1]]) #> # List of 126 <epiparameter> objects #> Number of diseases: 23 #> ❯ Adenovirus ❯ COVID-19 ❯ Chikungunya ❯ Dengue ❯ Ebola Virus Disease ❯ Hantavirus Pulmonary Syndrome ❯ Human Coronavirus ❯ Influenza ❯ Japanese Encephalitis ❯ MERS ❯ Marburg Virus Disease ❯ Measles ❯ Mpox ❯ Parainfluenza ❯ Pneumonic Plague ❯ RSV ❯ Rhinovirus ❯ Rift Valley Fever ❯ SARS ❯ Smallpox ❯ West Nile Fever ❯ Yellow Fever ❯ Zika Virus Disease #> Number of epi parameters: 13 #> ❯ case fatality risk ❯ generation time ❯ hospitalisation to death ❯ hospitalisation to discharge ❯ incubation period ❯ notification to death ❯ notification to discharge ❯ offspring distribution ❯ onset to death ❯ onset to discharge ❯ onset to hospitalisation ❯ onset to ventilation ❯ serial interval #> [[1]] #> Disease: Adenovirus #> Pathogen: Adenovirus #> Epi Parameter: incubation period #> Study: Lessler J, Reich N, Brookmeyer R, Perl T, Nelson K, Cummings D (2009). #> “Incubation periods of acute respiratory viral infections: a systematic #> review.” _The Lancet Infectious Diseases_. #> doi:10.1016/S1473-3099(09)70069-6 #> <https://doi.org/10.1016/S1473-3099%2809%2970069-6>. #> Distribution: lnorm (days) #> Parameters: #> meanlog: 1.723 #> sdlog: 0.231 #> #> [[2]] #> Disease: Human Coronavirus #> Pathogen: Human_Cov #> Epi Parameter: incubation period #> Study: Lessler J, Reich N, Brookmeyer R, Perl T, Nelson K, Cummings D (2009). #> “Incubation periods of acute respiratory viral infections: a systematic #> review.” _The Lancet Infectious Diseases_. #> doi:10.1016/S1473-3099(09)70069-7 #> <https://doi.org/10.1016/S1473-3099%2809%2970069-7>. #> Distribution: lnorm (days) #> Parameters: #> meanlog: 1.163 #> sdlog: 0.140 #> #> [[3]] #> Disease: SARS #> Pathogen: SARS-Cov-1 #> Epi Parameter: incubation period #> Study: Lessler J, Reich N, Brookmeyer R, Perl T, Nelson K, Cummings D (2009). #> “Incubation periods of acute respiratory viral infections: a systematic #> review.” _The Lancet Infectious Diseases_. #> doi:10.1016/S1473-3099(09)70069-8 #> <https://doi.org/10.1016/S1473-3099%2809%2970069-8>. #> Distribution: lnorm (days) #> Parameters: #> meanlog: 1.386 #> sdlog: 0.593 #> #> # ℹ 123 more elements #> # ℹ Use `print(n = ...)` to see more elements. #> # ℹ Use `parameter_tbl()` to see a summary table of the parameters. #> # ℹ Explore database online at: https://epiverse-trace.github.io/epiparameter/articles/database.html"},{"path":"https://epiverse-trace.github.io/epiparameter/reference/calc_disc_dist_quantile.html","id":null,"dir":"Reference","previous_headings":"","what":"Calculate the quantiles of a probability distribution based on the vector of probabilities and time data (e.g. time since infection) — calc_disc_dist_quantile","title":"Calculate the quantiles of a probability distribution based on the vector of probabilities and time data (e.g. time since infection) — calc_disc_dist_quantile","text":"function can used cases data fitted distribution openly available summary statistics distribution reported data scraped plot quantiles needed order use extract_param() function.","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/calc_disc_dist_quantile.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculate the quantiles of a probability distribution based on the vector of probabilities and time data (e.g. time since infection) — calc_disc_dist_quantile","text":"","code":"calc_disc_dist_quantile(prob, days, quantile)"},{"path":"https://epiverse-trace.github.io/epiparameter/reference/calc_disc_dist_quantile.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Calculate the quantiles of a probability distribution based on the vector of probabilities and time data (e.g. time since infection) — calc_disc_dist_quantile","text":"prob numeric vector probabilities. days numeric vector days. quantile single numeric vector numerics specifying quantiles extract distribution.","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/calc_disc_dist_quantile.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Calculate the quantiles of a probability distribution based on the vector of probabilities and time data (e.g. time since infection) — calc_disc_dist_quantile","text":"named vector quantiles.","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/calc_disc_dist_quantile.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Calculate the quantiles of a probability distribution based on the vector of probabilities and time data (e.g. time since infection) — calc_disc_dist_quantile","text":"","code":"prob <- dgamma(seq(0, 10, length.out = 21), shape = 2, scale = 2) days <- seq(0, 10, 0.5) quantiles <- c(0.025, 0.975) calc_disc_dist_quantile(prob = prob, days = days, quantile = quantiles) #> 0.025 0.975 #> 0 9"},{"path":"https://epiverse-trace.github.io/epiparameter/reference/convert_params_to_summary_stats.html","id":null,"dir":"Reference","previous_headings":"","what":"Convert the parameter(s) of a distribution to summary statistics — convert_params_to_summary_stats","title":"Convert the parameter(s) of a distribution to summary statistics — convert_params_to_summary_stats","text":"Convert parameters range distributions number summary statistics. summary statistics calculated analytically given parameters.","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/convert_params_to_summary_stats.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Convert the parameter(s) of a distribution to summary statistics — convert_params_to_summary_stats","text":"","code":"convert_params_to_summary_stats(x, ...) # S3 method for class 'character' convert_params_to_summary_stats( x = c(\"lnorm\", \"gamma\", \"weibull\", \"nbinom\", \"geom\"), ... ) # S3 method for class 'epiparameter' convert_params_to_summary_stats(x, ...)"},{"path":"https://epiverse-trace.github.io/epiparameter/reference/convert_params_to_summary_stats.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Convert the parameter(s) of a distribution to summary statistics — convert_params_to_summary_stats","text":"x R object. ... <dynamic-dots> Numeric named parameter(s) used convert summary statistics. example meanlog sdlog parameters lognormal (lnorm) distribution.","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/convert_params_to_summary_stats.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Convert the parameter(s) of a distribution to summary statistics — convert_params_to_summary_stats","text":"list eight elements including: mean, median, mode, variance (var), standard deviation (sd), coefficient variation (cv), skewness, excess kurtosis (ex_kurtosis).","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/convert_params_to_summary_stats.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Convert the parameter(s) of a distribution to summary statistics — convert_params_to_summary_stats","text":"distribution names parameter names follow style distributions R, example lognormal distribution lnorm, parameters meanlog sdlog.","code":""},{"path":[]},{"path":"https://epiverse-trace.github.io/epiparameter/reference/convert_params_to_summary_stats.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Convert the parameter(s) of a distribution to summary statistics — convert_params_to_summary_stats","text":"","code":"# example using characters convert_params_to_summary_stats(\"lnorm\", meanlog = 1, sdlog = 2) #> $mean #> [1] 20.08554 #> #> $median #> [1] 2.718282 #> #> $mode #> [1] 0.04978707 #> #> $var #> [1] 21623.04 #> #> $sd #> [1] 147.0477 #> #> $cv #> [1] 7.321076 #> #> $skewness #> [1] 414.3593 #> #> $ex_kurtosis #> [1] 9220557 #> convert_params_to_summary_stats(\"gamma\", shape = 1, scale = 1) #> $mean #> [1] 1 #> #> $median #> [1] 0.6931472 #> #> $mode #> [1] 0 #> #> $var #> [1] 1 #> #> $sd #> [1] 1 #> #> $cv #> [1] 1 #> #> $skewness #> [1] 2 #> #> $ex_kurtosis #> [1] 6 #> convert_params_to_summary_stats(\"nbinom\", prob = 0.5, dispersion = 2) #> $mean #> [1] 2 #> #> $median #> [1] 1 #> #> $mode #> [1] 1 #> #> $var #> [1] 4 #> #> $sd #> [1] 2 #> #> $cv #> [1] 1 #> #> $skewness #> [1] 1.5 #> #> $ex_kurtosis #> [1] 4 #> # example using <epiparameter> epiparameter <- epiparameter_db(single_epiparameter = TRUE) #> Using Linton N, Kobayashi T, Yang Y, Hayashi K, Akhmetzhanov A, Jung S, Yuan #> B, Kinoshita R, Nishiura H (2020). “Incubation Period and Other #> Epidemiological Characteristics of 2019 Novel Coronavirus Infections #> with Right Truncation: A Statistical Analysis of Publicly Available #> Case Data.” _Journal of Clinical Medicine_. doi:10.3390/jcm9020538 #> <https://doi.org/10.3390/jcm9020538>.. #> To retrieve the citation use the 'get_citation' function convert_params_to_summary_stats(epiparameter) #> $mean #> [1] 9.7 #> #> $median #> [1] 2.576957 #> #> $mode #> [1] 0.1818772 #> #> $var #> [1] 1239.04 #> #> $sd #> [1] 35.2 #> #> $cv #> [1] 3.628866 #> #> $skewness #> [1] 58.67393 #> #> $ex_kurtosis #> [1] 46586.04 #> # example using <epiparameter> and specifying parameters epiparameter <- epiparameter_db( disease = \"Influenza\", author = \"Virlogeux\", subset = prob_dist == \"weibull\" ) #> Returning 4 results that match the criteria (3 are parameterised). #> Use subset to filter by entry variables or single_epiparameter to return a single entry. #> To retrieve the citation for each use the 'get_citation' function convert_params_to_summary_stats(epiparameter[[2]], shape = 1, scale = 1) #> $mean #> [1] 1 #> #> $median #> [1] 0.6931472 #> #> $mode #> [1] 0 #> #> $var #> [1] 1 #> #> $sd #> [1] 1 #> #> $cv #> [1] 1 #> #> $skewness #> [1] 2 #> #> $ex_kurtosis #> [1] 6 #>"},{"path":"https://epiverse-trace.github.io/epiparameter/reference/convert_summary_stats_to_params.html","id":null,"dir":"Reference","previous_headings":"","what":"Convert the summary statistics of a distribution to parameters — convert_summary_stats_to_params","title":"Convert the summary statistics of a distribution to parameters — convert_summary_stats_to_params","text":"Convert summary statistics range distributions distribution's parameters. summary statistics calculated analytically given parameters. exception Weibull distribution uses root finding numerical method.","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/convert_summary_stats_to_params.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Convert the summary statistics of a distribution to parameters — convert_summary_stats_to_params","text":"","code":"convert_summary_stats_to_params(x, ...) # S3 method for class 'character' convert_summary_stats_to_params( x = c(\"lnorm\", \"gamma\", \"weibull\", \"nbinom\", \"geom\"), ... ) # S3 method for class 'epiparameter' convert_summary_stats_to_params(x, ...)"},{"path":"https://epiverse-trace.github.io/epiparameter/reference/convert_summary_stats_to_params.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Convert the summary statistics of a distribution to parameters — convert_summary_stats_to_params","text":"x R object. ... <dynamic-dots> Numeric named summary statistics used convert parameter(s). example mean sd summary statistics lognormal (lnorm) distribution.","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/convert_summary_stats_to_params.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Convert the summary statistics of a distribution to parameters — convert_summary_stats_to_params","text":"list either one two elements (depending many parameters distribution ).","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/convert_summary_stats_to_params.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Convert the summary statistics of a distribution to parameters — convert_summary_stats_to_params","text":"Summary statistics named accordingly (case-sensitive): mean: mean median: median mode: mode variance: var standard deviation: sd coefficient variation: cv skewness: skewness excess kurtosis: ex_kurtosis Note: combinations summary statistics can converted distribution parameters. case function error stating parameters calculated given input. distribution names parameter names follow style distributions R, example lognormal distribution lnorm, parameters meanlog sdlog.","code":""},{"path":[]},{"path":"https://epiverse-trace.github.io/epiparameter/reference/convert_summary_stats_to_params.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Convert the summary statistics of a distribution to parameters — convert_summary_stats_to_params","text":"","code":"# examples using characters convert_summary_stats_to_params(\"lnorm\", mean = 1, sd = 1) #> $meanlog #> [1] -0.3465736 #> #> $sdlog #> [1] 0.8325546 #> convert_summary_stats_to_params(\"weibull\", mean = 2, var = 2) #> Numerical approximation used, results may be unreliable. #> $shape #> [1] 1.435521 #> #> $scale #> [1] 2.202641 #> convert_summary_stats_to_params(\"geom\", mean = 2) #> $prob #> [1] 0.3333333 #> # examples using <epiparameter> epiparameter <- epiparameter_db(single_epiparameter = TRUE) #> Using Linton N, Kobayashi T, Yang Y, Hayashi K, Akhmetzhanov A, Jung S, Yuan #> B, Kinoshita R, Nishiura H (2020). “Incubation Period and Other #> Epidemiological Characteristics of 2019 Novel Coronavirus Infections #> with Right Truncation: A Statistical Analysis of Publicly Available #> Case Data.” _Journal of Clinical Medicine_. doi:10.3390/jcm9020538 #> <https://doi.org/10.3390/jcm9020538>.. #> To retrieve the citation use the 'get_citation' function convert_summary_stats_to_params(epiparameter) #> $meanlog #> [1] 0.9466094 #> #> $sdlog #> [1] 1.628199 #> # example using <epiparameter> and specifying summary stats epiparameter$summary_stats <- list() convert_summary_stats_to_params(epiparameter, mean = 10, sd = 2) #> $meanlog #> [1] 2.282975 #> #> $sdlog #> [1] 0.1980422 #>"},{"path":"https://epiverse-trace.github.io/epiparameter/reference/create_citation.html","id":null,"dir":"Reference","previous_headings":"","what":"Create a citation for an <epiparameter> object — create_citation","title":"Create a citation for an <epiparameter> object — create_citation","text":"helper function creating <epiparameter> object create citation list sensible defaults, type checking arguments help remember citation information accepted list.","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/create_citation.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Create a citation for an <epiparameter> object — create_citation","text":"","code":"create_citation( author = utils::person(), year = NA_integer_, title = NA_character_, journal = NA_character_, doi = NA_character_, pmid = NA_integer_ )"},{"path":"https://epiverse-trace.github.io/epiparameter/reference/create_citation.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Create a citation for an <epiparameter> object — create_citation","text":"author Either <person>, character string, vector list characters case multiple authors. Specify full name (\"<given name>\" \"<family name>\"). using characters make sure name can converted <person> (see .person()). Use white space separation names. Multiple names can stored within single <person> (see person()). year numeric year publication. title character string title article published epidemiological parameters. journal character string name journal published article published epidemiological parameters. can also pre-print server, e.g., medRxiv. doi character string Digital Object Identifier (DOI) assigned papers unique paper. pmid character string PubMed unique identifier number (PMID) assigned papers give unique identifier within PubMed.","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/create_citation.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Create a citation for an <epiparameter> object — create_citation","text":"<bibentry> object citation","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/create_citation.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Create a citation for an <epiparameter> object — create_citation","text":"function acts wrapper around bibentry() create citations sources reporting epidemiological parameters.","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/create_citation.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Create a citation for an <epiparameter> object — create_citation","text":"","code":"create_citation( author = person(given = \"John\", family = \"Smith\"), year = 2002, title = \"COVID-19 incubation period\", journal = \"Epi Journal\", doi = \"10.19832/j.1366-9516.2012.09147.x\" ) #> Using Smith J (2002). “COVID-19 incubation period.” _Epi Journal_. #> doi:10.19832/j.1366-9516.2012.09147.x #> <https://doi.org/10.19832/j.1366-9516.2012.09147.x>. #> To retrieve the citation use the 'get_citation' function #> Smith J (2002). “COVID-19 incubation period.” _Epi Journal_. #> doi:10.19832/j.1366-9516.2012.09147.x #> <https://doi.org/10.19832/j.1366-9516.2012.09147.x>."},{"path":"https://epiverse-trace.github.io/epiparameter/reference/create_metadata.html","id":null,"dir":"Reference","previous_headings":"","what":"Specify metadata associated with data set — create_metadata","title":"Specify metadata associated with data set — create_metadata","text":"helper function creating <epiparameter> object create metadata list sensible defaults, type checking arguments help remember metadata list structure (element names).","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/create_metadata.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Specify metadata associated with data set — create_metadata","text":"","code":"create_metadata( units = NA_character_, sample_size = NA_integer_, region = NA_character_, transmission_mode = NA_character_, vector = NA_character_, extrinsic = FALSE, inference_method = NA_character_ )"},{"path":"https://epiverse-trace.github.io/epiparameter/reference/create_metadata.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Specify metadata associated with data set — create_metadata","text":"units character units epidemiological parameter. sample_size sample data used fit delay distribution. usually number people data primary possibly secondary event interest. cases sample size stated NA can used. region geographical location data collected. can either given sub-national, national, continental. Multiple nested regions can given comma separated. region specified NA can given. transmission_mode character string specifying pathogen transmitted. information used determine whether epidemiological parameters vector-borne disease (.e. transmitted humans intermediate vector), specified transmission_mode = \"vector_borne\". vector name vector transmitting vector-borne disease. can common name, latin binomial name specific vector species. common name taxonomic name can given one given parentheses. disease vector-borne NA given. extrinsic boolean value defining whether data entry extrinsic delay distribution, extrinsic incubation period. field required intrinsic extrinsic delay distributions stored separate entries database can linked. disease vector-borne FALSE given. See Details explanation extrinsic distribution. inference_method type inference used fit delay distribution data. Abbreviations model fitting techniques can specified long non-ambiguous. field used determine whether uncertainty intervals possibly specified fields : confidence intervals (case maximum likelihood), credible intervals (case bayesian inference). Uncertainty bounds another types inference methods, inference method unstated assumed confidence intervals. inference method unknown disease probability distribution NA can given.","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/create_metadata.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Specify metadata associated with data set — create_metadata","text":"named list containing information sample size study, geography, whether disease vector-borne whether intrinsic extrinsic distribution well method distribution parameter estimation.","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/create_metadata.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Specify metadata associated with data set — create_metadata","text":"vector-borne diseases transmissibility disease dependent time taken host (.e. human) become infectious, also time takes vector become infectious. Therefore, extrinsic delay, vector infected yet infectious can role spread disease.","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/create_metadata.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Specify metadata associated with data set — create_metadata","text":"","code":"# it will automatically populate the fields with defaults if left empty create_metadata() #> $units #> [1] NA #> #> $sample_size #> [1] NA #> #> $region #> [1] NA #> #> $transmission_mode #> [1] NA #> #> $vector #> [1] NA #> #> $extrinsic #> [1] FALSE #> #> $inference_method #> [1] NA #> # supplying each field create_metadata( units = \"days\", sample_size = 10, region = \"UK\", transmission_mode = \"vector_borne\", vector = \"mosquito\", extrinsic = FALSE, inference_method = \"MLE\" ) #> $units #> [1] \"days\" #> #> $sample_size #> [1] 10 #> #> $region #> [1] \"UK\" #> #> $transmission_mode #> [1] \"vector_borne\" #> #> $vector #> [1] \"mosquito\" #> #> $extrinsic #> [1] FALSE #> #> $inference_method #> [1] \"MLE\" #>"},{"path":"https://epiverse-trace.github.io/epiparameter/reference/create_method_assess.html","id":null,"dir":"Reference","previous_headings":"","what":"Specify methodological aspects of distribution fitting — create_method_assess","title":"Specify methodological aspects of distribution fitting — create_method_assess","text":"helper function creating <epiparameter> object create method assessment list sensible defaults, type checking arguments help remember method assessments can accepted list.","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/create_method_assess.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Specify methodological aspects of distribution fitting — create_method_assess","text":"","code":"create_method_assess( censored = NA, right_truncated = NA, phase_bias_adjusted = NA )"},{"path":"https://epiverse-trace.github.io/epiparameter/reference/create_method_assess.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Specify methodological aspects of distribution fitting — create_method_assess","text":"censored boolean logical whether study used single double interval censoring methods infer delay distribution right_truncated boolean logical whether study used right- truncation methods infer delay distribution phase_bias_adjusted boolean logical whether study adjusted phase bias methods infer delay distribution","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/create_method_assess.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Specify methodological aspects of distribution fitting — create_method_assess","text":"named list three elements","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/create_method_assess.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Specify methodological aspects of distribution fitting — create_method_assess","text":"Currently, method assessment focuses common methodological aspects delay distributions (e.g. incubation period, serial interval, etc.), currently take account methodological aspects may important fitting offspring distributions data disease (super)spreading.","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/create_method_assess.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Specify methodological aspects of distribution fitting — create_method_assess","text":"","code":"create_method_assess( censored = FALSE, right_truncated = FALSE, phase_bias_adjusted = FALSE ) #> $censored #> [1] FALSE #> #> $right_truncated #> [1] FALSE #> #> $phase_bias_adjusted #> [1] FALSE #>"},{"path":"https://epiverse-trace.github.io/epiparameter/reference/create_prob_distribution.html","id":null,"dir":"Reference","previous_headings":"","what":"Create a distribution object — create_prob_distribution","title":"Create a distribution object — create_prob_distribution","text":"Creates S3 class holding distribution parameters probability distribution name, parameters distribution truncation discretisation. class holding distribution depends whether discretised distribution. continuous discrete distributions S3 classes distributional package used, discretised continuous distributions S3 class distcrete package used. details properties distribution classes respective package see documentation (either ?distributional ?distcrete)","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/create_prob_distribution.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Create a distribution object — create_prob_distribution","text":"","code":"create_prob_distribution( prob_distribution, prob_distribution_params, discretise = FALSE, truncation = NA, ... )"},{"path":"https://epiverse-trace.github.io/epiparameter/reference/create_prob_distribution.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Create a distribution object — create_prob_distribution","text":"prob_distribution character string specifying probability distribution. match R naming convention probability distributions (e.g. lognormal lnorm, negative binomial nbinom, geometric geom). prob_distribution_params named vector probability distribution parameters. discretise boolean logical whether distribution discretised. Default FALSE assumes continuous probability distribution. truncation numeric specifying truncation point inferred distribution truncated, NA unknown. ... dots Extra arguments passed distributional distcrete functions construct S3 distribution objects. see arguments can adjusted discretised distributions see distcrete::distcrete(), distributions see ?distributional documentation find specific distribution constructor function, e.g. Gamma distribution see distributional::dist_gamma().","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/create_prob_distribution.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Create a distribution object — create_prob_distribution","text":"S3 class containing probability distribution character string parameters probability distribution unknown.","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/create_prob_distribution.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Create a distribution object — create_prob_distribution","text":"Truncation enabled continuous distributions truncation implemented distcrete. default discretisation continuous distributions uses discretisation interval (interval) 1. unit distribution days, discretised day. endpoint weighting (w) discretisation 1. w can [0,1]. information please see distcrete::distcrete().","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/create_prob_distribution.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Create a distribution object — create_prob_distribution","text":"","code":"# example with continuous distribution without truncation create_prob_distribution( prob_distribution = \"gamma\", prob_distribution_params = c(shape = 1, scale = 1), discretise = FALSE, truncation = NA ) #> <distribution[1]> #> [1] Γ(1, 1) # example with continuous distribution with truncation create_prob_distribution( prob_distribution = \"gamma\", prob_distribution_params = c(shape = 1, scale = 1), discretise = FALSE, truncation = 10 ) #> <distribution[1]> #> [1] Γ(1, 1)[-Inf,10] # example with discrete distribution create_prob_distribution( prob_distribution = \"gamma\", prob_distribution_params = c(shape = 1, scale = 1), discretise = TRUE, truncation = NA ) #> A discrete distribution #> name: gamma #> parameters: #> shape: 1 #> scale: 1 # example passing extra arguments to distcrete create_prob_distribution( prob_distribution = \"gamma\", prob_distribution_params = c(shape = 1, scale = 1), discretise = TRUE, truncation = NA, w = 0.5 ) #> A discrete distribution #> name: gamma #> parameters: #> shape: 1 #> scale: 1"},{"path":"https://epiverse-trace.github.io/epiparameter/reference/create_region.html","id":null,"dir":"Reference","previous_headings":"","what":"Specify the geography of the data entry — create_region","title":"Specify the geography of the data entry — create_region","text":"geography data set can single geographical region either continent, country, region city level. specifying level geography fields may deduced.","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/create_region.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Specify the geography of the data entry — create_region","text":"","code":"create_region( continent = NA_character_, country = NA_character_, region = NA_character_, city = NA_character_ )"},{"path":"https://epiverse-trace.github.io/epiparameter/reference/create_region.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Specify the geography of the data entry — create_region","text":"continent character string specifying continent. country character string specifying country. region character string specifying region. city character string specifying city.","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/create_region.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Specify the geography of the data entry — create_region","text":"named list.","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/create_region.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Specify the geography of the data entry — create_region","text":"","code":"create_region(country = \"UK\") #> $continent #> [1] NA #> #> $country #> [1] \"UK\" #> #> $region #> [1] NA #> #> $city #> [1] NA #>"},{"path":"https://epiverse-trace.github.io/epiparameter/reference/create_summary_stats.html","id":null,"dir":"Reference","previous_headings":"","what":"Specify reported summary statistics — create_summary_stats","title":"Specify reported summary statistics — create_summary_stats","text":"helper function creating <epiparameter> object create summary statistics list sensible defaults, type checking arguments help remember summary statistics can accepted list.","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/create_summary_stats.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Specify reported summary statistics — create_summary_stats","text":"","code":"create_summary_stats( mean = NA_real_, mean_ci_limits = c(NA_real_, NA_real_), mean_ci = NA_real_, sd = NA_real_, sd_ci_limits = c(NA_real_, NA_real_), sd_ci = NA_real_, median = NA_real_, median_ci_limits = c(NA_real_, NA_real_), median_ci = NA_real_, dispersion = NA_real_, dispersion_ci_limits = c(NA_real_, NA_real_), dispersion_ci = NA_real_, lower_range = NA_real_, upper_range = NA_real_, quantiles = NA_real_ )"},{"path":"https://epiverse-trace.github.io/epiparameter/reference/create_summary_stats.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Specify reported summary statistics — create_summary_stats","text":"mean numeric mean (expectation) probability distribution. mean_ci_limits numeric vector length two confidence interval around mean. mean_ci numeric specifying confidence interval width, e.g. 95 95% CI sd numeric standard deviation probability distribution. sd_ci_limits numeric vector length 2 confidence interval around standard deviation. sd_ci numeric specifying confidence interval width, e.g. 95 95% confidence interval. median numeric median probability distribution. median_ci_limits numeric vector length two confidence interval around median. median_ci numeric specifying confidence interval width median. dispersion numeric dispersion probability distribution. Important dispersion probability distributions usually parameterised dispersion parameter, example lognormal distribution. probability distribution usually parameterised dispersion parameter, e.g. negative binomial distribution, considered parameter summary statistic go prob_distribution argument constructing <epiparameter> object epiparameter() (see create_prob_distribution()). dispersion_ci_limits numeric vector length 2 confidence interval around dispersion. dispersion_ci numeric specifying confidence interval width, e.g. 95 95% confidence interval. lower_range lower range data, used infer parameters distribution provided. upper_range upper range data, used infer parameters distribution provided. quantiles numeric vector quantiles distribution. quantiles provided default empty vector 2.5th, 5th, 25th, 75th, 95th, 97.5th quantiles supplied.","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/create_summary_stats.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Specify reported summary statistics — create_summary_stats","text":"list summary statistics. output list element names equal function arguments:","code":"$mean $mean_ci_limits $mean_ci $sd $sd_ci_limits $sd_ci $median $median_ci_limits $median_ci $dispersion $dispersion_ci_limits $dispersion_ci $lower_range $upper_range $quantiles"},{"path":"https://epiverse-trace.github.io/epiparameter/reference/create_summary_stats.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Specify reported summary statistics — create_summary_stats","text":"","code":"# mean and standard deviation create_summary_stats(mean = 5, sd = 2) #> $mean #> [1] 5 #> #> $mean_ci_limits #> [1] NA NA #> #> $mean_ci #> [1] NA #> #> $sd #> [1] 2 #> #> $sd_ci_limits #> [1] NA NA #> #> $sd_ci #> [1] NA #> #> $median #> [1] NA #> #> $median_ci_limits #> [1] NA NA #> #> $median_ci #> [1] NA #> #> $dispersion #> [1] NA #> #> $dispersion_ci_limits #> [1] NA NA #> #> $dispersion_ci #> [1] NA #> #> $quantiles #> [1] NA #> #> $range #> [1] NA NA #> # mean and standard deviation with uncertainty create_summary_stats( mean = 4, mean_ci_limits = c(2.1, 5.7), mean_ci = 95, sd = 0.7, sd_ci_limits = c(0.3, 1.1), sd_ci = 95 ) #> $mean #> [1] 4 #> #> $mean_ci_limits #> [1] 2.1 5.7 #> #> $mean_ci #> [1] 95 #> #> $sd #> [1] 0.7 #> #> $sd_ci_limits #> [1] 0.3 1.1 #> #> $sd_ci #> [1] 95 #> #> $median #> [1] NA #> #> $median_ci_limits #> [1] NA NA #> #> $median_ci #> [1] NA #> #> $dispersion #> [1] NA #> #> $dispersion_ci_limits #> [1] NA NA #> #> $dispersion_ci #> [1] NA #> #> $quantiles #> [1] NA #> #> $range #> [1] NA NA #> # median and range create_summary_stats( median = 5, lower_range = 1, upper_range = 13 ) #> $mean #> [1] NA #> #> $mean_ci_limits #> [1] NA NA #> #> $mean_ci #> [1] NA #> #> $sd #> [1] NA #> #> $sd_ci_limits #> [1] NA NA #> #> $sd_ci #> [1] NA #> #> $median #> [1] 5 #> #> $median_ci_limits #> [1] NA NA #> #> $median_ci #> [1] NA #> #> $dispersion #> [1] NA #> #> $dispersion_ci_limits #> [1] NA NA #> #> $dispersion_ci #> [1] NA #> #> $quantiles #> [1] NA #> #> $range #> [1] 1 13 #>"},{"path":"https://epiverse-trace.github.io/epiparameter/reference/create_uncertainty.html","id":null,"dir":"Reference","previous_headings":"","what":"Specify distribution parameter uncertainty — create_uncertainty","title":"Specify distribution parameter uncertainty — create_uncertainty","text":"helper function creating uncertainty parameters distribution <epiparameter> object.","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/create_uncertainty.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Specify distribution parameter uncertainty — create_uncertainty","text":"","code":"create_uncertainty(ci_limits = NA_real_, ci, ci_type)"},{"path":"https://epiverse-trace.github.io/epiparameter/reference/create_uncertainty.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Specify distribution parameter uncertainty — create_uncertainty","text":"ci_limits numeric vector length two lower upper bound confidence interval credible interval. ci numeric specifying interval ci, e.g. 95 95% ci. ci_type character string, either \"confidence interval\" \"credible interval\".","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/create_uncertainty.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Specify distribution parameter uncertainty — create_uncertainty","text":"List three elements: $ci_limits upper lower bounds CI (either confidence interval credible interval) (.e. two element numeric vector). $ci interval (e.g. 95 95% CI) given single numeric. $ci_type character string specifying type uncertainty (can either \"confidence interval\" \"credible interval\").","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/create_uncertainty.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Specify distribution parameter uncertainty — create_uncertainty","text":"","code":"# example with uncertainty for a single parameter create_uncertainty( ci_limits = c(1, 3), ci = 95, ci_type = \"confidence interval\" ) #> $ci_limits #> [1] 1 3 #> #> $ci #> [1] 95 #> #> $ci_type #> [1] \"confidence interval\" #> # example for multiple parameters # lengh of list should match number of parameters list( shape = create_uncertainty( ci_limits = c(1, 3), ci = 95, ci_type = \"confidence interval\" ), scale = create_uncertainty( ci_limits = c(2, 4), ci = 95, ci_type = \"confidence interval\" ) ) #> $shape #> $shape$ci_limits #> [1] 1 3 #> #> $shape$ci #> [1] 95 #> #> $shape$ci_type #> [1] \"confidence interval\" #> #> #> $scale #> $scale$ci_limits #> [1] 2 4 #> #> $scale$ci #> [1] 95 #> #> $scale$ci_type #> [1] \"confidence interval\" #> #> # example with unknown uncertainty # the function can be called without arguments create_uncertainty() #> $ci_limits #> [1] NA #> #> $ci #> [1] NA NA #> #> $ci_type #> [1] NA #> # or give NA as the first argument create_uncertainty(NA) #> $ci_limits #> [1] NA #> #> $ci #> [1] NA NA #> #> $ci_type #> [1] NA #>"},{"path":"https://epiverse-trace.github.io/epiparameter/reference/discretise.html","id":null,"dir":"Reference","previous_headings":"","what":"Discretises a continuous distribution in an <epiparameter> object — discretise","title":"Discretises a continuous distribution in an <epiparameter> object — discretise","text":"Discretises continuous distribution <epiparameter> object","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/discretise.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Discretises a continuous distribution in an <epiparameter> object — discretise","text":"","code":"discretise(x, ...) discretize(x, ...) # S3 method for class 'epiparameter' discretise(x, ...) # Default S3 method discretise(x, ...)"},{"path":"https://epiverse-trace.github.io/epiparameter/reference/discretise.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Discretises a continuous distribution in an <epiparameter> object — discretise","text":"x <epiparameter> object. ... dots Extra arguments passed method.","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/discretise.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Discretises a continuous distribution in an <epiparameter> object — discretise","text":"<epiparameter> object.","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/discretise.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Discretises a continuous distribution in an <epiparameter> object — discretise","text":"Converts S3 distribution object <epiparameter> continuous (using object {distributional} package) discretised distribution (using object {distcrete} package).","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/discretise.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Discretises a continuous distribution in an <epiparameter> object — discretise","text":"","code":"ebola_incubation <- epiparameter( disease = \"ebola\", epi_name = \"incubation_period\", prob_distribution = create_prob_distribution( prob_distribution = \"gamma\", prob_distribution_params = c(shape = 1, scale = 1) ) ) #> Citation cannot be created as author, year, journal or title is missing discretise(ebola_incubation) #> Disease: ebola #> Pathogen: NA #> Epi Parameter: incubation period #> Study: (????). “No citation.” #> Distribution: discrete gamma (NA) #> Parameters: #> shape: 1.000 #> scale: 1.000"},{"path":"https://epiverse-trace.github.io/epiparameter/reference/dot-calc_dist_params.html","id":null,"dir":"Reference","previous_headings":"","what":"Calculate the parameters of a probability distribution from a list of summary statistics — .calc_dist_params","title":"Calculate the parameters of a probability distribution from a list of summary statistics — .calc_dist_params","text":"parameters probability distribution provided (e.g. describing distribution literature) instead summary statistics distribution provided, parameters can usually calculated summary statistics. function can provide convenient wrapper around convert_summary_stats_to_params() extract_param() known summary statistics can used calculate parameters distribution.","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/dot-calc_dist_params.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculate the parameters of a probability distribution from a list of summary statistics — .calc_dist_params","text":"","code":".calc_dist_params(prob_distribution, summary_stats, sample_size)"},{"path":"https://epiverse-trace.github.io/epiparameter/reference/dot-calc_dist_params.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Calculate the parameters of a probability distribution from a list of summary statistics — .calc_dist_params","text":"prob_distribution S3 class containing probability distribution character string parameters probability distribution unknown name distribution known, NA distribution name parameters unknown. Use create_prob_distribution() create prob_distribution. summary_stats list summary statistics, use create_summary_stats() create list. list can include summary statistics inferred distribution mean standard deviation, quantiles distribution, information data used fit distribution lower upper range. summary statistics can also include uncertainty around metrics confidence interval around mean standard deviation. sample_size sample size data. needed falling back using median-range extraction calculation.","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/dot-calc_dist_params.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Calculate the parameters of a probability distribution from a list of summary statistics — .calc_dist_params","text":"named numeric vector parameters.","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/dot-calc_dist_params.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Calculate the parameters of a probability distribution from a list of summary statistics — .calc_dist_params","text":"hierarchy methods : Conversion prioritised mean standard deviation available mostly analytical conversions (except one Weibull conversions). Next method possible extraction percentiles. method requires lower percentile ((0-50]) upper percentile ((50-100)). multiple percentiles ranges provided lowest value used calculation. last method extraction using median range data.","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/dot-check_optim_conv.html","id":null,"dir":"Reference","previous_headings":"","what":"Check whether the optimisation of distribution parameters has converged to stable value for the parameters and function output for multiple iterations — .check_optim_conv","title":"Check whether the optimisation of distribution parameters has converged to stable value for the parameters and function output for multiple iterations — .check_optim_conv","text":"function try prevent optimisation local optimum thus checks whether multiple optimisation routines consistently finding parameter values within set tolerance.","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/dot-check_optim_conv.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Check whether the optimisation of distribution parameters has converged to stable value for the parameters and function output for multiple iterations — .check_optim_conv","text":"","code":".check_optim_conv(optim_params_list, optim_params, tolerance)"},{"path":"https://epiverse-trace.github.io/epiparameter/reference/dot-check_optim_conv.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Check whether the optimisation of distribution parameters has converged to stable value for the parameters and function output for multiple iterations — .check_optim_conv","text":"optim_params_list list, element output stats::optim(). See ?optim details. optim_params list given output stats::optim(). tolerance numeric specifying within disparity convergence parameter estimates function minimisation accepted.","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/dot-check_optim_conv.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Check whether the optimisation of distribution parameters has converged to stable value for the parameters and function output for multiple iterations — .check_optim_conv","text":"Boolean","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/dot-citet.html","id":null,"dir":"Reference","previous_headings":"","what":"Format short citation from <bibentry> object — .citet","title":"Format short citation from <bibentry> object — .citet","text":"Output equivalent \\citet{} function natbib LaTeX package.","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/dot-citet.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Format short citation from <bibentry> object — .citet","text":"","code":".citet(x)"},{"path":"https://epiverse-trace.github.io/epiparameter/reference/dot-citet.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Format short citation from <bibentry> object — .citet","text":"x <bibentry> object, see bibentry().","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/dot-citet.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Format short citation from <bibentry> object — .citet","text":"character string short citation.","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/dot-clean_params.html","id":null,"dir":"Reference","previous_headings":"","what":"Standardise distribution parameters — .clean_params","title":"Standardise distribution parameters — .clean_params","text":".clean_params() dispatches distribution specific parameter cleaning function depending prob_dist. example prob_dist = \"gamma\" call .clean_params_gamma().","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/dot-clean_params.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Standardise distribution parameters — .clean_params","text":"","code":".clean_params(prob_distribution, prob_distribution_params) .clean_params_gamma(prob_dist_params) .clean_params_lnorm(prob_dist_params) .clean_params_nbinom(prob_dist_params) .clean_params_geom(prob_dist_params) .clean_params_pois(prob_dist_params) .clean_params_norm(prob_dist_params) .clean_params_exp(prob_dist_params)"},{"path":"https://epiverse-trace.github.io/epiparameter/reference/dot-clean_params.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Standardise distribution parameters — .clean_params","text":"prob_distribution character string specifying probability distribution. match R naming convention probability distributions (e.g. lognormal lnorm, negative binomial nbinom, geometric geom). prob_distribution_params named vector probability distribution parameters.","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/dot-clean_params.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Standardise distribution parameters — .clean_params","text":"Named numeric vector parameters.","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/dot-clean_params.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Standardise distribution parameters — .clean_params","text":"Calling is_epiparameter_params() start .clean_params() ensures parameterisation incorrect error early dispatch distribution specific cleaning functions (e.g. .clean_params_gamma()). means distribution specific parameter cleaning functions need check error incorrect parameterisation.","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/dot-clean_string.html","id":null,"dir":"Reference","previous_headings":"","what":"Standardise the variables input by users — .clean_string","title":"Standardise the variables input by users — .clean_string","text":"Checks user supplied character string converts epiparameter standards: lower-case whitespace instead dashes underscores. Examples strings needing cleaned : disease pathogen names, epidemiological distributions.","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/dot-clean_string.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Standardise the variables input by users — .clean_string","text":"","code":".clean_string(x)"},{"path":"https://epiverse-trace.github.io/epiparameter/reference/dot-clean_string.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Standardise the variables input by users — .clean_string","text":"x character string.","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/dot-clean_string.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Standardise the variables input by users — .clean_string","text":"character vector equal length input.","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/dot-clean_uncertainty.html","id":null,"dir":"Reference","previous_headings":"","what":"Standardise distribution parameter uncertainty — .clean_uncertainty","title":"Standardise distribution parameter uncertainty — .clean_uncertainty","text":"Standardise distribution parameter uncertainty","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/dot-clean_uncertainty.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Standardise distribution parameter uncertainty — .clean_uncertainty","text":"","code":".clean_uncertainty(x, prob_distribution, uncertainty_missing)"},{"path":"https://epiverse-trace.github.io/epiparameter/reference/dot-clean_uncertainty.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Standardise distribution parameter uncertainty — .clean_uncertainty","text":"x <epiparameter> object. prob_distribution S3 class containing probability distribution character string parameters probability distribution unknown name distribution known, NA distribution name parameters unknown. Use create_prob_distribution() create prob_distribution. uncertainty_missing boolean logical whether uncertainty missing (see missing()) parent function.","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/dot-clean_uncertainty.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Standardise distribution parameter uncertainty — .clean_uncertainty","text":"uncertainty list <epiparameter> object.","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/dot-convert_params_gamma.html","id":null,"dir":"Reference","previous_headings":"","what":"Convert parameters of the gamma distribution to summary statistics — .convert_params_gamma","title":"Convert parameters of the gamma distribution to summary statistics — .convert_params_gamma","text":"Convert shape scale parameters gamma distribution number summary statistics can calculated analytically given gamma parameters. One exception median calculated using qgamma() analytical form available.","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/dot-convert_params_gamma.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Convert parameters of the gamma distribution to summary statistics — .convert_params_gamma","text":"","code":".convert_params_gamma(...)"},{"path":"https://epiverse-trace.github.io/epiparameter/reference/dot-convert_params_gamma.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Convert parameters of the gamma distribution to summary statistics — .convert_params_gamma","text":"... <dynamic-dots> Numeric named parameter(s) used convert summary statistics. example meanlog sdlog parameters lognormal (lnorm) distribution.","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/dot-convert_params_gamma.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Convert parameters of the gamma distribution to summary statistics — .convert_params_gamma","text":"list eight elements including: mean, median, mode, variance (var), standard deviation (sd), coefficient variation (cv), skewness, excess kurtosis (ex_kurtosis).","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/dot-convert_params_geom.html","id":null,"dir":"Reference","previous_headings":"","what":"Convert parameter of the geometric distribution to summary statistics — .convert_params_geom","title":"Convert parameter of the geometric distribution to summary statistics — .convert_params_geom","text":"Convert probability (prob) geometric distribution number summary statistics can calculated analytically given geometric parameter. One exception median calculated using stats::qgeom() analytical form always unique.","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/dot-convert_params_geom.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Convert parameter of the geometric distribution to summary statistics — .convert_params_geom","text":"","code":".convert_params_geom(...)"},{"path":"https://epiverse-trace.github.io/epiparameter/reference/dot-convert_params_geom.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Convert parameter of the geometric distribution to summary statistics — .convert_params_geom","text":"... <dynamic-dots> Numeric named parameter(s) used convert summary statistics. example meanlog sdlog parameters lognormal (lnorm) distribution.","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/dot-convert_params_geom.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Convert parameter of the geometric distribution to summary statistics — .convert_params_geom","text":"list eight elements including: mean, median, mode, variance (var), standard deviation (sd), coefficient variation (cv), skewness, excess kurtosis (ex_kurtosis).","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/dot-convert_params_geom.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Convert parameter of the geometric distribution to summary statistics — .convert_params_geom","text":"conversion function assumes distribution represents number failures first success (supported zero). form used base R distributional::dist_geometric().","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/dot-convert_params_lnorm.html","id":null,"dir":"Reference","previous_headings":"","what":"Converts the parameters of the lognormal distribution to summary statistics — .convert_params_lnorm","title":"Converts the parameters of the lognormal distribution to summary statistics — .convert_params_lnorm","text":"Converts meanlog sdlog parameters lognormal distribution number summary statistics can calculated analytically given lognormal parameters.","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/dot-convert_params_lnorm.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Converts the parameters of the lognormal distribution to summary statistics — .convert_params_lnorm","text":"","code":".convert_params_lnorm(...)"},{"path":"https://epiverse-trace.github.io/epiparameter/reference/dot-convert_params_lnorm.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Converts the parameters of the lognormal distribution to summary statistics — .convert_params_lnorm","text":"... <dynamic-dots> Numeric named parameter(s) used convert summary statistics. example meanlog sdlog parameters lognormal (lnorm) distribution.","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/dot-convert_params_lnorm.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Converts the parameters of the lognormal distribution to summary statistics — .convert_params_lnorm","text":"list eight elements including: mean, median, mode, variance (var), standard deviation (sd), coefficient variation (cv), skewness, excess kurtosis (ex_kurtosis).","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/dot-convert_params_nbinom.html","id":null,"dir":"Reference","previous_headings":"","what":"Convert parameters of the negative binomial distribution to summary statistics — .convert_params_nbinom","title":"Convert parameters of the negative binomial distribution to summary statistics — .convert_params_nbinom","text":"Convert probability (prob) dispersion parameters negative binomial distribution number summary statistics can calculated analytically given negative binomial parameters. One exception median calculated using qnbinom() analytical form available. parameters prob dispersion (also commonly represented r).","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/dot-convert_params_nbinom.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Convert parameters of the negative binomial distribution to summary statistics — .convert_params_nbinom","text":"","code":".convert_params_nbinom(...)"},{"path":"https://epiverse-trace.github.io/epiparameter/reference/dot-convert_params_nbinom.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Convert parameters of the negative binomial distribution to summary statistics — .convert_params_nbinom","text":"... <dynamic-dots> Numeric named parameter(s) used convert summary statistics. example meanlog sdlog parameters lognormal (lnorm) distribution.","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/dot-convert_params_nbinom.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Convert parameters of the negative binomial distribution to summary statistics — .convert_params_nbinom","text":"list eight elements including: mean, median, mode, variance (var), standard deviation (sd), coefficient variation (cv), skewness, ex_kurtosis.","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/dot-convert_params_weibull.html","id":null,"dir":"Reference","previous_headings":"","what":"Convert parameters of the Weibull distribution to summary statistics — .convert_params_weibull","title":"Convert parameters of the Weibull distribution to summary statistics — .convert_params_weibull","text":"Convert shape scale parameters Weibull distribution number summary statistics can calculated analytically given Weibull parameters. Note conversion uses gamma() function.","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/dot-convert_params_weibull.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Convert parameters of the Weibull distribution to summary statistics — .convert_params_weibull","text":"","code":".convert_params_weibull(...)"},{"path":"https://epiverse-trace.github.io/epiparameter/reference/dot-convert_params_weibull.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Convert parameters of the Weibull distribution to summary statistics — .convert_params_weibull","text":"... <dynamic-dots> Numeric named parameter(s) used convert summary statistics. example meanlog sdlog parameters lognormal (lnorm) distribution.","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/dot-convert_params_weibull.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Convert parameters of the Weibull distribution to summary statistics — .convert_params_weibull","text":"list eight elements including: mean, median, mode, variance (var), standard deviation (sd), coefficient variation (cv), skewness, excess kurtosis (ex_kurtosis).","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/dot-convert_summary_stats_gamma.html","id":null,"dir":"Reference","previous_headings":"","what":"Convert summary statistics to parameters of the gamma distribution — .convert_summary_stats_gamma","title":"Convert summary statistics to parameters of the gamma distribution — .convert_summary_stats_gamma","text":"Convert summary statistics input shape scale parameters gamma distribution.","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/dot-convert_summary_stats_gamma.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Convert summary statistics to parameters of the gamma distribution — .convert_summary_stats_gamma","text":"","code":".convert_summary_stats_gamma(...)"},{"path":"https://epiverse-trace.github.io/epiparameter/reference/dot-convert_summary_stats_gamma.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Convert summary statistics to parameters of the gamma distribution — .convert_summary_stats_gamma","text":"... <dynamic-dots> Numeric named summary statistics used convert parameter(s). example mean sd summary statistics lognormal (lnorm) distribution.","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/dot-convert_summary_stats_gamma.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Convert summary statistics to parameters of the gamma distribution — .convert_summary_stats_gamma","text":"list two elements, shape scale","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/dot-convert_summary_stats_geom.html","id":null,"dir":"Reference","previous_headings":"","what":"Convert summary statistics to parameters of the geometric distribution — .convert_summary_stats_geom","title":"Convert summary statistics to parameters of the geometric distribution — .convert_summary_stats_geom","text":"Convert summary statistics geometric distribution parameter (prob) geometric distribution.","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/dot-convert_summary_stats_geom.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Convert summary statistics to parameters of the geometric distribution — .convert_summary_stats_geom","text":"","code":".convert_summary_stats_geom(...)"},{"path":"https://epiverse-trace.github.io/epiparameter/reference/dot-convert_summary_stats_geom.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Convert summary statistics to parameters of the geometric distribution — .convert_summary_stats_geom","text":"... <dynamic-dots> Numeric named summary statistics used convert parameter(s). example mean sd summary statistics lognormal (lnorm) distribution.","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/dot-convert_summary_stats_geom.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Convert summary statistics to parameters of the geometric distribution — .convert_summary_stats_geom","text":"list one element, probability parameter.","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/dot-convert_summary_stats_geom.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Convert summary statistics to parameters of the geometric distribution — .convert_summary_stats_geom","text":"conversion function assumes distribution represents number failures first success (supported zero). form used base R distributional::dist_geometric().","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/dot-convert_summary_stats_lnorm.html","id":null,"dir":"Reference","previous_headings":"","what":"Convert summary statistics to parameters of the lognormal distribution — .convert_summary_stats_lnorm","title":"Convert summary statistics to parameters of the lognormal distribution — .convert_summary_stats_lnorm","text":"Convert summary statistics input meanlog sdlog parameters lognormal distribution.","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/dot-convert_summary_stats_lnorm.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Convert summary statistics to parameters of the lognormal distribution — .convert_summary_stats_lnorm","text":"","code":".convert_summary_stats_lnorm(...)"},{"path":"https://epiverse-trace.github.io/epiparameter/reference/dot-convert_summary_stats_lnorm.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Convert summary statistics to parameters of the lognormal distribution — .convert_summary_stats_lnorm","text":"... <dynamic-dots> Numeric named summary statistics used convert parameter(s). example mean sd summary statistics lognormal (lnorm) distribution.","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/dot-convert_summary_stats_lnorm.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Convert summary statistics to parameters of the lognormal distribution — .convert_summary_stats_lnorm","text":"list two elements: meanlog sdlog","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/dot-convert_summary_stats_nbinom.html","id":null,"dir":"Reference","previous_headings":"","what":"Convert summary statistics to parameters of the negative binomial distribution — .convert_summary_stats_nbinom","title":"Convert summary statistics to parameters of the negative binomial distribution — .convert_summary_stats_nbinom","text":"Convert summary statistics negative binomial distribution parameters (prob) (dispersion) negative binomial distribution.","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/dot-convert_summary_stats_nbinom.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Convert summary statistics to parameters of the negative binomial distribution — .convert_summary_stats_nbinom","text":"","code":".convert_summary_stats_nbinom(...)"},{"path":"https://epiverse-trace.github.io/epiparameter/reference/dot-convert_summary_stats_nbinom.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Convert summary statistics to parameters of the negative binomial distribution — .convert_summary_stats_nbinom","text":"... <dynamic-dots> Numeric named summary statistics used convert parameter(s). example mean sd summary statistics lognormal (lnorm) distribution.","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/dot-convert_summary_stats_nbinom.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Convert summary statistics to parameters of the negative binomial distribution — .convert_summary_stats_nbinom","text":"list two elements, probability dispersion parameters.","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/dot-convert_summary_stats_weibull.html","id":null,"dir":"Reference","previous_headings":"","what":"Convert summary statistics to parameters of the Weibull distribution — .convert_summary_stats_weibull","title":"Convert summary statistics to parameters of the Weibull distribution — .convert_summary_stats_weibull","text":"Convert summary statistics input shape scale parameters Weibull distribution.","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/dot-convert_summary_stats_weibull.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Convert summary statistics to parameters of the Weibull distribution — .convert_summary_stats_weibull","text":"","code":".convert_summary_stats_weibull(...)"},{"path":"https://epiverse-trace.github.io/epiparameter/reference/dot-convert_summary_stats_weibull.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Convert summary statistics to parameters of the Weibull distribution — .convert_summary_stats_weibull","text":"... <dynamic-dots> Numeric named summary statistics used convert parameter(s). example mean sd summary statistics lognormal (lnorm) distribution.","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/dot-convert_summary_stats_weibull.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Convert summary statistics to parameters of the Weibull distribution — .convert_summary_stats_weibull","text":"list two elements, shape scale.","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/dot-distributional_family.html","id":null,"dir":"Reference","previous_headings":"","what":"Get the underlying distributions names from a <distribution> object from the distributional package in R distribution naming convention. — .distributional_family","title":"Get the underlying distributions names from a <distribution> object from the distributional package in R distribution naming convention. — .distributional_family","text":"Get underlying distributions names <distribution> object distributional package R distribution naming convention.","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/dot-distributional_family.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Get the underlying distributions names from a <distribution> object from the distributional package in R distribution naming convention. — .distributional_family","text":"","code":".distributional_family(x, base_dist = TRUE)"},{"path":"https://epiverse-trace.github.io/epiparameter/reference/dot-distributional_family.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Get the underlying distributions names from a <distribution> object from the distributional package in R distribution naming convention. — .distributional_family","text":"x <distribution> object. base_dist boolean logical whether return name transformed distribution (e.g. \"mixture\" \"truncated\") underlying distribution type (e.g. \"gamma\" \"lnorm\"). Default TRUE.","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/dot-distributional_family.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Get the underlying distributions names from a <distribution> object from the distributional package in R distribution naming convention. — .distributional_family","text":"character vector.","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/dot-distributional_family.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Get the underlying distributions names from a <distribution> object from the distributional package in R distribution naming convention. — .distributional_family","text":"Get standardise distribution name. untransformed distributions (e.g. distributional::dist_gamma()) return distribution name. transformed distributions (e.g. distributional::dist_mixture()) get name underlying distribution(s) default (base_dist = TRUE). Distribution names returned R naming style (e.g. lognormal \"lnorm\"). base_dist = FALSE transformed distributions return name transformation (e.g. \"mixture\").","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/dot-epiparameter_df_to_epiparameter.html","id":null,"dir":"Reference","previous_headings":"","what":"Convert <data.frame> from as.data.frame.epiparameter() to <epiparameter> — .epiparameter_df_to_epiparameter","title":"Convert <data.frame> from as.data.frame.epiparameter() to <epiparameter> — .epiparameter_df_to_epiparameter","text":"Convert <data.frame> .data.frame.epiparameter() <epiparameter>","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/dot-epiparameter_df_to_epiparameter.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Convert <data.frame> from as.data.frame.epiparameter() to <epiparameter> — .epiparameter_df_to_epiparameter","text":"","code":".epiparameter_df_to_epiparameter(x, ...)"},{"path":"https://epiverse-trace.github.io/epiparameter/reference/dot-epiparameter_df_to_epiparameter.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Convert <data.frame> from as.data.frame.epiparameter() to <epiparameter> — .epiparameter_df_to_epiparameter","text":"x <data.frame>. ... dots Extra arguments pass epiparameter().","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/dot-epiparameter_df_to_epiparameter.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Convert <data.frame> from as.data.frame.epiparameter() to <epiparameter> — .epiparameter_df_to_epiparameter","text":"<epiparameter> object.","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/dot-extract_param.html","id":null,"dir":"Reference","previous_headings":"","what":"Optimises the parameters for a specified probability distribution given the percentiles of a distribution and the values at those percentiles, or the median and range of a sample and the number of samples. — .extract_param","title":"Optimises the parameters for a specified probability distribution given the percentiles of a distribution and the values at those percentiles, or the median and range of a sample and the number of samples. — .extract_param","text":"Optimises parameters specified probability distribution given percentiles distribution values percentiles, median range sample number samples.","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/dot-extract_param.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Optimises the parameters for a specified probability distribution given the percentiles of a distribution and the values at those percentiles, or the median and range of a sample and the number of samples. — .extract_param","text":"","code":".extract_param(values, distribution, percentiles, samples)"},{"path":"https://epiverse-trace.github.io/epiparameter/reference/dot-extract_param.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Optimises the parameters for a specified probability distribution given the percentiles of a distribution and the values at those percentiles, or the median and range of a sample and the number of samples. — .extract_param","text":"values vector. type = percentiles: c(percentile_1, percentile_2); type = range: c(median, min, max). distribution character specifying distribution use. Default lnorm; also takes gamma, weibull norm. percentiles vector two elements specifying percentiles defined values using type = \"percentiles\". Percentiles specified 0 1. example 2.5th 97.5th percentile given c(0.025, 0.975). samples numeric specifying sample size using type = \"range\".","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/dot-extract_param.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Optimises the parameters for a specified probability distribution given the percentiles of a distribution and the values at those percentiles, or the median and range of a sample and the number of samples. — .extract_param","text":"list output stats::optim().","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/dot-get_percentiles.html","id":null,"dir":"Reference","previous_headings":"","what":"Convert a vector of named percentiles into correct format and selects two values for parameter extraction — .get_percentiles","title":"Convert a vector of named percentiles into correct format and selects two values for parameter extraction — .get_percentiles","text":"Parameters probability distribution can extracted using values given percentiles distribution percentiles using extract_param(). .get_percentiles() takes named vector percentiles (names) values percentiles (elements vector) selects two values lower upper percentiles used extraction. lower upper percentile available NA returned. also formats vector names can correctly converted numeric using .numeric().","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/dot-get_percentiles.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Convert a vector of named percentiles into correct format and selects two values for parameter extraction — .get_percentiles","text":"","code":".get_percentiles(percentiles)"},{"path":"https://epiverse-trace.github.io/epiparameter/reference/dot-get_percentiles.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Convert a vector of named percentiles into correct format and selects two values for parameter extraction — .get_percentiles","text":"percentiles named vector values percentiles names percentiles. See Details accepted vector name format.","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/dot-get_percentiles.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Convert a vector of named percentiles into correct format and selects two values for parameter extraction — .get_percentiles","text":"named numeric vector percentiles.","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/dot-get_percentiles.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Convert a vector of named percentiles into correct format and selects two values for parameter extraction — .get_percentiles","text":"name format character value percentile. Numbers decimal places decimal point name. example 5th 95th percentile can given 2.5th 97.5th percentile can given ","code":".get_percentiles(c(\"5\" = 1, \"95\" = 10)) .get_percentiles(c(\"2.5\" = 1, \"97.5\" = 10))"},{"path":"https://epiverse-trace.github.io/epiparameter/reference/dot-get_sym_percentiles.html","id":null,"dir":"Reference","previous_headings":"","what":"Get the lower and upper percentiles with a preference for symmetrical percentiles — .get_sym_percentiles","title":"Get the lower and upper percentiles with a preference for symmetrical percentiles — .get_sym_percentiles","text":"Get lower upper percentiles preference symmetrical percentiles","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/dot-get_sym_percentiles.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Get the lower and upper percentiles with a preference for symmetrical percentiles — .get_sym_percentiles","text":"","code":".get_sym_percentiles(percentiles)"},{"path":"https://epiverse-trace.github.io/epiparameter/reference/dot-get_sym_percentiles.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Get the lower and upper percentiles with a preference for symmetrical percentiles — .get_sym_percentiles","text":"percentiles named vector percentiles. names correct format converted numeric value using .numeric().","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/dot-get_sym_percentiles.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Get the lower and upper percentiles with a preference for symmetrical percentiles — .get_sym_percentiles","text":"named numeric vector two elements lower (first element) upper (second element) percentiles.","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/dot-is_epireview.html","id":null,"dir":"Reference","previous_headings":"","what":"Check if <data.frame> input is from epireview — .is_epireview","title":"Check if <data.frame> input is from epireview — .is_epireview","text":"Check <data.frame> input epireview","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/dot-is_epireview.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Check if <data.frame> input is from epireview — .is_epireview","text":"","code":".is_epireview(x)"},{"path":"https://epiverse-trace.github.io/epiparameter/reference/dot-is_epireview.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Check if <data.frame> input is from epireview — .is_epireview","text":"x <data.frame>.","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/dot-is_epireview.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Check if <data.frame> input is from epireview — .is_epireview","text":"single logical boolean.","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/epidist_db.html","id":null,"dir":"Reference","previous_headings":"","what":"Create <epiparameter> object(s) directly from the epiparameter library (database) — epidist_db","title":"Create <epiparameter> object(s) directly from the epiparameter library (database) — epidist_db","text":"epidist_db() renamed epiparameter_db(). Please use epiparameter_db() instead epidist_db() alias removed package future.","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/epidist_db.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Create <epiparameter> object(s) directly from the epiparameter library (database) — epidist_db","text":"","code":"epidist_db( disease = \"all\", pathogen = \"all\", epi_name = \"all\", author = NULL, subset = NULL, single_epiparameter = FALSE )"},{"path":"https://epiverse-trace.github.io/epiparameter/reference/epidist_db.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Create <epiparameter> object(s) directly from the epiparameter library (database) — epidist_db","text":"disease character string specifying disease. pathogen character string specifying pathogen. epi_name character string specifying epidemiological parameter. See details full list epidemiological distributions. author character string specifying author study reporting distribution. first author matched. recommended use family name first names may may initialised. subset Either NULL valid R expressions evaluates logicals subset list <epiparameter>, function can applied list <epiparameter> objects. Subsetting (using subset) can combined subsetting done disease epi_name arguments (author specified). left NULL (default) subsetting carried . subset argument similar subsetting <data.frame>, difference fixed comparisons vectorised comparisons needed. example sample_size > 10 valid subset expression, sample_size == max(sample_size), valid subset expression <data.frame> work. vectorised expression often error, likely return unexpected results. sample_size == max(sample_size) example always return TRUE (except NAs) single numeric equal max value. expression specified without using data object name (e.g. df$var) instead just var supplied. words, argument uses non-standard evaluation, just subset argument subset(), similar <data-masking> used dplyr package. single_epiparameter boolean logical determining whether single <epiparameter> multiple entries library can returned matched arguments (disease, epi_name, author). argument used prevent multiple sets parameters returned one wanted. Note: multiple entries match arguments supplied single_epiparameter = TRUE <epiparameter> parameterised (accounts truncation available) largest sample size returned (see is_parameterised()). multiple entries equal sorting first entry returned.","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/epidist_db.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Create <epiparameter> object(s) directly from the epiparameter library (database) — epidist_db","text":"<epiparameter> object list <epiparameter> objects.","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/epiparameter-package.html","id":null,"dir":"Reference","previous_headings":"","what":"epiparameter: Library of Epidemiological Parameters with Helper Functions and Classes — epiparameter-package","title":"epiparameter: Library of Epidemiological Parameters with Helper Functions and Classes — epiparameter-package","text":"Library epidemiological parameters infectious diseases extracted literature, set classes helper functions working parameters.","code":""},{"path":[]},{"path":"https://epiverse-trace.github.io/epiparameter/reference/epiparameter-package.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"epiparameter: Library of Epidemiological Parameters with Helper Functions and Classes — epiparameter-package","text":"Maintainer: Joshua W. Lambert joshua.lambert@lshtm.ac.uk (ORCID) [copyright holder] Authors: Adam Kucharski adam.kucharski@lshtm.ac.uk (ORCID) [copyright holder] Carmen Tamayo carmen.tamayo-cuartero@lshtm.ac.uk (ORCID) contributors: Hugo Gruson hugo.gruson@data.org (ORCID) [contributor, reviewer] Sebastian Funk sebastian.funk@lshtm.ac.uk (ORCID) [contributor] Pratik Gupte pratik.gupte@lshtm.ac.uk (ORCID) [reviewer] James M. Azam james.azam@lshtm.ac.uk (ORCID) [reviewer] Chris Hartgerink chris@data.org (ORCID) [reviewer] Tim Taylor tim.taylor@hiddenelephants.co.uk (ORCID) [reviewer]","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/epiparameter.html","id":null,"dir":"Reference","previous_headings":"","what":"Create an <epiparameter> object — epiparameter","title":"Create an <epiparameter> object — epiparameter","text":"<epiparameter> class used store epidemiological parameters single disease. epidemiological parameters cover variety aspects including delay distributions (e.g. incubation periods serial intervals, among others) offspring distributions. <epiparameter> object functional unit provided {epiparameter} plug epidemiological pipelines. Obtaining <epiparameter> object can achieved two main ways: epidemiological distribution stored {epiparameter} library can accessed epiparameter_db(). alternative method information (e.g. disease distribution parameter estimates) like input <epiparameter> object order work existing analysis pipelines. epiparameter() function can used fill field information known.","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/epiparameter.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Create an <epiparameter> object — epiparameter","text":"","code":"epiparameter( disease, pathogen = NA_character_, epi_name, prob_distribution = create_prob_distribution(prob_distribution = NA_character_), uncertainty = create_uncertainty(), summary_stats = create_summary_stats(), citation = create_citation(), metadata = create_metadata(), method_assess = create_method_assess(), notes = NULL, auto_calc_params = TRUE, ... )"},{"path":"https://epiverse-trace.github.io/epiparameter/reference/epiparameter.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Create an <epiparameter> object — epiparameter","text":"disease character string name infectious disease. pathogen character string name causative agent disease, NA known. epi_name character string name epidemiological parameter type. prob_distribution S3 class containing probability distribution character string parameters probability distribution unknown name distribution known, NA distribution name parameters unknown. Use create_prob_distribution() create prob_distribution. uncertainty list named vectors uncertainty around probability distribution parameters. uncertainty around parameter estimates unknown use create_uncertainty() (argument default) create list correct names missing values. summary_stats list summary statistics, use create_summary_stats() create list. list can include summary statistics inferred distribution mean standard deviation, quantiles distribution, information data used fit distribution lower upper range. summary statistics can also include uncertainty around metrics confidence interval around mean standard deviation. citation <bibentry> citation source data paper inferred distribution parameters, use create_citation() create citation. metadata list metadata, can include: units, sample size, transmission mode disease (e.g. vector-borne directly transmitted), etc. assumed disease vector-borne distribution intrinsic (e.g. extrinsic delay distribution extrinsic incubation period) unless transmission_mode = \"vector_borne\" contained metadata. Use create_metadata() create metadata. method_assess list methodological aspects used fitting distribution, use create_method_assess() create method assessment. notes character string additional information data, inference method disease. auto_calc_params boolean logical determining whether try calculate probability distribution parameters summary statistics distribution parameters provided. Default TRUE. case sufficient summary statistics provided parameter(s) distribution , .calc_dist_params() function called calculate parameters add epiparameter object created. ... dots Extra arguments passed internal functions. commonly used pass arguments distcrete::distcrete() construct discretised distribution S3 object. see arguments can adjusted discretised distributions see distcrete::distcrete().","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/epiparameter.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Create an <epiparameter> object — epiparameter","text":"<epiparameter> object.","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/epiparameter.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Create an <epiparameter> object — epiparameter","text":"Accepted <epiparameter> distribution parameterisations : Gamma must either 'shape' 'scale' 'shape' 'rate' Weibull must 'shape' 'scale' Lognormal must 'meanlog' 'sdlog' 'mu' 'sigma' Negative Binomial must either 'mean' 'dispersion' 'n' 'p' Geometric must either 'mean' 'prob' Poisson must 'mean'","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/epiparameter.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Create an <epiparameter> object — epiparameter","text":"","code":"# minimal input required for `epiparameter` ebola_incubation <- epiparameter( disease = \"ebola\", epi_name = \"incubation_period\", prob_distribution = create_prob_distribution( prob_distribution = \"gamma\", prob_distribution_params = c(shape = 1, scale = 1) ) ) #> Citation cannot be created as author, year, journal or title is missing # minimal input required for discrete `epiparameter` ebola_incubation <- epiparameter( disease = \"ebola\", epi_name = \"incubation_period\", prob_distribution = create_prob_distribution( prob_distribution = \"gamma\", prob_distribution_params = c(shape = 1, scale = 1), discretise = TRUE ) ) #> Citation cannot be created as author, year, journal or title is missing # example with more fields filled in ebola_incubation <- epiparameter( disease = \"ebola\", pathogen = \"ebola_virus\", epi_name = \"incubation\", prob_distribution = create_prob_distribution( prob_distribution = \"gamma\", prob_distribution_params = c(shape = 1, scale = 1), discretise = FALSE, truncation = NA ), uncertainty = list( shape = create_uncertainty(), scale = create_uncertainty() ), summary_stats = create_summary_stats(mean = 2, sd = 1), citation = create_citation( author = person(given = \"John\", family = \"Smith\"), year = 2002, title = \"COVID-19 incubation period\", journal = \"Epi Journal\", doi = \"10.19832/j.1366-9516.2012.09147.x\" ), metadata = create_metadata( units = \"days\", sample_size = 10, region = \"UK\", transmission_mode = \"natural_human_to_human\", inference_method = \"MLE\" ), method_assess = create_method_assess( censored = TRUE ), notes = \"No notes\" ) #> Using Smith J (2002). “COVID-19 incubation period.” _Epi Journal_. #> doi:10.19832/j.1366-9516.2012.09147.x #> <https://doi.org/10.19832/j.1366-9516.2012.09147.x>. #> To retrieve the citation use the 'get_citation' function"},{"path":"https://epiverse-trace.github.io/epiparameter/reference/epiparameter_db.html","id":null,"dir":"Reference","previous_headings":"","what":"Create <epiparameter> object(s) directly from the epiparameter library (database) — epiparameter_db","title":"Create <epiparameter> object(s) directly from the epiparameter library (database) — epiparameter_db","text":"Extract <epiparameter> object(s) directly library epidemiological parameters. epiparameter library epidemiological parameters compiled primary literature sources. list output epiparameter_db() can subset data contains, example : disease, pathogen, epidemiological distribution, sample size, region, etc. distribution specific study required, author argument can specified. Multiple entries (<epiparameter> objects) can returned, use arguments subset entries use single_epiparameter = TRUE force single <epiparameter> returned.","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/epiparameter_db.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Create <epiparameter> object(s) directly from the epiparameter library (database) — epiparameter_db","text":"","code":"epiparameter_db( disease = \"all\", pathogen = \"all\", epi_name = \"all\", author = NULL, subset = NULL, single_epiparameter = FALSE )"},{"path":"https://epiverse-trace.github.io/epiparameter/reference/epiparameter_db.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Create <epiparameter> object(s) directly from the epiparameter library (database) — epiparameter_db","text":"disease character string specifying disease. pathogen character string specifying pathogen. epi_name character string specifying epidemiological parameter. See details full list epidemiological distributions. author character string specifying author study reporting distribution. first author matched. recommended use family name first names may may initialised. subset Either NULL valid R expressions evaluates logicals subset list <epiparameter>, function can applied list <epiparameter> objects. Subsetting (using subset) can combined subsetting done disease epi_name arguments (author specified). left NULL (default) subsetting carried . subset argument similar subsetting <data.frame>, difference fixed comparisons vectorised comparisons needed. example sample_size > 10 valid subset expression, sample_size == max(sample_size), valid subset expression <data.frame> work. vectorised expression often error, likely return unexpected results. sample_size == max(sample_size) example always return TRUE (except NAs) single numeric equal max value. expression specified without using data object name (e.g. df$var) instead just var supplied. words, argument uses non-standard evaluation, just subset argument subset(), similar <data-masking> used dplyr package. single_epiparameter boolean logical determining whether single <epiparameter> multiple entries library can returned matched arguments (disease, epi_name, author). argument used prevent multiple sets parameters returned one wanted. Note: multiple entries match arguments supplied single_epiparameter = TRUE <epiparameter> parameterised (accounts truncation available) largest sample size returned (see is_parameterised()). multiple entries equal sorting first entry returned.","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/epiparameter_db.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Create <epiparameter> object(s) directly from the epiparameter library (database) — epiparameter_db","text":"<epiparameter> object list <epiparameter> objects.","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/epiparameter_db.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Create <epiparameter> object(s) directly from the epiparameter library (database) — epiparameter_db","text":"disease, epi_name author given individual arguments common variables subset parameter library . subset argument facilitates subsetting rows select <epiparameter> object(s) desired. subset based multiple variables separate expression &. List epidemiological parameters: \"\" (default, returns entries library) \"incubation period\" \"onset hospitalisation\" \"onset death\" \"serial interval\" \"generation time\" \"offspring distribution\" \"hospitalisation death\" \"hospitalisation discharge\" \"notification death\" \"notification discharge\" \"onset discharge\" \"onset ventilation\"","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/epiparameter_db.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Create <epiparameter> object(s) directly from the epiparameter library (database) — epiparameter_db","text":"","code":"epiparameter_db(disease = \"influenza\", epi_name = \"serial_interval\") #> Returning 1 results that match the criteria (1 are parameterised). #> Use subset to filter by entry variables or single_epiparameter to return a single entry. #> To retrieve the citation for each use the 'get_citation' function #> Disease: Influenza #> Pathogen: Influenza-A-H1N1Pdm #> Epi Parameter: serial interval #> Study: Ghani A, Baguelin M, Griffin J, Flasche S, van Hoek A, Cauchemez S, #> Donnelly C, Robertson C, White M, Truscott J, Fraser C, Garske T, White #> P, Leach S, Hall I, Jenkins H, Ferguson N, Cooper B (2009). “The Early #> Transmission Dynamics of H1N1pdm Influenza in the United Kingdom.” #> _PLoS Currents_. doi:10.1371/currents.RRN1130 #> <https://doi.org/10.1371/currents.RRN1130>. #> Distribution: gamma (days) #> Parameters: #> shape: 2.622 #> scale: 0.957 # example using custom subsetting eparam <- epiparameter_db( disease = \"SARS\", epi_name = \"offspring_distribution\", subset = sample_size > 40 ) #> Returning 1 results that match the criteria (1 are parameterised). #> Use subset to filter by entry variables or single_epiparameter to return a single entry. #> To retrieve the citation for each use the 'get_citation' function # example using functional subsetting eparam <- epiparameter_db( disease = \"COVID-19\", epi_name = \"incubation_period\", subset = is_parameterised ) #> Returning 11 results that match the criteria (11 are parameterised). #> Use subset to filter by entry variables or single_epiparameter to return a single entry. #> To retrieve the citation for each use the 'get_citation' function # example forcing a single <epiparameter> to be returned eparam <- epiparameter_db( disease = \"SARS\", epi_name = \"offspring_distribution\", single_epiparameter = TRUE ) #> Using Lloyd-Smith J, Schreiber S, Kopp P, Getz W (2005). “Superspreading and #> the effect of individual variation on disease emergence.” _Nature_. #> doi:10.1038/nature04153 <https://doi.org/10.1038/nature04153>.. #> To retrieve the citation use the 'get_citation' function"},{"path":"https://epiverse-trace.github.io/epiparameter/reference/epiparameter_distribution_functions.html","id":null,"dir":"Reference","previous_headings":"","what":"PDF, CDF, PMF, quantiles and random number generation for <epiparameter> objects — epiparameter_distribution_functions","title":"PDF, CDF, PMF, quantiles and random number generation for <epiparameter> objects — epiparameter_distribution_functions","text":"<epiparameter> object holds probability distribution can either continuous discrete distribution. density, cumulative distribution, quantile random number generation functions. operate distribution can included <epiparameter> object.","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/epiparameter_distribution_functions.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"PDF, CDF, PMF, quantiles and random number generation for <epiparameter> objects — epiparameter_distribution_functions","text":"","code":"# S3 method for class 'epiparameter' density(x, at, ...) # S3 method for class 'epiparameter' cdf(x, q, ..., log = FALSE) # S3 method for class 'epiparameter' quantile(x, p, ...) # S3 method for class 'epiparameter' generate(x, times, ...)"},{"path":"https://epiverse-trace.github.io/epiparameter/reference/epiparameter_distribution_functions.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"PDF, CDF, PMF, quantiles and random number generation for <epiparameter> objects — epiparameter_distribution_functions","text":"x <epiparameter> object. quantiles evaluate . ... dots Extra arguments passed method. q quantiles evaluate . log TRUE, probabilities given log probabilities. p probabilities evaluate . times number random samples.","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/epiparameter_distribution_functions.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"PDF, CDF, PMF, quantiles and random number generation for <epiparameter> objects — epiparameter_distribution_functions","text":"numeric vector.","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/epiparameter_distribution_functions.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"PDF, CDF, PMF, quantiles and random number generation for <epiparameter> objects — epiparameter_distribution_functions","text":"","code":"ep <- epiparameter( disease = \"ebola\", epi_name = \"incubation_period\", prob_distribution = create_prob_distribution( prob_distribution = \"gamma\", prob_distribution_params = c(shape = 1, scale = 1) ) ) #> Citation cannot be created as author, year, journal or title is missing # example of each distribution method for an `epiparameter` object stats::density(ep, at = 1) #> [1] 0.3678794 distributional::cdf(ep, q = 1) #> [1] 0.6321206 stats::quantile(ep, p = 0.2) #> [1] 0.2231436 distributional::generate(ep, times = 10) #> [1] 3.56720380 1.16790186 0.05745463 0.34040705 3.47156091 1.04366207 #> [7] 3.02627506 0.36756952 0.09970044 0.78957870"},{"path":"https://epiverse-trace.github.io/epiparameter/reference/epiparameter_options.html","id":null,"dir":"Reference","previous_headings":"","what":"Package options — epiparameter_options","title":"Package options — epiparameter_options","text":"Options modify printing epiparameter objects. Currently options used modify printing <multi_epiparameter> class.","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/epiparameter_options.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Package options — epiparameter_options","text":"","code":"epiparameter_options"},{"path":"https://epiverse-trace.github.io/epiparameter/reference/epiparameter_options.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Package options — epiparameter_options","text":"object class list length 2.","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/epiparameter_options.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Package options — epiparameter_options","text":"Options set options() retrieved getOption(). options changed epiparameter package need reloaded new options taken account. Options can set .Rprofile persist across R sessions.","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/epireview_core_cols.html","id":null,"dir":"Reference","previous_headings":"","what":"A vector of character strings with the core column names of the epidemiological parameter data exported by the epireview R package. — epireview_core_cols","title":"A vector of character strings with the core column names of the epidemiological parameter data exported by the epireview R package. — epireview_core_cols","text":"vector character strings core column names epidemiological parameter data exported epireview R package.","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/epireview_core_cols.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"A vector of character strings with the core column names of the epidemiological parameter data exported by the epireview R package. — epireview_core_cols","text":"","code":"epireview_core_cols"},{"path":[]},{"path":"https://epiverse-trace.github.io/epiparameter/reference/epireview_core_cols.html","id":"epireview-core-cols","dir":"Reference","previous_headings":"","what":"epireview_core_cols","title":"A vector of character strings with the core column names of the epidemiological parameter data exported by the epireview R package. — epireview_core_cols","text":"character vector 58 elements. data taken intersection column names disease parameter tables epireview R package.","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/epireview_core_cols.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"A vector of character strings with the core column names of the epidemiological parameter data exported by the epireview R package. — epireview_core_cols","text":"https://github.com/mrc-ide/epireview","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/extract_param.html","id":null,"dir":"Reference","previous_headings":"","what":"Calculate the parameters of a parametric probability distribution from reported values of percentiles, or median and range — extract_param","title":"Calculate the parameters of a parametric probability distribution from reported values of percentiles, or median and range — extract_param","text":"Summary data distributions, provided reports meta-analyses, can used extract parameters chosen distribution. Currently available distributions : lognormal, gamma, Weibull normal. Extracting lognormal returns meanlog sdlog parameters, extracting gamma Weibull returns shape scale parameters, extracting normal returns mean sd parameters.","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/extract_param.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculate the parameters of a parametric probability distribution from reported values of percentiles, or median and range — extract_param","text":"","code":"extract_param( type = c(\"percentiles\", \"range\"), values, distribution = c(\"lnorm\", \"gamma\", \"weibull\", \"norm\"), percentiles, samples, control = list(max_iter = 1000, tolerance = 1e-05) )"},{"path":"https://epiverse-trace.github.io/epiparameter/reference/extract_param.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Calculate the parameters of a parametric probability distribution from reported values of percentiles, or median and range — extract_param","text":"type character defining whether summary statistics based around percentiles (default) range. values vector. type = percentiles: c(percentile_1, percentile_2); type = range: c(median, min, max). distribution character specifying distribution use. Default lnorm; also takes gamma, weibull norm. percentiles vector two elements specifying percentiles defined values using type = \"percentiles\". Percentiles specified 0 1. example 2.5th 97.5th percentile given c(0.025, 0.975). samples numeric specifying sample size using type = \"range\". control named list containing options optimisation. List element $max_iter numeric specifying maximum number times parameter extraction run optimisation returning result early. prevents overly long optimisation loops optimisation unstable converge multiple iterations. Default 1000 iterations. List element $tolerance passed .check_optim_conv() tolerance parameter convergence iterations optimisation. Elements control list passed optim().","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/extract_param.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Calculate the parameters of a parametric probability distribution from reported values of percentiles, or median and range — extract_param","text":"named numeric vector parameter values distribution. distribution = lnorm parameters returned meanlog sdlog; distribution = gamma distribution = weibull parameters returned shape scale; distribution = norm parameters returned mean sd.","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/extract_param.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Calculate the parameters of a parametric probability distribution from reported values of percentiles, or median and range — extract_param","text":"gamma, lnorm weibull, extract_param() works strictly positive values percentiles distribution median range data (numerics supplied values argument). means negative values lower percentile lower range work function although may present epidemiological data (e.g. negative serial interval). norm distribution negative values allowed.","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/extract_param.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Calculate the parameters of a parametric probability distribution from reported values of percentiles, or median and range — extract_param","text":"Adam Kucharski, Joshua W. Lambert","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/extract_param.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Calculate the parameters of a parametric probability distribution from reported values of percentiles, or median and range — extract_param","text":"","code":"# set seed to control for stochasticity set.seed(1) # extract parameters of a lognormal distribution from the 75 percentiles extract_param( type = \"percentiles\", values = c(6, 13), distribution = \"lnorm\", percentiles = c(0.125, 0.875) ) #> Stochastic numerical optimisation used. #> Rerun function multiple times to check global optimum is found #> meanlog sdlog #> 2.1783557 0.3360688 # extract parameters of a gamma distribution from median and range extract_param( type = \"range\", values = c(10, 3, 18), distribution = \"gamma\", samples = 20 ) #> Stochastic numerical optimisation used. #> Rerun function multiple times to check global optimum is found #> shape scale #> 5.342206 1.994304"},{"path":"https://epiverse-trace.github.io/epiparameter/reference/extraction_functions.html","id":null,"dir":"Reference","previous_headings":"","what":"Function for extracting distribution parameters — extraction_functions","title":"Function for extracting distribution parameters — extraction_functions","text":"Set functions can used estimate parameters distribution (lognormal, gamma, Weibull, normal) via optimisation either percentiles median ranges.","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/extraction_functions.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Function for extracting distribution parameters — extraction_functions","text":"","code":".fit_range(param, val, dist = c(\"lnorm\", \"gamma\", \"weibull\", \"norm\")) .fit_percentiles(param, val, dist = c(\"lnorm\", \"gamma\", \"weibull\", \"norm\"))"},{"path":"https://epiverse-trace.github.io/epiparameter/reference/extraction_functions.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Function for extracting distribution parameters — extraction_functions","text":"param Named numeric vector distribution parameters optimised. val Numeric vector, case using percentiles contains values percentiles percentiles, case median range contains median, lower range, upper range number sample points evaluate function . dist character string name distribution fitting. Naming follows base R distribution names (e.g. lnorm lognormal).","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/extraction_functions.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Function for extracting distribution parameters — extraction_functions","text":"Adam Kucharski, Joshua W. Lambert","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/family.epiparameter.html","id":null,"dir":"Reference","previous_headings":"","what":"Family method for the <epiparameter> class — family.epiparameter","title":"Family method for the <epiparameter> class — family.epiparameter","text":"family() function used extract distribution names objects {distributional} {distcrete}. method provides interface <epiparameter> objects give consistent output irrespective internal distribution class.","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/family.epiparameter.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Family method for the <epiparameter> class — family.epiparameter","text":"","code":"# S3 method for class 'epiparameter' family(object, ..., base_dist = FALSE)"},{"path":"https://epiverse-trace.github.io/epiparameter/reference/family.epiparameter.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Family method for the <epiparameter> class — family.epiparameter","text":"object <epiparameter> object. ... arguments passed methods. base_dist boolean logical whether return name transformed distribution (e.g. \"mixture\" \"truncated\") underlying distribution type (e.g. \"gamma\" \"lnorm\"). Default FALSE.","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/family.epiparameter.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Family method for the <epiparameter> class — family.epiparameter","text":"character string name distribution, NA <epiparameter> object unparameterised.","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/family.epiparameter.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Family method for the <epiparameter> class — family.epiparameter","text":"","code":"# example with continuous distribution ep <- epiparameter( disease = \"ebola\", epi_name = \"incubation_period\", prob_distribution = create_prob_distribution( prob_distribution = \"gamma\", prob_distribution_params = c(shape = 1, scale = 1) ) ) #> Citation cannot be created as author, year, journal or title is missing family(ep) #> [1] \"gamma\" # example with discretised distribution ep <- epiparameter( disease = \"ebola\", epi_name = \"incubation_period\", prob_distribution = create_prob_distribution( prob_distribution = \"lnorm\", prob_distribution_params = c(meanlog = 1, sdlog = 1), discretise = TRUE ) ) #> Citation cannot be created as author, year, journal or title is missing family(ep) #> [1] \"lnorm\""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/format.epiparameter.html","id":null,"dir":"Reference","previous_headings":"","what":"Format method for <epiparameter> class — format.epiparameter","title":"Format method for <epiparameter> class — format.epiparameter","text":"Format method <epiparameter> class","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/format.epiparameter.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Format method for <epiparameter> class — format.epiparameter","text":"","code":"# S3 method for class 'epiparameter' format(x, ...)"},{"path":"https://epiverse-trace.github.io/epiparameter/reference/format.epiparameter.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Format method for <epiparameter> class — format.epiparameter","text":"x <epiparameter> object. ... dots Extra arguments passed method.","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/format.epiparameter.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Format method for <epiparameter> class — format.epiparameter","text":"Invisibly returns <epiparameter>. Called printing side-effects.","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/format.epiparameter.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Format method for <epiparameter> class — format.epiparameter","text":"","code":"epiparameter <- epiparameter( disease = \"ebola\", epi_name = \"incubation_period\", prob_distribution = create_prob_distribution( prob_distribution = \"gamma\", prob_distribution_params = c(shape = 1, scale = 1) ) ) #> Citation cannot be created as author, year, journal or title is missing format(epiparameter) #> Disease: ebola #> Pathogen: NA #> Epi Parameter: incubation period #> Study: (????). “No citation.” #> Distribution: gamma (NA) #> Parameters: #> shape: 1.000 #> scale: 1.000"},{"path":"https://epiverse-trace.github.io/epiparameter/reference/get_citation.epiparameter.html","id":null,"dir":"Reference","previous_headings":"","what":"Get citation from an <epiparameter> object — get_citation.epiparameter","title":"Get citation from an <epiparameter> object — get_citation.epiparameter","text":"Extract citation stored <epiparameter> object.","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/get_citation.epiparameter.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Get citation from an <epiparameter> object — get_citation.epiparameter","text":"","code":"# S3 method for class 'epiparameter' get_citation(x, ...)"},{"path":"https://epiverse-trace.github.io/epiparameter/reference/get_citation.epiparameter.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Get citation from an <epiparameter> object — get_citation.epiparameter","text":"x <epiparameter> object. ... dots used, extra arguments supplied cause warning.","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/get_citation.epiparameter.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Get citation from an <epiparameter> object — get_citation.epiparameter","text":"<bibentry> object.","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/get_citation.epiparameter.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Get citation from an <epiparameter> object — get_citation.epiparameter","text":"","code":"# example with <epiparameter> ep <- epiparameter_db(single_epiparameter = TRUE) #> Using Linton N, Kobayashi T, Yang Y, Hayashi K, Akhmetzhanov A, Jung S, Yuan #> B, Kinoshita R, Nishiura H (2020). “Incubation Period and Other #> Epidemiological Characteristics of 2019 Novel Coronavirus Infections #> with Right Truncation: A Statistical Analysis of Publicly Available #> Case Data.” _Journal of Clinical Medicine_. doi:10.3390/jcm9020538 #> <https://doi.org/10.3390/jcm9020538>.. #> To retrieve the citation use the 'get_citation' function get_citation(ep) #> Linton N, Kobayashi T, Yang Y, Hayashi K, Akhmetzhanov A, Jung S, Yuan #> B, Kinoshita R, Nishiura H (2020). “Incubation Period and Other #> Epidemiological Characteristics of 2019 Novel Coronavirus Infections #> with Right Truncation: A Statistical Analysis of Publicly Available #> Case Data.” _Journal of Clinical Medicine_. doi:10.3390/jcm9020538 #> <https://doi.org/10.3390/jcm9020538>. # example returning bibtex format ep <- epiparameter_db(disease = \"COVID-19\", single_epiparameter = TRUE) #> Using Linton N, Kobayashi T, Yang Y, Hayashi K, Akhmetzhanov A, Jung S, Yuan #> B, Kinoshita R, Nishiura H (2020). “Incubation Period and Other #> Epidemiological Characteristics of 2019 Novel Coronavirus Infections #> with Right Truncation: A Statistical Analysis of Publicly Available #> Case Data.” _Journal of Clinical Medicine_. doi:10.3390/jcm9020538 #> <https://doi.org/10.3390/jcm9020538>.. #> To retrieve the citation use the 'get_citation' function cit <- get_citation(ep) format(cit, style = \"bibtex\") #> [1] \"@Article{,\\n author = {Natalie M. Linton and Tetsuro Kobayashi and Yichi Yang and Katsuma Hayashi and Andrei R. Akhmetzhanov and Sung-mok Jung and Baoyin Yuan and Ryo Kinoshita and Hiroshi Nishiura},\\n year = {2020},\\n title = {Incubation Period and Other Epidemiological Characteristics of 2019 Novel Coronavirus Infections with Right Truncation: A Statistical Analysis of Publicly Available Case Data},\\n journal = {Journal of Clinical Medicine},\\n doi = {10.3390/jcm9020538},\\n pmid = {32079150},\\n}\""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/get_citation.html","id":null,"dir":"Reference","previous_headings":"","what":"Get citation from an object — get_citation","title":"Get citation from an object — get_citation","text":"Extract citation stored R object.","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/get_citation.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Get citation from an object — get_citation","text":"","code":"get_citation(x, ...)"},{"path":"https://epiverse-trace.github.io/epiparameter/reference/get_citation.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Get citation from an object — get_citation","text":"x object used select method. ... dots Extra arguments passed method.","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/get_citation.multi_epiparameter.html","id":null,"dir":"Reference","previous_headings":"","what":"Get citation from a list of <epiparameter> objects — get_citation.multi_epiparameter","title":"Get citation from a list of <epiparameter> objects — get_citation.multi_epiparameter","text":"Extract citation stored list <epiparameter> objects.","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/get_citation.multi_epiparameter.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Get citation from a list of <epiparameter> objects — get_citation.multi_epiparameter","text":"","code":"# S3 method for class 'multi_epiparameter' get_citation(x, ...)"},{"path":"https://epiverse-trace.github.io/epiparameter/reference/get_citation.multi_epiparameter.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Get citation from a list of <epiparameter> objects — get_citation.multi_epiparameter","text":"x <epiparameter> object. ... dots used, extra arguments supplied cause warning.","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/get_citation.multi_epiparameter.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Get citation from a list of <epiparameter> objects — get_citation.multi_epiparameter","text":"<bibentry> object containing multiple references. length output <bibentry> equal length list <epiparameter> objects supplied.","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/get_citation.multi_epiparameter.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Get citation from a list of <epiparameter> objects — get_citation.multi_epiparameter","text":"","code":"# example with list of <epiparameter> db <- epiparameter_db() #> Returning 125 results that match the criteria (100 are parameterised). #> Use subset to filter by entry variables or single_epiparameter to return a single entry. #> To retrieve the citation for each use the 'get_citation' function get_citation(db) #> Lessler J, Reich N, Brookmeyer R, Perl T, Nelson K, Cummings D (2009). #> “Incubation periods of acute respiratory viral infections: a systematic #> review.” _The Lancet Infectious Diseases_. #> doi:10.1016/S1473-3099(09)70069-6 #> <https://doi.org/10.1016/S1473-3099%2809%2970069-6>. #> #> Lessler J, Reich N, Brookmeyer R, Perl T, Nelson K, Cummings D (2009). #> “Incubation periods of acute respiratory viral infections: a systematic #> review.” _The Lancet Infectious Diseases_. #> doi:10.1016/S1473-3099(09)70069-7 #> <https://doi.org/10.1016/S1473-3099%2809%2970069-7>. #> #> Lessler J, Reich N, Brookmeyer R, Perl T, Nelson K, Cummings D (2009). #> “Incubation periods of acute respiratory viral infections: a systematic #> review.” _The Lancet Infectious Diseases_. #> doi:10.1016/S1473-3099(09)70069-8 #> <https://doi.org/10.1016/S1473-3099%2809%2970069-8>. #> #> Lessler J, Reich N, Brookmeyer R, Perl T, Nelson K, Cummings D (2009). #> “Incubation periods of acute respiratory viral infections: a systematic #> review.” _The Lancet Infectious Diseases_. #> doi:10.1016/S1473-3099(09)70069-9 #> <https://doi.org/10.1016/S1473-3099%2809%2970069-9>. #> #> Lessler J, Reich N, Brookmeyer R, Perl T, Nelson K, Cummings D (2009). #> “Incubation periods of acute respiratory viral infections: a systematic #> review.” _The Lancet Infectious Diseases_. #> doi:10.1016/S1473-3099(09)70069-10 #> <https://doi.org/10.1016/S1473-3099%2809%2970069-10>. #> #> Lessler J, Reich N, Brookmeyer R, Perl T, Nelson K, Cummings D (2009). #> “Incubation periods of acute respiratory viral infections: a systematic #> review.” _The Lancet Infectious Diseases_. #> doi:10.1016/S1473-3099(09)70069-11 #> <https://doi.org/10.1016/S1473-3099%2809%2970069-11>. #> #> Lessler J, Reich N, Brookmeyer R, Perl T, Nelson K, Cummings D (2009). #> “Incubation periods of acute respiratory viral infections: a systematic #> review.” _The Lancet Infectious Diseases_. #> doi:10.1016/S1473-3099(09)70069-12 #> <https://doi.org/10.1016/S1473-3099%2809%2970069-12>. #> #> Lessler J, Reich N, Brookmeyer R, Perl T, Nelson K, Cummings D (2009). #> “Incubation periods of acute respiratory viral infections: a systematic #> review.” _The Lancet Infectious Diseases_. #> doi:10.1016/S1473-3099(09)70069-13 #> <https://doi.org/10.1016/S1473-3099%2809%2970069-13>. #> #> Lessler J, Reich N, Brookmeyer R, Perl T, Nelson K, Cummings D (2009). #> “Incubation periods of acute respiratory viral infections: a systematic #> review.” _The Lancet Infectious Diseases_. #> doi:10.1016/S1473-3099(09)70069-14 #> <https://doi.org/10.1016/S1473-3099%2809%2970069-14>. #> #> Lessler J, Reich N, Brookmeyer R, Perl T, Nelson K, Cummings D (2009). #> “Incubation periods of acute respiratory viral infections: a systematic #> review.” _The Lancet Infectious Diseases_. #> doi:10.1016/S1473-3099(09)70069-15 #> <https://doi.org/10.1016/S1473-3099%2809%2970069-15>. #> #> Reich N, Lessler J, Cummings D, Brookmeyer R (2009). “Estimating #> incubation period distributions with coarse data.” _Statistics in #> Medicine_. doi:10.1002/sim.3659 <https://doi.org/10.1002/sim.3659>. #> #> Reich N, Lessler J, Cummings D, Brookmeyer R (2009). “Estimating #> incubation period distributions with coarse data.” _Statistics in #> Medicine_. doi:10.1002/sim.3659 <https://doi.org/10.1002/sim.3659>. #> #> Reich N, Lessler J, Cummings D, Brookmeyer R (2009). “Estimating #> incubation period distributions with coarse data.” _Statistics in #> Medicine_. doi:10.1002/sim.3659 <https://doi.org/10.1002/sim.3659>. #> #> Reich N, Lessler J, Cummings D, Brookmeyer R (2009). “Estimating #> incubation period distributions with coarse data.” _Statistics in #> Medicine_. doi:10.1002/sim.3659 <https://doi.org/10.1002/sim.3659>. #> #> Nishiura H, Inaba H (2011). “Estimation of the incubation period of #> influenza A (H1N1-2009) among imported cases: addressing censoring #> using outbreak data at the origin of importation.” _Journal of #> Theoretical Biology_. doi:10.1016/j.jtbi.2010.12.017 #> <https://doi.org/10.1016/j.jtbi.2010.12.017>. #> #> Nishiura H, Inaba H (2011). “Estimation of the incubation period of #> influenza A (H1N1-2009) among imported cases: addressing censoring #> using outbreak data at the origin of importation.” _Journal of #> Theoretical Biology_. doi:10.1016/j.jtbi.2010.12.017 #> <https://doi.org/10.1016/j.jtbi.2010.12.017>. #> #> Virlogeux V, Li M, Tsang T, Feng L, Fang V, Jiang H, Wu P, Zheng J, Lau #> E, Cao Y, Qin Y, Liao Q, Yu H, Cowling B (2015). “Estimating the #> Distribution of the Incubation Periods of Human Avian Influenza A(H7N9) #> Virus Infections.” _American Journal of Epidemiology_. #> doi:10.1093/aje/kwv115 <https://doi.org/10.1093/aje/kwv115>. #> #> Virlogeux V, Li M, Tsang T, Feng L, Fang V, Jiang H, Wu P, Zheng J, Lau #> E, Cao Y, Qin Y, Liao Q, Yu H, Cowling B (2015). “Estimating the #> Distribution of the Incubation Periods of Human Avian Influenza A(H7N9) #> Virus Infections.” _American Journal of Epidemiology_. #> doi:10.1093/aje/kwv115 <https://doi.org/10.1093/aje/kwv115>. #> #> Virlogeux V, Yang J, Fang V, Feng L, Tsang T, Jiang H, Wu P, Zheng J, #> Lau E, Qin Y, Peng Z, Peiris J, Yu H, Cowling B (2016). “Association #> between the Severity of Influenza A(H7N9) Virus Infections and Length #> of the Incubation Period.” _PLoS One_. doi:10.1371/journal.pone.0148506 #> <https://doi.org/10.1371/journal.pone.0148506>. #> #> Virlogeux V, Yang J, Fang V, Feng L, Tsang T, Jiang H, Wu P, Zheng J, #> Lau E, Qin Y, Peng Z, Peiris J, Yu H, Cowling B (2016). “Association #> between the Severity of Influenza A(H7N9) Virus Infections and Length #> of the Incubation Period.” _PLoS One_. doi:10.1371/journal.pone.0148506 #> <https://doi.org/10.1371/journal.pone.0148506>. #> #> Virlogeux V, Yang J, Fang V, Feng L, Tsang T, Jiang H, Wu P, Zheng J, #> Lau E, Qin Y, Peng Z, Peiris J, Yu H, Cowling B (2016). “Association #> between the Severity of Influenza A(H7N9) Virus Infections and Length #> of the Incubation Period.” _PLoS One_. doi:10.1371/journal.pone.0148506 #> <https://doi.org/10.1371/journal.pone.0148506>. #> #> Tuite A, Greer A, Whelan M, Winter A, Lee B, Yan P, Wu J, Moghadas S, #> Buckeridge D, Pourbohloul B, Fisman D (2010). “Estimated epidemiologic #> parameters and morbidity associated with pandemic H1N1 influenza.” #> _Canadian Medical Association Journal_. doi:10.1503/cmaj.091807 #> <https://doi.org/10.1503/cmaj.091807>. #> #> Ghani A, Baguelin M, Griffin J, Flasche S, van Hoek A, Cauchemez S, #> Donnelly C, Robertson C, White M, Truscott J, Fraser C, Garske T, White #> P, Leach S, Hall I, Jenkins H, Ferguson N, Cooper B (2009). “The Early #> Transmission Dynamics of H1N1pdm Influenza in the United Kingdom.” #> _PLoS Currents_. doi:10.1371/currents.RRN1130 #> <https://doi.org/10.1371/currents.RRN1130>. #> #> Ghani A, Baguelin M, Griffin J, Flasche S, van Hoek A, Cauchemez S, #> Donnelly C, Robertson C, White M, Truscott J, Fraser C, Garske T, White #> P, Leach S, Hall I, Jenkins H, Ferguson N, Cooper B (2009). “The Early #> Transmission Dynamics of H1N1pdm Influenza in the United Kingdom.” #> _PLoS Currents_. doi:10.1371/currents.RRN1130 #> <https://doi.org/10.1371/currents.RRN1130>. #> #> Lessler J, Reich N, Cummings D, New York City Department of Health and #> Mental Hygiene Swine Influenza Investigation Team (2009). “Outbreak of #> 2009 Pandemic Influenza A (H1N1) at a New York City School.” _The New #> England Journal of Medicine_. doi:10.1056/NEJMoa0906089 #> <https://doi.org/10.1056/NEJMoa0906089>. #> #> Lessler J, Reich N, Cummings D, New York City Department of Health and #> Mental Hygiene Swine Influenza Investigation Team (2009). “Outbreak of #> 2009 Pandemic Influenza A (H1N1) at a New York City School.” _The New #> England Journal of Medicine_. doi:10.1056/NEJMoa0906089 #> <https://doi.org/10.1056/NEJMoa0906089>. #> #> Pavlin B (2014). “Calculation of incubation period and serial interval #> from multiple outbreaks of Marburg virus disease.” _BMC Research #> Notes_. doi:10.1186/1756-0500-7-906 #> <https://doi.org/10.1186/1756-0500-7-906>. #> #> Pavlin B (2014). “Calculation of incubation period and serial interval #> from multiple outbreaks of Marburg virus disease.” _BMC Research #> Notes_. doi:10.1186/1756-0500-7-906 #> <https://doi.org/10.1186/1756-0500-7-906>. #> #> Pavlin B (2014). “Calculation of incubation period and serial interval #> from multiple outbreaks of Marburg virus disease.” _BMC Research #> Notes_. doi:10.1186/1756-0500-7-906 #> <https://doi.org/10.1186/1756-0500-7-906>. #> #> Colebunders R, Tshomba A, Van Kerkhove M, Bausch D, Campbell P, Libande #> M, Pirard P, Tshioko F, Mardel S, Mulangu S, Sleurs H, Rollin P, #> Muyembe-Tamfum J, Jeffs B, Borchert M, International Scientific and #> Technical Committee 'DRC Watsa/Durba 1999 Marburg Outbreak #> Investigation Group' (2007). “Marburg hemorrhagic fever in Durba and #> Watsa, Democratic Republic of the Congo: clinical documentation, #> features of illness, and treatment.” _The Journal of Infectious #> Diseases_. doi:10.1086/520543 <https://doi.org/10.1086/520543>. #> #> Ajelli M, Merler S (2012). “Transmission Potential and Design of #> Adequate Control Measures for Marburg Hemorrhagic Fever.” _PLoS One_. #> doi:10.1371/journal.pone.0050948 #> <https://doi.org/10.1371/journal.pone.0050948>. #> #> Lloyd-Smith J, Schreiber S, Kopp P, Getz W (2005). “Superspreading and #> the effect of individual variation on disease emergence.” _Nature_. #> doi:10.1038/nature04153 <https://doi.org/10.1038/nature04153>. #> #> Lloyd-Smith J, Schreiber S, Kopp P, Getz W (2005). “Superspreading and #> the effect of individual variation on disease emergence.” _Nature_. #> doi:10.1038/nature04153 <https://doi.org/10.1038/nature04153>. #> #> Lloyd-Smith J, Schreiber S, Kopp P, Getz W (2005). “Superspreading and #> the effect of individual variation on disease emergence.” _Nature_. #> doi:10.1038/nature04153 <https://doi.org/10.1038/nature04153>. #> #> Lloyd-Smith J, Schreiber S, Kopp P, Getz W (2005). “Superspreading and #> the effect of individual variation on disease emergence.” _Nature_. #> doi:10.1038/nature04153 <https://doi.org/10.1038/nature04153>. #> #> Lloyd-Smith J, Schreiber S, Kopp P, Getz W (2005). “Superspreading and #> the effect of individual variation on disease emergence.” _Nature_. #> doi:10.1038/nature04153 <https://doi.org/10.1038/nature04153>. #> #> Lloyd-Smith J, Schreiber S, Kopp P, Getz W (2005). “Superspreading and #> the effect of individual variation on disease emergence.” _Nature_. #> doi:10.1038/nature04153 <https://doi.org/10.1038/nature04153>. #> #> Lloyd-Smith J, Schreiber S, Kopp P, Getz W (2005). “Superspreading and #> the effect of individual variation on disease emergence.” _Nature_. #> doi:10.1038/nature04153 <https://doi.org/10.1038/nature04153>. #> #> Lloyd-Smith J, Schreiber S, Kopp P, Getz W (2005). “Superspreading and #> the effect of individual variation on disease emergence.” _Nature_. #> doi:10.1038/nature04153 <https://doi.org/10.1038/nature04153>. #> #> Lloyd-Smith J, Schreiber S, Kopp P, Getz W (2005). “Superspreading and #> the effect of individual variation on disease emergence.” _Nature_. #> doi:10.1038/nature04153 <https://doi.org/10.1038/nature04153>. #> #> Lloyd-Smith J, Schreiber S, Kopp P, Getz W (2005). “Superspreading and #> the effect of individual variation on disease emergence.” _Nature_. #> doi:10.1038/nature04153 <https://doi.org/10.1038/nature04153>. #> #> Chan M, Johansson M (2012). “The Incubation Periods of Dengue Viruses.” #> _PLoS One_. doi:10.1371/journal.pone.0050972 #> <https://doi.org/10.1371/journal.pone.0050972>. #> #> Chan M, Johansson M (2012). “The Incubation Periods of Dengue Viruses.” #> _PLoS One_. doi:10.1371/journal.pone.0050972 #> <https://doi.org/10.1371/journal.pone.0050972>. #> #> Chan M, Johansson M (2012). “The Incubation Periods of Dengue Viruses.” #> _PLoS One_. doi:10.1371/journal.pone.0050972 #> <https://doi.org/10.1371/journal.pone.0050972>. #> #> Lessler J, Ott C, Carcelen A, Konikoff J, Williamson J, Bi Q, Kucirka #> L, Cummings D, Reich N, Chaisson L (2016). “Times to key events in Zika #> virus infection and implications for blood donation: a systematic #> review.” _Bulletin of the World Health Organization_. #> doi:10.2471/BLT.16.174540 <https://doi.org/10.2471/BLT.16.174540>. #> #> Rudolph K, Lessler J, Moloney R, Kmush B, Cummings D (2014). #> “Incubation periods of mosquito-borne viral infections: a systematic #> review.” _The American Journal of Tropical Medicine and Hygiene_. #> doi:10.4269/ajtmh.13-0403 <https://doi.org/10.4269/ajtmh.13-0403>. #> #> Rudolph K, Lessler J, Moloney R, Kmush B, Cummings D (2014). #> “Incubation periods of mosquito-borne viral infections: a systematic #> review.” _The American Journal of Tropical Medicine and Hygiene_. #> doi:10.4269/ajtmh.13-0403 <https://doi.org/10.4269/ajtmh.13-0403>. #> #> Rudolph K, Lessler J, Moloney R, Kmush B, Cummings D (2014). #> “Incubation periods of mosquito-borne viral infections: a systematic #> review.” _The American Journal of Tropical Medicine and Hygiene_. #> doi:10.4269/ajtmh.13-0403 <https://doi.org/10.4269/ajtmh.13-0403>. #> #> Rudolph K, Lessler J, Moloney R, Kmush B, Cummings D (2014). #> “Incubation periods of mosquito-borne viral infections: a systematic #> review.” _The American Journal of Tropical Medicine and Hygiene_. #> doi:10.4269/ajtmh.13-0403 <https://doi.org/10.4269/ajtmh.13-0403>. #> #> Rudolph K, Lessler J, Moloney R, Kmush B, Cummings D (2014). #> “Incubation periods of mosquito-borne viral infections: a systematic #> review.” _The American Journal of Tropical Medicine and Hygiene_. #> doi:10.4269/ajtmh.13-0403 <https://doi.org/10.4269/ajtmh.13-0403>. #> #> Rudolph K, Lessler J, Moloney R, Kmush B, Cummings D (2014). #> “Incubation periods of mosquito-borne viral infections: a systematic #> review.” _The American Journal of Tropical Medicine and Hygiene_. #> doi:10.4269/ajtmh.13-0403 <https://doi.org/10.4269/ajtmh.13-0403>. #> #> Rudolph K, Lessler J, Moloney R, Kmush B, Cummings D (2014). #> “Incubation periods of mosquito-borne viral infections: a systematic #> review.” _The American Journal of Tropical Medicine and Hygiene_. #> doi:10.4269/ajtmh.13-0403 <https://doi.org/10.4269/ajtmh.13-0403>. #> #> Rudolph K, Lessler J, Moloney R, Kmush B, Cummings D (2014). #> “Incubation periods of mosquito-borne viral infections: a systematic #> review.” _The American Journal of Tropical Medicine and Hygiene_. #> doi:10.4269/ajtmh.13-0403 <https://doi.org/10.4269/ajtmh.13-0403>. #> #> Rudolph K, Lessler J, Moloney R, Kmush B, Cummings D (2014). #> “Incubation periods of mosquito-borne viral infections: a systematic #> review.” _The American Journal of Tropical Medicine and Hygiene_. #> doi:10.4269/ajtmh.13-0403 <https://doi.org/10.4269/ajtmh.13-0403>. #> #> Rudolph K, Lessler J, Moloney R, Kmush B, Cummings D (2014). #> “Incubation periods of mosquito-borne viral infections: a systematic #> review.” _The American Journal of Tropical Medicine and Hygiene_. #> doi:10.4269/ajtmh.13-0403 <https://doi.org/10.4269/ajtmh.13-0403>. #> #> Miura F, van Ewijk C, Backer J, Xiridou M, Franz E, Op de Coul E, #> Brandwagt D, van Cleef B, van Rijckevorsel G, Swaan C, van den Hof S, #> Wallinga J (2022). “Estimated incubation period for monkeypox cases #> confirmed in the Netherlands, May 2022.” _Eurosurveillance_. #> doi:10.2807/1560-7917.ES.2022.27.24.2200448 #> <https://doi.org/10.2807/1560-7917.ES.2022.27.24.2200448>. #> #> Charniga K, Masters N, Slayton R, Gosdin L, Minhaj F, Philpott D, Smith #> D, Gearhart S, Alvarado-Ramy F, Brown C, Waltenburg M, Hughes C, #> Nakazawa Y (2022). “Estimating the incubation period of monkeypox virus #> during the 2022 multi-national outbreak.” _medRxiv_. #> doi:10.1101/2022.06.22.22276713 #> <https://doi.org/10.1101/2022.06.22.22276713>. #> #> Guzzetta G, Mammone A, Ferraro F, Caraglia A, Rapiti A, Marziano V, #> Poletti P, Cereda D, Vairo F, Mattei G, Maraglino F, Rezza G, Merler S #> (2022). “Early Estimates of Monkeypox Incubation Period, Generation #> Time, and Reproduction Number, Italy, May-June 2022.” _Emerging #> Infectious Diseases_. doi:10.3201/eid2810.221126 #> <https://doi.org/10.3201/eid2810.221126>. #> #> Madewell Z, Charniga K, Masters N, Asher J, Fahrenwald L, Still W, Chen #> J, Kipperman N, Bui D, Shea M, Saathoff-Huber L, Johnson S, Harbi K, #> Berns A, Perez T, Gateley E, Spicknall I, Nakazawa Y, Gift T, 2022 #> Monkeypox Outbreak Response Team (2022). “Serial interval and #> incubation period estimates of monkeypox virus infection in 12 U.S. #> jurisdictions, May – August 2022.” _medRxiv_. #> doi:10.1101/2022.10.26.22281516 #> <https://doi.org/10.1101/2022.10.26.22281516>. #> #> Madewell Z, Charniga K, Masters N, Asher J, Fahrenwald L, Still W, Chen #> J, Kipperman N, Bui D, Shea M, Saathoff-Huber L, Johnson S, Harbi K, #> Berns A, Perez T, Gateley E, Spicknall I, Nakazawa Y, Gift T, 2022 #> Monkeypox Outbreak Response Team (2022). “Serial interval and #> incubation period estimates of monkeypox virus infection in 12 U.S. #> jurisdictions, May – August 2023.” _medRxiv_. #> doi:10.1101/2022.10.26.22281516 #> <https://doi.org/10.1101/2022.10.26.22281516>. #> #> Madewell Z, Charniga K, Masters N, Asher J, Fahrenwald L, Still W, Chen #> J, Kipperman N, Bui D, Shea M, Saathoff-Huber L, Johnson S, Harbi K, #> Berns A, Perez T, Gateley E, Spicknall I, Nakazawa Y, Gift T, 2022 #> Monkeypox Outbreak Response Team (2022). “Serial interval and #> incubation period estimates of monkeypox virus infection in 12 U.S. #> jurisdictions, May – August 2024.” _medRxiv_. #> doi:10.1101/2022.10.26.22281516 #> <https://doi.org/10.1101/2022.10.26.22281516>. #> #> Madewell Z, Charniga K, Masters N, Asher J, Fahrenwald L, Still W, Chen #> J, Kipperman N, Bui D, Shea M, Saathoff-Huber L, Johnson S, Harbi K, #> Berns A, Perez T, Gateley E, Spicknall I, Nakazawa Y, Gift T, 2022 #> Monkeypox Outbreak Response Team (2022). “Serial interval and #> incubation period estimates of monkeypox virus infection in 12 U.S. #> jurisdictions, May – August 2025.” _medRxiv_. #> doi:10.1101/2022.10.26.22281516 #> <https://doi.org/10.1101/2022.10.26.22281516>. #> #> Eichner M, Dowell S, Firese N (2011). “Incubation period of ebola #> hemorrhagic virus subtype zaire.” _Osong Public Health and Research #> Perspectives_. doi:10.1016/j.phrp.2011.04.001 #> <https://doi.org/10.1016/j.phrp.2011.04.001>. #> #> The Ebola Outbreak Epidemiology Team, Barry A, Ahuka-Mundeke S, Ali #> Ahmed Y, Allarangar Y, Anoko J, Archer B, Abedi A, Bagaria J, Belizaire #> M, Bhatia S, Bokenge T, Bruni E, Cori A, Dabire E, Diallo A, Diallo B, #> Donnelly C, Dorigatti I, Dorji T, Waeber A, Fall I, Ferguson N, #> FitzJohn R, Tengomo G, Formenty P, Forna A, Fortin A, Garske T, #> Gaythorpe K, Gurry C, Hamblion E, Djingarey M, Haskew C, Hugonnet S, #> Imai N, Impouma B, Kabongo G, Kalenga O, Kibangou E, Lee T, Lukoya C, #> Ly O, Makiala-Mandanda S, Mamba A, Mbala-Kingebeni P, Mboussou F, #> Mlanda T, Makuma V, Morgan O, Mulumba A, Kakoni P, Mukadi-Bamuleka D, #> Muyembe J, Bathé N, Ndumbi Ngamala P, Ngom R, Ngoy G, Nouvellet P, Nsio #> J, Ousman K, Peron E, Polonsky J, Ryan M, Touré A, Towner R, Tshapenda #> G, Van De Weerdt R, Van Kerkhove M, Wendland A, Yao N, Yoti Z, Yuma E, #> Kalambayi Kabamba G, Mwati J, Mbuy G, Lubula L, Mutombo A, Mavila O, #> Lay Y, Kitenge E (2018). “Outbreak of Ebola virus disease in the #> Democratic Republic of the Congo, April–May, 2018: an epidemiological #> study.” _The Lancet_. doi:10.1016/S0140-6736(18)31387-4 #> <https://doi.org/10.1016/S0140-6736%2818%2931387-4>. #> #> WHO Ebola Response Team, Agua-Agum J, Ariyarajah A, Aylward B, Blake I, #> Brennan R, Cori A, Donnelly C, Dorigatti I, Dye C, Eckmanns T, Ferguson #> N, Formenty P, Fraser C, Garcia E, Garske T, Hinsley W, Holmes D, #> Hugonnet S, Iyengar S, Jombart T, Krishnan R, Meijers S, Mills H, #> Mohamed Y, Nedjati-Gilani G, Newton E, Nouvellet P, Pelletier L, #> Perkins D, Riley S, Sagrado M, Schnitzler J, Schumacher D, Shah A, Van #> Kerkhove M, Varsaneux O, Kannangarage N (2015). “West African Ebola #> Epidemic after One Year — Slowing but Not Yet under Control.” _The New #> England Journal of Medicine_. doi:10.1056/NEJMc1414992 #> <https://doi.org/10.1056/NEJMc1414992>. #> #> WHO Ebola Response Team, Agua-Agum J, Ariyarajah A, Aylward B, Blake I, #> Brennan R, Cori A, Donnelly C, Dorigatti I, Dye C, Eckmanns T, Ferguson #> N, Formenty P, Fraser C, Garcia E, Garske T, Hinsley W, Holmes D, #> Hugonnet S, Iyengar S, Jombart T, Krishnan R, Meijers S, Mills H, #> Mohamed Y, Nedjati-Gilani G, Newton E, Nouvellet P, Pelletier L, #> Perkins D, Riley S, Sagrado M, Schnitzler J, Schumacher D, Shah A, Van #> Kerkhove M, Varsaneux O, Kannangarage N (2015). “West African Ebola #> Epidemic after One Year — Slowing but Not Yet under Control.” _The New #> England Journal of Medicine_. doi:10.1056/NEJMc1414992 #> <https://doi.org/10.1056/NEJMc1414992>. #> #> WHO Ebola Response Team, Agua-Agum J, Ariyarajah A, Aylward B, Blake I, #> Brennan R, Cori A, Donnelly C, Dorigatti I, Dye C, Eckmanns T, Ferguson #> N, Formenty P, Fraser C, Garcia E, Garske T, Hinsley W, Holmes D, #> Hugonnet S, Iyengar S, Jombart T, Krishnan R, Meijers S, Mills H, #> Mohamed Y, Nedjati-Gilani G, Newton E, Nouvellet P, Pelletier L, #> Perkins D, Riley S, Sagrado M, Schnitzler J, Schumacher D, Shah A, Van #> Kerkhove M, Varsaneux O, Kannangarage N (2015). “West African Ebola #> Epidemic after One Year — Slowing but Not Yet under Control.” _The New #> England Journal of Medicine_. doi:10.1056/NEJMc1414992 #> <https://doi.org/10.1056/NEJMc1414992>. #> #> WHO Ebola Response Team, Agua-Agum J, Ariyarajah A, Aylward B, Blake I, #> Brennan R, Cori A, Donnelly C, Dorigatti I, Dye C, Eckmanns T, Ferguson #> N, Formenty P, Fraser C, Garcia E, Garske T, Hinsley W, Holmes D, #> Hugonnet S, Iyengar S, Jombart T, Krishnan R, Meijers S, Mills H, #> Mohamed Y, Nedjati-Gilani G, Newton E, Nouvellet P, Pelletier L, #> Perkins D, Riley S, Sagrado M, Schnitzler J, Schumacher D, Shah A, Van #> Kerkhove M, Varsaneux O, Kannangarage N (2015). “West African Ebola #> Epidemic after One Year — Slowing but Not Yet under Control.” _The New #> England Journal of Medicine_. doi:10.1056/NEJMc1414992 #> <https://doi.org/10.1056/NEJMc1414992>. #> #> WHO Ebola Response Team, Agua-Agum J, Ariyarajah A, Aylward B, Blake I, #> Brennan R, Cori A, Donnelly C, Dorigatti I, Dye C, Eckmanns T, Ferguson #> N, Formenty P, Fraser C, Garcia E, Garske T, Hinsley W, Holmes D, #> Hugonnet S, Iyengar S, Jombart T, Krishnan R, Meijers S, Mills H, #> Mohamed Y, Nedjati-Gilani G, Newton E, Nouvellet P, Pelletier L, #> Perkins D, Riley S, Sagrado M, Schnitzler J, Schumacher D, Shah A, Van #> Kerkhove M, Varsaneux O, Kannangarage N (2015). “West African Ebola #> Epidemic after One Year — Slowing but Not Yet under Control.” _The New #> England Journal of Medicine_. doi:10.1056/NEJMc1414992 #> <https://doi.org/10.1056/NEJMc1414992>. #> #> WHO Ebola Response Team, Agua-Agum J, Ariyarajah A, Aylward B, Blake I, #> Brennan R, Cori A, Donnelly C, Dorigatti I, Dye C, Eckmanns T, Ferguson #> N, Formenty P, Fraser C, Garcia E, Garske T, Hinsley W, Holmes D, #> Hugonnet S, Iyengar S, Jombart T, Krishnan R, Meijers S, Mills H, #> Mohamed Y, Nedjati-Gilani G, Newton E, Nouvellet P, Pelletier L, #> Perkins D, Riley S, Sagrado M, Schnitzler J, Schumacher D, Shah A, Van #> Kerkhove M, Varsaneux O, Kannangarage N (2015). “West African Ebola #> Epidemic after One Year — Slowing but Not Yet under Control.” _The New #> England Journal of Medicine_. doi:10.1056/NEJMc1414992 #> <https://doi.org/10.1056/NEJMc1414992>. #> #> WHO Ebola Response Team, Agua-Agum J, Ariyarajah A, Aylward B, Blake I, #> Brennan R, Cori A, Donnelly C, Dorigatti I, Dye C, Eckmanns T, Ferguson #> N, Formenty P, Fraser C, Garcia E, Garske T, Hinsley W, Holmes D, #> Hugonnet S, Iyengar S, Jombart T, Krishnan R, Meijers S, Mills H, #> Mohamed Y, Nedjati-Gilani G, Newton E, Nouvellet P, Pelletier L, #> Perkins D, Riley S, Sagrado M, Schnitzler J, Schumacher D, Shah A, Van #> Kerkhove M, Varsaneux O, Kannangarage N (2015). “West African Ebola #> Epidemic after One Year — Slowing but Not Yet under Control.” _The New #> England Journal of Medicine_. doi:10.1056/NEJMc1414992 #> <https://doi.org/10.1056/NEJMc1414992>. #> #> WHO Ebola Response Team, Agua-Agum J, Ariyarajah A, Aylward B, Blake I, #> Brennan R, Cori A, Donnelly C, Dorigatti I, Dye C, Eckmanns T, Ferguson #> N, Formenty P, Fraser C, Garcia E, Garske T, Hinsley W, Holmes D, #> Hugonnet S, Iyengar S, Jombart T, Krishnan R, Meijers S, Mills H, #> Mohamed Y, Nedjati-Gilani G, Newton E, Nouvellet P, Pelletier L, #> Perkins D, Riley S, Sagrado M, Schnitzler J, Schumacher D, Shah A, Van #> Kerkhove M, Varsaneux O, Kannangarage N (2015). “West African Ebola #> Epidemic after One Year — Slowing but Not Yet under Control.” _The New #> England Journal of Medicine_. doi:10.1056/NEJMc1414992 #> <https://doi.org/10.1056/NEJMc1414992>. #> #> WHO Ebola Response Team, Agua-Agum J, Ariyarajah A, Aylward B, Blake I, #> Brennan R, Cori A, Donnelly C, Dorigatti I, Dye C, Eckmanns T, Ferguson #> N, Formenty P, Fraser C, Garcia E, Garske T, Hinsley W, Holmes D, #> Hugonnet S, Iyengar S, Jombart T, Krishnan R, Meijers S, Mills H, #> Mohamed Y, Nedjati-Gilani G, Newton E, Nouvellet P, Pelletier L, #> Perkins D, Riley S, Sagrado M, Schnitzler J, Schumacher D, Shah A, Van #> Kerkhove M, Varsaneux O, Kannangarage N (2015). “West African Ebola #> Epidemic after One Year — Slowing but Not Yet under Control.” _The New #> England Journal of Medicine_. doi:10.1056/NEJMc1414992 #> <https://doi.org/10.1056/NEJMc1414992>. #> #> WHO Ebola Response Team, Agua-Agum J, Ariyarajah A, Aylward B, Blake I, #> Brennan R, Cori A, Donnelly C, Dorigatti I, Dye C, Eckmanns T, Ferguson #> N, Formenty P, Fraser C, Garcia E, Garske T, Hinsley W, Holmes D, #> Hugonnet S, Iyengar S, Jombart T, Krishnan R, Meijers S, Mills H, #> Mohamed Y, Nedjati-Gilani G, Newton E, Nouvellet P, Pelletier L, #> Perkins D, Riley S, Sagrado M, Schnitzler J, Schumacher D, Shah A, Van #> Kerkhove M, Varsaneux O, Kannangarage N (2015). “West African Ebola #> Epidemic after One Year — Slowing but Not Yet under Control.” _The New #> England Journal of Medicine_. doi:10.1056/NEJMc1414992 #> <https://doi.org/10.1056/NEJMc1414992>. #> #> WHO Ebola Response Team, Agua-Agum J, Ariyarajah A, Aylward B, Blake I, #> Brennan R, Cori A, Donnelly C, Dorigatti I, Dye C, Eckmanns T, Ferguson #> N, Formenty P, Fraser C, Garcia E, Garske T, Hinsley W, Holmes D, #> Hugonnet S, Iyengar S, Jombart T, Krishnan R, Meijers S, Mills H, #> Mohamed Y, Nedjati-Gilani G, Newton E, Nouvellet P, Pelletier L, #> Perkins D, Riley S, Sagrado M, Schnitzler J, Schumacher D, Shah A, Van #> Kerkhove M, Varsaneux O, Kannangarage N (2015). “West African Ebola #> Epidemic after One Year — Slowing but Not Yet under Control.” _The New #> England Journal of Medicine_. doi:10.1056/NEJMc1414992 #> <https://doi.org/10.1056/NEJMc1414992>. #> #> WHO Ebola Response Team, Agua-Agum J, Ariyarajah A, Aylward B, Blake I, #> Brennan R, Cori A, Donnelly C, Dorigatti I, Dye C, Eckmanns T, Ferguson #> N, Formenty P, Fraser C, Garcia E, Garske T, Hinsley W, Holmes D, #> Hugonnet S, Iyengar S, Jombart T, Krishnan R, Meijers S, Mills H, #> Mohamed Y, Nedjati-Gilani G, Newton E, Nouvellet P, Pelletier L, #> Perkins D, Riley S, Sagrado M, Schnitzler J, Schumacher D, Shah A, Van #> Kerkhove M, Varsaneux O, Kannangarage N (2015). “West African Ebola #> Epidemic after One Year — Slowing but Not Yet under Control.” _The New #> England Journal of Medicine_. doi:10.1056/NEJMc1414992 #> <https://doi.org/10.1056/NEJMc1414992>. #> #> WHO Ebola Response Team, Agua-Agum J, Ariyarajah A, Aylward B, Blake I, #> Brennan R, Cori A, Donnelly C, Dorigatti I, Dye C, Eckmanns T, Ferguson #> N, Formenty P, Fraser C, Garcia E, Garske T, Hinsley W, Holmes D, #> Hugonnet S, Iyengar S, Jombart T, Krishnan R, Meijers S, Mills H, #> Mohamed Y, Nedjati-Gilani G, Newton E, Nouvellet P, Pelletier L, #> Perkins D, Riley S, Sagrado M, Schnitzler J, Schumacher D, Shah A, Van #> Kerkhove M, Varsaneux O, Kannangarage N (2015). “West African Ebola #> Epidemic after One Year — Slowing but Not Yet under Control.” _The New #> England Journal of Medicine_. doi:10.1056/NEJMc1414992 #> <https://doi.org/10.1056/NEJMc1414992>. #> #> WHO Ebola Response Team, Agua-Agum J, Ariyarajah A, Aylward B, Blake I, #> Brennan R, Cori A, Donnelly C, Dorigatti I, Dye C, Eckmanns T, Ferguson #> N, Formenty P, Fraser C, Garcia E, Garske T, Hinsley W, Holmes D, #> Hugonnet S, Iyengar S, Jombart T, Krishnan R, Meijers S, Mills H, #> Mohamed Y, Nedjati-Gilani G, Newton E, Nouvellet P, Pelletier L, #> Perkins D, Riley S, Sagrado M, Schnitzler J, Schumacher D, Shah A, Van #> Kerkhove M, Varsaneux O, Kannangarage N (2015). “West African Ebola #> Epidemic after One Year — Slowing but Not Yet under Control.” _The New #> England Journal of Medicine_. doi:10.1056/NEJMc1414992 #> <https://doi.org/10.1056/NEJMc1414992>. #> #> Assiri A, McGeer A, Perl T, Price C, Al Rabeeah A, Cummings D, #> Alabdullatif Z, Assad M, Almulhim A, Makhdoom H, Madani H, Alhakeem R, #> Al-Tawfiq J, Cotten M, Watson S, Kellam P, Zumla A, Memish Z, KSA #> MERSCOV Investigation Team (2013). “Hospital Outbreak of Middle East #> Respiratory Syndrome Coronavirus.” _The New England Journal of #> Medicine_. doi:10.1056/NEJMoa1306742 #> <https://doi.org/10.1056/NEJMoa1306742>. #> #> Assiri A, McGeer A, Perl T, Price C, Al Rabeeah A, Cummings D, #> Alabdullatif Z, Assad M, Almulhim A, Makhdoom H, Madani H, Alhakeem R, #> Al-Tawfiq J, Cotten M, Watson S, Kellam P, Zumla A, Memish Z, KSA #> MERSCOV Investigation Team (2013). “Hospital Outbreak of Middle East #> Respiratory Syndrome Coronavirus.” _The New England Journal of #> Medicine_. doi:10.1056/NEJMoa1306742 #> <https://doi.org/10.1056/NEJMoa1306742>. #> #> Assiri A, McGeer A, Perl T, Price C, Al Rabeeah A, Cummings D, #> Alabdullatif Z, Assad M, Almulhim A, Makhdoom H, Madani H, Alhakeem R, #> Al-Tawfiq J, Cotten M, Watson S, Kellam P, Zumla A, Memish Z, KSA #> MERSCOV Investigation Team (2013). “Hospital Outbreak of Middle East #> Respiratory Syndrome Coronavirus.” _The New England Journal of #> Medicine_. doi:10.1056/NEJMoa1306742 #> <https://doi.org/10.1056/NEJMoa1306742>. #> #> Assiri A, McGeer A, Perl T, Price C, Al Rabeeah A, Cummings D, #> Alabdullatif Z, Assad M, Almulhim A, Makhdoom H, Madani H, Alhakeem R, #> Al-Tawfiq J, Cotten M, Watson S, Kellam P, Zumla A, Memish Z, KSA #> MERSCOV Investigation Team (2013). “Hospital Outbreak of Middle East #> Respiratory Syndrome Coronavirus.” _The New England Journal of #> Medicine_. doi:10.1056/NEJMoa1306742 #> <https://doi.org/10.1056/NEJMoa1306742>. #> #> Assiri A, McGeer A, Perl T, Price C, Al Rabeeah A, Cummings D, #> Alabdullatif Z, Assad M, Almulhim A, Makhdoom H, Madani H, Alhakeem R, #> Al-Tawfiq J, Cotten M, Watson S, Kellam P, Zumla A, Memish Z, KSA #> MERSCOV Investigation Team (2013). “Hospital Outbreak of Middle East #> Respiratory Syndrome Coronavirus.” _The New England Journal of #> Medicine_. doi:10.1056/NEJMoa1306742 #> <https://doi.org/10.1056/NEJMoa1306742>. #> #> Mizumoto K, Endo A, Chowell G, Miyamatsu Y, Saitoh M, Nishiura H #> (2015). “Real-time characterization of risks of death associated with #> the Middle East respiratory syndrome (MERS) in the Republic of Korea, #> 2015.” _BMC Medicine_. doi:10.1186/s12916-015-0468-3 #> <https://doi.org/10.1186/s12916-015-0468-3>. #> #> Cowling B, Park M, Fang V, Wu P, Leung G, Wu J (2015). “Preliminary #> epidemiological assessment of MERS-CoV outbreak in South Korea, May to #> June 2015.” _Eurosurveillance_. #> doi:10.2807/1560-7917.es2015.20.25.21163 #> <https://doi.org/10.2807/1560-7917.es2015.20.25.21163>. #> #> Cowling B, Park M, Fang V, Wu P, Leung G, Wu J (2015). “Preliminary #> epidemiological assessment of MERS-CoV outbreak in South Korea, May to #> June 2016.” _Eurosurveillance_. #> doi:10.2807/1560-7917.es2015.20.25.21163 #> <https://doi.org/10.2807/1560-7917.es2015.20.25.21163>. #> #> Men K, Wang X, Yihao L, Zhang G, Hu J, Gao Y, Han H (2020). “Estimate #> the incubation period of coronavirus 2019 (COVID-19).” _medRxiv_. #> doi:10.1101/2020.02.24.20027474 #> <https://doi.org/10.1101/2020.02.24.20027474>. #> #> Rai B, Shukla A, Dwivedi L (2022). “Incubation period for COVID-19: a #> systematic review and meta-analysis.” _Zeitschrift fur #> Gesundheitswissenschaften_. doi:10.1007/s10389-021-01478-1 #> <https://doi.org/10.1007/s10389-021-01478-1>. #> #> Alene M, Yismaw L, Assemie M, Ketema D, Gietaneh W, Birhan T (2021). #> “Serial interval and incubation period of COVID-19: a systematic review #> and meta-analysis.” _BMC Infectious Diseases_. #> doi:10.1186/s12879-021-05950-x #> <https://doi.org/10.1186/s12879-021-05950-x>. #> #> Alene M, Yismaw L, Assemie M, Ketema D, Gietaneh W, Birhan T (2021). #> “Serial interval and incubation period of COVID-19: a systematic review #> and meta-analysis.” _BMC Infectious Diseases_. #> doi:10.1186/s12879-021-05950-x #> <https://doi.org/10.1186/s12879-021-05950-x>. #> #> Nishiura H, Linton N, Akhmetzhanov A (2020). “Serial interval of novel #> coronavirus (COVID-19) infections.” _International Journal of #> Infectious Diseases_. doi:10.1016/j.ijid.2020.02.060 #> <https://doi.org/10.1016/j.ijid.2020.02.060>. #> #> Nishiura H, Linton N, Akhmetzhanov A (2020). “Serial interval of novel #> coronavirus (COVID-19) infections.” _International Journal of #> Infectious Diseases_. doi:10.1016/j.ijid.2020.02.060 #> <https://doi.org/10.1016/j.ijid.2020.02.060>. #> #> Yang L, Dai J, Zhao J, Wang Y, Deng P, Wang J (2020). “Estimation of #> incubation period and serial interval of COVID-19: analysis of 178 #> cases and 131 transmission chains in Hubei province, China.” #> _Epidemiology and Infection_. doi:10.1017/S0950268820001338 #> <https://doi.org/10.1017/S0950268820001338>. #> #> Yang L, Dai J, Zhao J, Wang Y, Deng P, Wang J (2020). “Estimation of #> incubation period and serial interval of COVID-19: analysis of 178 #> cases and 131 transmission chains in Hubei province, China.” #> _Epidemiology and Infection_. doi:10.1017/S0950268820001338 #> <https://doi.org/10.1017/S0950268820001338>. #> #> Elias C, Sekri A, Leblanc P, Cucherat M, Vanhems P (2021). “The #> incubation period of COVID-19: A meta-analysis.” _International Journal #> of Infectious Diseases_. doi:10.1016/j.ijid.2021.01.069 #> <https://doi.org/10.1016/j.ijid.2021.01.069>. #> #> Bui L, Nguyen H, Levine H, Nguyen H, Nguyen T, Nguyen T, Nguyen T, Do #> T, Pham N, Bui M (2020). “Estimation of the incubation period of #> COVID-19 in Vietnam.” _PLoS One_. doi:10.1371/journal.pone.0243889 #> <https://doi.org/10.1371/journal.pone.0243889>. #> #> McAloon C, Collins Á, Hunt K, Barber A, Byrne A, Butler F, Casey M, #> Griffin J, Lane E, McEvoy D, Wall P, Green M, O'Grady L, More S (2020). #> “Incubation period of COVID-19: a rapid systematic review and #> meta-analysis of observational research.” _BMJ Open_. #> doi:10.1136/bmjopen-2020-039652 #> <https://doi.org/10.1136/bmjopen-2020-039652>. #> #> McAloon C, Collins Á, Hunt K, Barber A, Byrne A, Butler F, Casey M, #> Griffin J, Lane E, McEvoy D, Wall P, Green M, O'Grady L, More S (2020). #> “Incubation period of COVID-19: a rapid systematic review and #> meta-analysis of observational research.” _BMJ Open_. #> doi:10.1136/bmjopen-2020-039652 #> <https://doi.org/10.1136/bmjopen-2020-039652>. #> #> Linton N, Kobayashi T, Yang Y, Hayashi K, Akhmetzhanov A, Jung S, Yuan #> B, Kinoshita R, Nishiura H (2020). “Incubation Period and Other #> Epidemiological Characteristics of 2019 Novel Coronavirus Infections #> with Right Truncation: A Statistical Analysis of Publicly Available #> Case Data.” _Journal of Clinical Medicine_. doi:10.3390/jcm9020538 #> <https://doi.org/10.3390/jcm9020538>. #> #> Linton N, Kobayashi T, Yang Y, Hayashi K, Akhmetzhanov A, Jung S, Yuan #> B, Kinoshita R, Nishiura H (2020). “Incubation Period and Other #> Epidemiological Characteristics of 2019 Novel Coronavirus Infections #> with Right Truncation: A Statistical Analysis of Publicly Available #> Case Data.” _Journal of Clinical Medicine_. doi:10.3390/jcm9020538 #> <https://doi.org/10.3390/jcm9020538>. #> #> Linton N, Kobayashi T, Yang Y, Hayashi K, Akhmetzhanov A, Jung S, Yuan #> B, Kinoshita R, Nishiura H (2020). “Incubation Period and Other #> Epidemiological Characteristics of 2019 Novel Coronavirus Infections #> with Right Truncation: A Statistical Analysis of Publicly Available #> Case Data.” _Journal of Clinical Medicine_. doi:10.3390/jcm9020538 #> <https://doi.org/10.3390/jcm9020538>. #> #> Linton N, Kobayashi T, Yang Y, Hayashi K, Akhmetzhanov A, Jung S, Yuan #> B, Kinoshita R, Nishiura H (2020). “Incubation Period and Other #> Epidemiological Characteristics of 2019 Novel Coronavirus Infections #> with Right Truncation: A Statistical Analysis of Publicly Available #> Case Data.” _Journal of Clinical Medicine_. doi:10.3390/jcm9020538 #> <https://doi.org/10.3390/jcm9020538>. #> #> Linton N, Kobayashi T, Yang Y, Hayashi K, Akhmetzhanov A, Jung S, Yuan #> B, Kinoshita R, Nishiura H (2020). “Incubation Period and Other #> Epidemiological Characteristics of 2019 Novel Coronavirus Infections #> with Right Truncation: A Statistical Analysis of Publicly Available #> Case Data.” _Journal of Clinical Medicine_. doi:10.3390/jcm9020538 #> <https://doi.org/10.3390/jcm9020538>. #> #> Linton N, Kobayashi T, Yang Y, Hayashi K, Akhmetzhanov A, Jung S, Yuan #> B, Kinoshita R, Nishiura H (2020). “Incubation Period and Other #> Epidemiological Characteristics of 2019 Novel Coronavirus Infections #> with Right Truncation: A Statistical Analysis of Publicly Available #> Case Data.” _Journal of Clinical Medicine_. doi:10.3390/jcm9020538 #> <https://doi.org/10.3390/jcm9020538>. #> #> Linton N, Kobayashi T, Yang Y, Hayashi K, Akhmetzhanov A, Jung S, Yuan #> B, Kinoshita R, Nishiura H (2020). “Incubation Period and Other #> Epidemiological Characteristics of 2019 Novel Coronavirus Infections #> with Right Truncation: A Statistical Analysis of Publicly Available #> Case Data.” _Journal of Clinical Medicine_. doi:10.3390/jcm9020538 #> <https://doi.org/10.3390/jcm9020538>. #> #> Linton N, Kobayashi T, Yang Y, Hayashi K, Akhmetzhanov A, Jung S, Yuan #> B, Kinoshita R, Nishiura H (2020). “Incubation Period and Other #> Epidemiological Characteristics of 2019 Novel Coronavirus Infections #> with Right Truncation: A Statistical Analysis of Publicly Available #> Case Data.” _Journal of Clinical Medicine_. doi:10.3390/jcm9020538 #> <https://doi.org/10.3390/jcm9020538>. #> #> Linton N, Kobayashi T, Yang Y, Hayashi K, Akhmetzhanov A, Jung S, Yuan #> B, Kinoshita R, Nishiura H (2020). “Incubation Period and Other #> Epidemiological Characteristics of 2019 Novel Coronavirus Infections #> with Right Truncation: A Statistical Analysis of Publicly Available #> Case Data.” _Journal of Clinical Medicine_. doi:10.3390/jcm9020538 #> <https://doi.org/10.3390/jcm9020538>. #> #> Linton N, Kobayashi T, Yang Y, Hayashi K, Akhmetzhanov A, Jung S, Yuan #> B, Kinoshita R, Nishiura H (2020). “Incubation Period and Other #> Epidemiological Characteristics of 2019 Novel Coronavirus Infections #> with Right Truncation: A Statistical Analysis of Publicly Available #> Case Data.” _Journal of Clinical Medicine_. doi:10.3390/jcm9020538 #> <https://doi.org/10.3390/jcm9020538>. #> #> Linton N, Kobayashi T, Yang Y, Hayashi K, Akhmetzhanov A, Jung S, Yuan #> B, Kinoshita R, Nishiura H (2020). “Incubation Period and Other #> Epidemiological Characteristics of 2019 Novel Coronavirus Infections #> with Right Truncation: A Statistical Analysis of Publicly Available #> Case Data.” _Journal of Clinical Medicine_. doi:10.3390/jcm9020538 #> <https://doi.org/10.3390/jcm9020538>. #> #> Lauer S, Grantz K, Bi Q, Jones F, Zheng Q, Meredith H, Azman A, Reich #> N, Lessler J (2020). “The Incubation Period of Coronavirus Disease 2019 #> (COVID-19) From Publicly Reported Confirmed Cases: Estimation and #> Application.” _Annals of Internal Medicine_. doi:10.7326/M20-0504 #> <https://doi.org/10.7326/M20-0504>. #> #> Lauer S, Grantz K, Bi Q, Jones F, Zheng Q, Meredith H, Azman A, Reich #> N, Lessler J (2020). “The Incubation Period of Coronavirus Disease 2019 #> (COVID-19) From Publicly Reported Confirmed Cases: Estimation and #> Application.” _Annals of Internal Medicine_. doi:10.7326/M20-0504 #> <https://doi.org/10.7326/M20-0504>. #> #> Lauer S, Grantz K, Bi Q, Jones F, Zheng Q, Meredith H, Azman A, Reich #> N, Lessler J (2020). “The Incubation Period of Coronavirus Disease 2019 #> (COVID-19) From Publicly Reported Confirmed Cases: Estimation and #> Application.” _Annals of Internal Medicine_. doi:10.7326/M20-0504 #> <https://doi.org/10.7326/M20-0504>. #> #> Lauer S, Grantz K, Bi Q, Jones F, Zheng Q, Meredith H, Azman A, Reich #> N, Lessler J (2020). “The Incubation Period of Coronavirus Disease 2019 #> (COVID-19) From Publicly Reported Confirmed Cases: Estimation and #> Application.” _Annals of Internal Medicine_. doi:10.7326/M20-0504 #> <https://doi.org/10.7326/M20-0504>. #> #> Guo Z, Zhao S, Sun S, He D, Chong K, Yeoh E (2022). “Estimation of the #> serial interval of monkeypox during the early outbreak in 2022.” #> _Journal of Medical Virology_. doi:10.1002/jmv.28248 #> <https://doi.org/10.1002/jmv.28248>. #> #> Wang S, Zhang F, Yuan Z, Xu M, Wang Z, Gao C, Guo R, Du Z (2022). #> “Serial intervals and incubation periods of the monkeypox virus #> clades.” _Journal of Travel Medicine_. doi:10.1093/jtm/taac105 #> <https://doi.org/10.1093/jtm/taac105>. #> #> Wang S, Zhang F, Yuan Z, Xu M, Wang Z, Gao C, Guo R, Du Z (2022). #> “Serial intervals and incubation periods of the monkeypox virus #> clades.” _Journal of Travel Medicine_. doi:10.1093/jtm/taac105 #> <https://doi.org/10.1093/jtm/taac105>. #> #> Wang S, Zhang F, Yuan Z, Xu M, Wang Z, Gao C, Guo R, Du Z (2022). #> “Serial intervals and incubation periods of the monkeypox virus #> clades.” _Journal of Travel Medicine_. doi:10.1093/jtm/taac105 #> <https://doi.org/10.1093/jtm/taac105>. #> #> Wang S, Zhang F, Yuan Z, Xu M, Wang Z, Gao C, Guo R, Du Z (2022). #> “Serial intervals and incubation periods of the monkeypox virus #> clades.” _Journal of Travel Medicine_. doi:10.1093/jtm/taac105 #> <https://doi.org/10.1093/jtm/taac105>. #> #> Wang S, Zhang F, Yuan Z, Xu M, Wang Z, Gao C, Guo R, Du Z (2022). #> “Serial intervals and incubation periods of the monkeypox virus #> clades.” _Journal of Travel Medicine_. doi:10.1093/jtm/taac105 #> <https://doi.org/10.1093/jtm/taac105>. #> #> Wei F, Peng Z, Jin Z, Wang J, Xu X, Zhang X, Xu J, Ren Z, Bai Y, Wang #> X, Lu B, Wang Z, Xu J, Huang S (2022). “Study and prediction of the #> 2022 global monkeypox epidemic.” _Journal of Biosafety and #> Biosecurity_. doi:10.1016/j.jobb.2022.12.001 #> <https://doi.org/10.1016/j.jobb.2022.12.001>. #> #> Wei F, Peng Z, Jin Z, Wang J, Xu X, Zhang X, Xu J, Ren Z, Bai Y, Wang #> X, Lu B, Wang Z, Xu J, Huang S (2022). “Study and prediction of the #> 2022 global monkeypox epidemic.” _Journal of Biosafety and #> Biosecurity_. doi:10.1016/j.jobb.2022.12.001 #> <https://doi.org/10.1016/j.jobb.2022.12.001>. #> #> Wei F, Peng Z, Jin Z, Wang J, Xu X, Zhang X, Xu J, Ren Z, Bai Y, Wang #> X, Lu B, Wang Z, Xu J, Huang S (2022). “Study and prediction of the #> 2022 global monkeypox epidemic.” _Journal of Biosafety and #> Biosecurity_. doi:10.1016/j.jobb.2022.12.001 #> <https://doi.org/10.1016/j.jobb.2022.12.001>. #> #> Salje H, Cauchemez S, Alera M, Rodriguez-Barraquer I, Thaisomboonsuk B, #> Srikiatkhachorn A, Lago C, Villa D, Klungthong C, Tac-An I, Fernandez #> S, Velasco J, Roque Jr V, Nisalak A, Macareo L, Levy J, Cummings D, #> Yoon I (2015). “Reconstruction of 60 Years of Chikungunya Epidemiology #> in the Philippines Demonstrates Episodic and Focal Transmission.” _The #> Journal of Infectious Diseases_. doi:10.1093/infdis/jiv470 #> <https://doi.org/10.1093/infdis/jiv470>. #> #> Guzzetta G, Vairo F, Mammone A, Lanini S, Poletti P, Manica M, Rosa R, #> Caputo B, Solimini A, della Torre A, Scognamiglio P, Zumla A, Ippolito #> G, Merler S (2020). “Spatial modes for transmission of chikungunya #> virus during a large chikungunya outbreak in Italy: a modeling #> analysis.” _BMC Medicine_. doi:10.1186/s12916-020-01674-y #> <https://doi.org/10.1186/s12916-020-01674-y>. #> #> de Souza W, de Lima S, Mello L, Candido D, Buss L, Whittaker C, Claro #> I, Chandradeva N, Granja F, de Jesus R, Lemos P, Toledo-Teixeira D, #> Barbosa P, Firmino A, Amorim M, Duarte L, Pessoa Jr I, Forato J, #> Vasconcelos I, Maximo A, Araújo E, Mello L, Sabino E, Proença-Módena J, #> Faria N, Weaver S (2023). “Spatiotemporal dynamics and recurrence of #> chikungunya virus in Brazil: an epidemiological study.” _The Lancet #> Microbe_. doi:10.1016/S2666-5247(23)00033-2 #> <https://doi.org/10.1016/S2666-5247%2823%2900033-2>."},{"path":"https://epiverse-trace.github.io/epiparameter/reference/get_parameters.epiparameter.html","id":null,"dir":"Reference","previous_headings":"","what":"Get parameters from an <epiparameter> object — get_parameters.epiparameter","title":"Get parameters from an <epiparameter> object — get_parameters.epiparameter","text":"Extract parameters distribution stored <epiparameter> object.","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/get_parameters.epiparameter.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Get parameters from an <epiparameter> object — get_parameters.epiparameter","text":"","code":"# S3 method for class 'epiparameter' get_parameters(x, ...) # S3 method for class 'multi_epiparameter' get_parameters(x, ...)"},{"path":"https://epiverse-trace.github.io/epiparameter/reference/get_parameters.epiparameter.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Get parameters from an <epiparameter> object — get_parameters.epiparameter","text":"x <epiparameter> object. ... dots used, extra arguments supplied cause warning.","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/get_parameters.epiparameter.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Get parameters from an <epiparameter> object — get_parameters.epiparameter","text":"named vector parameters NA <epiparameter> object unparameterised.","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/get_parameters.epiparameter.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Get parameters from an <epiparameter> object — get_parameters.epiparameter","text":"<epiparameter> object can unparameterised lacks probability distribution parameters probability distribution, case get_parameters.epiparameter() method return NA.","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/get_parameters.epiparameter.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Get parameters from an <epiparameter> object — get_parameters.epiparameter","text":"","code":"ep <- epiparameter_db( disease = \"COVID-19\", epi_name = \"serial interval\", single_epiparameter = TRUE ) #> Using Nishiura H, Linton N, Akhmetzhanov A (2020). “Serial interval of novel #> coronavirus (COVID-19) infections.” _International Journal of #> Infectious Diseases_. doi:10.1016/j.ijid.2020.02.060 #> <https://doi.org/10.1016/j.ijid.2020.02.060>.. #> To retrieve the citation use the 'get_citation' function get_parameters(ep) #> meanlog sdlog #> 1.3862617 0.5679803"},{"path":"https://epiverse-trace.github.io/epiparameter/reference/get_parameters.html","id":null,"dir":"Reference","previous_headings":"","what":"Get parameters from an object — get_parameters","title":"Get parameters from an object — get_parameters","text":"Extract parameters stored R object.","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/get_parameters.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Get parameters from an object — get_parameters","text":"","code":"get_parameters(x, ...)"},{"path":"https://epiverse-trace.github.io/epiparameter/reference/get_parameters.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Get parameters from an object — get_parameters","text":"x object used select method. ... dots Extra arguments passed method.","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/is_continuous.html","id":null,"dir":"Reference","previous_headings":"","what":"Check if distribution in <epiparameter> is continuous — is_continuous","title":"Check if distribution in <epiparameter> is continuous — is_continuous","text":"Check distribution <epiparameter> continuous","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/is_continuous.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Check if distribution in <epiparameter> is continuous — is_continuous","text":"","code":"is_continuous(x)"},{"path":"https://epiverse-trace.github.io/epiparameter/reference/is_continuous.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Check if distribution in <epiparameter> is continuous — is_continuous","text":"x <epiparameter> object.","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/is_continuous.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Check if distribution in <epiparameter> is continuous — is_continuous","text":"boolean logical.","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/is_continuous.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Check if distribution in <epiparameter> is continuous — is_continuous","text":"<epiparameter> class can hold <distribution> <distcrete> probability distribution objects distributional package distcrete package, respectively. <distribution> objects can continuous discrete distributions (e.g. gamma negative binomial), <distcrete> objects discrete.","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/is_continuous.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Check if distribution in <epiparameter> is continuous — is_continuous","text":"","code":"ep <- epiparameter( disease = \"ebola\", epi_name = \"incubation_period\", prob_distribution = create_prob_distribution( prob_distribution = \"lnorm\", prob_distribution_params = c(meanlog = 1, sdlog = 1) ) ) #> Citation cannot be created as author, year, journal or title is missing is_continuous(ep) #> [1] TRUE is_continuous(discretise(ep)) #> [1] FALSE ep <- epiparameter( disease = \"ebola\", epi_name = \"offspring distribution\", prob_distribution = create_prob_distribution( prob_distribution = \"nbinom\", prob_distribution_params = c(mean = 2, dispersion = 0.5) ) ) #> Citation cannot be created as author, year, journal or title is missing is_continuous(ep) #> [1] FALSE"},{"path":"https://epiverse-trace.github.io/epiparameter/reference/is_epiparameter.html","id":null,"dir":"Reference","previous_headings":"","what":"Check object is an <epiparameter> — is_epiparameter","title":"Check object is an <epiparameter> — is_epiparameter","text":"Check object <epiparameter>","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/is_epiparameter.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Check object is an <epiparameter> — is_epiparameter","text":"","code":"is_epiparameter(x)"},{"path":"https://epiverse-trace.github.io/epiparameter/reference/is_epiparameter.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Check object is an <epiparameter> — is_epiparameter","text":"x R object.","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/is_epiparameter.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Check object is an <epiparameter> — is_epiparameter","text":"boolean logical, TRUE object <epiparameter> FALSE .","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/is_epiparameter.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Check object is an <epiparameter> — is_epiparameter","text":"","code":"ep <- epiparameter( disease = \"ebola\", epi_name = \"serial_interval\", prob_distribution = create_prob_distribution( prob_distribution = \"gamma\", prob_distribution_params = c(shape = 1, scale = 1) ) ) #> Citation cannot be created as author, year, journal or title is missing is_epiparameter(ep) #> [1] TRUE false_ep <- list( disease = \"ebola\", epi_name = \"serial_interval\", prob_distribution = \"gamma\", prob_distribution_params = c(shape = 1, scale = 1) ) is_epiparameter(false_ep) #> [1] FALSE"},{"path":"https://epiverse-trace.github.io/epiparameter/reference/is_epiparameter_df.html","id":null,"dir":"Reference","previous_headings":"","what":"Check if <data.frame> input is from as.data.frame.epiparameter() — is_epiparameter_df","title":"Check if <data.frame> input is from as.data.frame.epiparameter() — is_epiparameter_df","text":"Check <data.frame> input .data.frame.epiparameter()","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/is_epiparameter_df.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Check if <data.frame> input is from as.data.frame.epiparameter() — is_epiparameter_df","text":"","code":"is_epiparameter_df(x)"},{"path":"https://epiverse-trace.github.io/epiparameter/reference/is_epiparameter_df.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Check if <data.frame> input is from as.data.frame.epiparameter() — is_epiparameter_df","text":"x <data.frame>.","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/is_epiparameter_df.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Check if <data.frame> input is from as.data.frame.epiparameter() — is_epiparameter_df","text":"single logical boolean.","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/is_epiparameter_params.html","id":null,"dir":"Reference","previous_headings":"","what":"Check whether the vector of parameters for the probability distribution are in the set of possible parameters used in the epiparameter package — is_epiparameter_params","title":"Check whether the vector of parameters for the probability distribution are in the set of possible parameters used in the epiparameter package — is_epiparameter_params","text":"Check whether vector parameters probability distribution set possible parameters used epiparameter package","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/is_epiparameter_params.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Check whether the vector of parameters for the probability distribution are in the set of possible parameters used in the epiparameter package — is_epiparameter_params","text":"","code":"is_epiparameter_params(prob_distribution, prob_distribution_params)"},{"path":"https://epiverse-trace.github.io/epiparameter/reference/is_epiparameter_params.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Check whether the vector of parameters for the probability distribution are in the set of possible parameters used in the epiparameter package — is_epiparameter_params","text":"prob_distribution character string specifying probability distribution. match R naming convention probability distributions (e.g. lognormal lnorm, negative binomial nbinom, geometric geom). prob_distribution_params named vector probability distribution parameters.","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/is_epiparameter_params.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Check whether the vector of parameters for the probability distribution are in the set of possible parameters used in the epiparameter package — is_epiparameter_params","text":"boolean logical.","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/is_epiparameter_params.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Check whether the vector of parameters for the probability distribution are in the set of possible parameters used in the epiparameter package — is_epiparameter_params","text":"check valid parameters independent whether distribution truncated discretised.","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/is_epiparameter_params.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Check whether the vector of parameters for the probability distribution are in the set of possible parameters used in the epiparameter package — is_epiparameter_params","text":"","code":"is_epiparameter_params( prob_distribution = \"gamma\", prob_distribution_params = c(shape = 2, scale = 1) ) #> [1] TRUE"},{"path":"https://epiverse-trace.github.io/epiparameter/reference/is_parameterised.html","id":null,"dir":"Reference","previous_headings":"","what":"Check if <epiparameter> or list of <epiparameter> objects contains a distribution and distribution parameters — is_parameterised","title":"Check if <epiparameter> or list of <epiparameter> objects contains a distribution and distribution parameters — is_parameterised","text":"Check <epiparameter> list <epiparameter> objects contains distribution distribution parameters","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/is_parameterised.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Check if <epiparameter> or list of <epiparameter> objects contains a distribution and distribution parameters — is_parameterised","text":"","code":"is_parameterised(x, ...) is_parameterized(x, ...) # S3 method for class 'epiparameter' is_parameterised(x, ...) # S3 method for class 'multi_epiparameter' is_parameterised(x, ...)"},{"path":"https://epiverse-trace.github.io/epiparameter/reference/is_parameterised.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Check if <epiparameter> or list of <epiparameter> objects contains a distribution and distribution parameters — is_parameterised","text":"x <epiparameter> list <epiparameter> objects. ... dots Extra arguments passed method.","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/is_parameterised.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Check if <epiparameter> or list of <epiparameter> objects contains a distribution and distribution parameters — is_parameterised","text":"single boolean logical <epiparameter> vector logicals equal length list <epiparameter> objects input. <epiparameter> object missing either probability distribution parameters probability distribution returns FALSE, otherwise returns TRUE.","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/is_parameterised.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Check if <epiparameter> or list of <epiparameter> objects contains a distribution and distribution parameters — is_parameterised","text":"","code":"# parameterised <epiparameter> ep <- epiparameter( disease = \"ebola\", epi_name = \"incubation\", prob_distribution = create_prob_distribution( prob_distribution = \"gamma\", prob_distribution_params = c(shape = 1, scale = 1) ) ) #> Citation cannot be created as author, year, journal or title is missing is_parameterised(ep) #> [1] TRUE # unparameterised <epiparameter> ep <- epiparameter( disease = \"ebola\", epi_name = \"incubation\" ) #> Citation cannot be created as author, year, journal or title is missing #> No adequate summary statistics available to calculate the parameters of the NA distribution #> Unparameterised <epiparameter> object is_parameterised(ep) #> [1] FALSE # list of <epiparameter> db <- epiparameter_db() #> Returning 125 results that match the criteria (100 are parameterised). #> Use subset to filter by entry variables or single_epiparameter to return a single entry. #> To retrieve the citation for each use the 'get_citation' function is_parameterised(db) #> [1] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE #> [13] TRUE TRUE TRUE TRUE TRUE TRUE FALSE TRUE TRUE FALSE TRUE TRUE #> [25] TRUE TRUE FALSE FALSE FALSE FALSE TRUE TRUE TRUE TRUE TRUE TRUE #> [37] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE #> [49] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE #> [61] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE #> [73] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE FALSE FALSE FALSE TRUE #> [85] FALSE TRUE FALSE FALSE FALSE FALSE TRUE TRUE TRUE TRUE FALSE TRUE #> [97] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE #> [109] TRUE TRUE TRUE TRUE TRUE TRUE FALSE FALSE FALSE FALSE FALSE FALSE #> [121] FALSE FALSE FALSE TRUE FALSE"},{"path":"https://epiverse-trace.github.io/epiparameter/reference/is_truncated.html","id":null,"dir":"Reference","previous_headings":"","what":"Check if distribution in <epiparameter> is truncated — is_truncated","title":"Check if distribution in <epiparameter> is truncated — is_truncated","text":"Check distribution <epiparameter> truncated","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/is_truncated.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Check if distribution in <epiparameter> is truncated — is_truncated","text":"","code":"is_truncated(x)"},{"path":"https://epiverse-trace.github.io/epiparameter/reference/is_truncated.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Check if distribution in <epiparameter> is truncated — is_truncated","text":"x <epiparameter> object.","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/is_truncated.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Check if distribution in <epiparameter> is truncated — is_truncated","text":"boolean logical.","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/is_truncated.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Check if distribution in <epiparameter> is truncated — is_truncated","text":"<epiparameter> class can hold probability distribution objects {distributional} package {distcrete} package, however, distribution objects {distributional} can truncated. <epiparameter> object <distcrete> object is_truncated return FALSE default.","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/is_truncated.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Check if distribution in <epiparameter> is truncated — is_truncated","text":"","code":"ep <- epiparameter( disease = \"ebola\", epi_name = \"incubation_period\", prob_distribution = create_prob_distribution( prob_distribution = \"lnorm\", prob_distribution_params = c(meanlog = 1, sdlog = 1) ) ) #> Citation cannot be created as author, year, journal or title is missing is_truncated(ep) #> [1] FALSE ep <- epiparameter( disease = \"ebola\", epi_name = \"incubation_period\", prob_distribution = create_prob_distribution( prob_distribution = \"lnorm\", prob_distribution_params = c(meanlog = 1, sdlog = 1), truncation = 10 ) ) #> Citation cannot be created as author, year, journal or title is missing is_truncated(ep) #> [1] TRUE"},{"path":"https://epiverse-trace.github.io/epiparameter/reference/mean.epiparameter.html","id":null,"dir":"Reference","previous_headings":"","what":"Mean method for <epiparameter> class — mean.epiparameter","title":"Mean method for <epiparameter> class — mean.epiparameter","text":"Mean method <epiparameter> class","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/mean.epiparameter.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Mean method for <epiparameter> class — mean.epiparameter","text":"","code":"# S3 method for class 'epiparameter' mean(x, ...)"},{"path":"https://epiverse-trace.github.io/epiparameter/reference/mean.epiparameter.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Mean method for <epiparameter> class — mean.epiparameter","text":"x <epiparameter> object. ... dots used, extra arguments supplied cause warning.","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/mean.epiparameter.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Mean method for <epiparameter> class — mean.epiparameter","text":"numeric mean distribution NA.","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/mean.epiparameter.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Mean method for <epiparameter> class — mean.epiparameter","text":"","code":"ep <- epiparameter_db( disease = \"COVID-19\", epi_name = \"incubation period\", single_epiparameter = TRUE ) #> Using Linton N, Kobayashi T, Yang Y, Hayashi K, Akhmetzhanov A, Jung S, Yuan #> B, Kinoshita R, Nishiura H (2020). “Incubation Period and Other #> Epidemiological Characteristics of 2019 Novel Coronavirus Infections #> with Right Truncation: A Statistical Analysis of Publicly Available #> Case Data.” _Journal of Clinical Medicine_. doi:10.3390/jcm9020538 #> <https://doi.org/10.3390/jcm9020538>.. #> To retrieve the citation use the 'get_citation' function mean(ep) #> [1] 5.6"},{"path":"https://epiverse-trace.github.io/epiparameter/reference/new_epiparameter.html","id":null,"dir":"Reference","previous_headings":"","what":"Constructor for <epiparameter> class — new_epiparameter","title":"Constructor for <epiparameter> class — new_epiparameter","text":"Create <epiparameter> object. constructor search whether parameters probability distribution supplied look see whether can inferred/extracted/ converted summary statistics provided. also convert probability distribution (prob_dist) parameters (prob_dist_params) S3 class, either distribution object {distributional} discretise = FALSE, distcrete object {distcrete} discretise = TRUE.","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/new_epiparameter.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Constructor for <epiparameter> class — new_epiparameter","text":"","code":"new_epiparameter( disease = character(), pathogen = character(), epi_name = character(), prob_distribution = list(), uncertainty = list(), summary_stats = list(), citation = character(), metadata = list(), method_assess = list(), notes = character(), auto_calc_params = logical(), ... )"},{"path":"https://epiverse-trace.github.io/epiparameter/reference/new_epiparameter.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Constructor for <epiparameter> class — new_epiparameter","text":"disease character string name infectious disease. pathogen character string name causative agent disease, NA known. epi_name character string name epidemiological parameter type. prob_distribution S3 class containing probability distribution character string parameters probability distribution unknown name distribution known, NA distribution name parameters unknown. Use create_prob_distribution() create prob_distribution. uncertainty list named vectors uncertainty around probability distribution parameters. uncertainty around parameter estimates unknown use create_uncertainty() (argument default) create list correct names missing values. summary_stats list summary statistics, use create_summary_stats() create list. list can include summary statistics inferred distribution mean standard deviation, quantiles distribution, information data used fit distribution lower upper range. summary statistics can also include uncertainty around metrics confidence interval around mean standard deviation. citation <bibentry> citation source data paper inferred distribution parameters, use create_citation() create citation. metadata list metadata, can include: units, sample size, transmission mode disease (e.g. vector-borne directly transmitted), etc. assumed disease vector-borne distribution intrinsic (e.g. extrinsic delay distribution extrinsic incubation period) unless transmission_mode = \"vector_borne\" contained metadata. Use create_metadata() create metadata. method_assess list methodological aspects used fitting distribution, use create_method_assess() create method assessment. notes character string additional information data, inference method disease. auto_calc_params boolean logical determining whether try calculate probability distribution parameters summary statistics distribution parameters provided. Default TRUE. case sufficient summary statistics provided parameter(s) distribution , .calc_dist_params() function called calculate parameters add epiparameter object created. ... dots Extra arguments passed internal functions. commonly used pass arguments distcrete::distcrete() construct discretised distribution S3 object. see arguments can adjusted discretised distributions see distcrete::distcrete().","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/new_epiparameter.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Constructor for <epiparameter> class — new_epiparameter","text":"<epiparameter> object.","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/parameter_tbl.html","id":null,"dir":"Reference","previous_headings":"","what":"Table of epidemiological distributions — parameter_tbl","title":"Table of epidemiological distributions — parameter_tbl","text":"function subsets epidemiological parameter library return chosen epidemiological distribution. results returned data frame containing disease, epidemiological distribution, probability distribution, author study, year publication.","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/parameter_tbl.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Table of epidemiological distributions — parameter_tbl","text":"","code":"parameter_tbl( multi_epiparameter, disease = \"all\", pathogen = \"all\", epi_name = \"all\" )"},{"path":"https://epiverse-trace.github.io/epiparameter/reference/parameter_tbl.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Table of epidemiological distributions — parameter_tbl","text":"multi_epiparameter Either <epiparameter> object list <epiparameter> objects. disease character string name infectious disease. pathogen character string name causative agent disease, NA known. epi_name character string name epidemiological parameter type.","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/parameter_tbl.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Table of epidemiological distributions — parameter_tbl","text":"<parameter_tbl> object subclass <data.frame>.","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/parameter_tbl.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Table of epidemiological distributions — parameter_tbl","text":"Joshua W. Lambert, Adam Kucharski","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/parameter_tbl.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Table of epidemiological distributions — parameter_tbl","text":"","code":"epiparameter_list <- epiparameter_db(disease = \"COVID-19\") #> Returning 27 results that match the criteria (22 are parameterised). #> Use subset to filter by entry variables or single_epiparameter to return a single entry. #> To retrieve the citation for each use the 'get_citation' function parameter_tbl(multi_epiparameter = epiparameter_list) #> # Parameter table: #> # A data frame: 27 × 7 #> disease pathogen epi_name prob_distribution author year sample_size #> <chr> <chr> <chr> <chr> <chr> <dbl> <dbl> #> 1 COVID-19 SARS-CoV-2 incubation pe… NA Men e… 2020 59 #> 2 COVID-19 SARS-CoV-2 incubation pe… NA Rai e… 2022 6241 #> 3 COVID-19 SARS-CoV-2 incubation pe… NA Alene… 2021 1453 #> 4 COVID-19 SARS-CoV-2 serial interv… NA Alene… 2021 3924 #> 5 COVID-19 SARS-CoV-2 serial interv… lnorm Nishi… 2020 28 #> 6 COVID-19 SARS-CoV-2 serial interv… weibull Nishi… 2020 18 #> 7 COVID-19 SARS-CoV-2 incubation pe… weibull Yang … 2020 178 #> 8 COVID-19 SARS-CoV-2 serial interv… norm Yang … 2020 131 #> 9 COVID-19 SARS-CoV-2 incubation pe… NA Elias… 2021 28675 #> 10 COVID-19 SARS-CoV-2 incubation pe… weibull Bui e… 2020 19 #> # ℹ 17 more rows # example filtering an existing list to incubation periods epiparameter_list <- epiparameter_db(disease = \"COVID-19\") #> Returning 27 results that match the criteria (22 are parameterised). #> Use subset to filter by entry variables or single_epiparameter to return a single entry. #> To retrieve the citation for each use the 'get_citation' function parameter_tbl( multi_epiparameter = epiparameter_list, epi_name = \"incubation period\" ) #> # Parameter table: #> # A data frame: 15 × 7 #> disease pathogen epi_name prob_distribution author year sample_size #> <chr> <chr> <chr> <chr> <chr> <dbl> <dbl> #> 1 COVID-19 SARS-CoV-2 incubation pe… NA Men e… 2020 59 #> 2 COVID-19 SARS-CoV-2 incubation pe… NA Rai e… 2022 6241 #> 3 COVID-19 SARS-CoV-2 incubation pe… NA Alene… 2021 1453 #> 4 COVID-19 SARS-CoV-2 incubation pe… weibull Yang … 2020 178 #> 5 COVID-19 SARS-CoV-2 incubation pe… NA Elias… 2021 28675 #> 6 COVID-19 SARS-CoV-2 incubation pe… weibull Bui e… 2020 19 #> 7 COVID-19 SARS-CoV-2 incubation pe… lnorm McAlo… 2020 1357 #> 8 COVID-19 SARS-CoV-2 incubation pe… lnorm McAlo… 2020 1269 #> 9 COVID-19 SARS-CoV-2 incubation pe… lnorm Linto… 2020 52 #> 10 COVID-19 SARS-CoV-2 incubation pe… lnorm Linto… 2020 158 #> 11 COVID-19 SARS-CoV-2 incubation pe… lnorm Linto… 2020 52 #> 12 COVID-19 SARS-CoV-2 incubation pe… lnorm Lauer… 2020 181 #> 13 COVID-19 SARS-CoV-2 incubation pe… lnorm Lauer… 2020 99 #> 14 COVID-19 SARS-CoV-2 incubation pe… lnorm Lauer… 2020 108 #> 15 COVID-19 SARS-CoV-2 incubation pe… lnorm Lauer… 2020 73"},{"path":"https://epiverse-trace.github.io/epiparameter/reference/plot.epiparameter.html","id":null,"dir":"Reference","previous_headings":"","what":"Plot method for <epiparameter> class — plot.epiparameter","title":"Plot method for <epiparameter> class — plot.epiparameter","text":"Plot <epiparameter> object displaying either probability mass function (PMF), (case discrete distributions) probability density function (PDF) (case continuous distributions), cumulative distribution function (CDF).","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/plot.epiparameter.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plot method for <epiparameter> class — plot.epiparameter","text":"","code":"# S3 method for class 'epiparameter' plot(x, cumulative = FALSE, ...)"},{"path":"https://epiverse-trace.github.io/epiparameter/reference/plot.epiparameter.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plot method for <epiparameter> class — plot.epiparameter","text":"x <epiparameter> object. cumulative boolean logical, default FALSE. cumulative = TRUE plots cumulative distribution function (CDF). ... arguments passed methods.","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/plot.epiparameter.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Plot method for <epiparameter> class — plot.epiparameter","text":"default xlim argument specified distribution plotted day 0 99th quantile distribution. Alternatively, numeric vector length 2 first last day plot x-axis can supplied xlim (...).","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/plot.epiparameter.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Plot method for <epiparameter> class — plot.epiparameter","text":"Joshua W. Lambert","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/plot.epiparameter.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Plot method for <epiparameter> class — plot.epiparameter","text":"","code":"# plot continuous epiparameter ep <- epiparameter( disease = \"ebola\", epi_name = \"incubation_period\", prob_distribution = create_prob_distribution( prob_distribution = \"gamma\", prob_distribution_params = c(shape = 2, scale = 1) ) ) #> Citation cannot be created as author, year, journal or title is missing plot(ep) # plot different day range (x-axis) plot(ep, xlim = c(0, 10)) # plot CDF plot(ep, cumulative = TRUE) # plot discrete epiparameter ep <- discretise(ep) plot(ep)"},{"path":"https://epiverse-trace.github.io/epiparameter/reference/print.epiparameter.html","id":null,"dir":"Reference","previous_headings":"","what":"Print method for <epiparameter> class — print.epiparameter","title":"Print method for <epiparameter> class — print.epiparameter","text":"Print method <epiparameter> class","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/print.epiparameter.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Print method for <epiparameter> class — print.epiparameter","text":"","code":"# S3 method for class 'epiparameter' print(x, ...)"},{"path":"https://epiverse-trace.github.io/epiparameter/reference/print.epiparameter.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Print method for <epiparameter> class — print.epiparameter","text":"x <epiparameter> object. ... dots Extra arguments passed method.","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/print.epiparameter.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Print method for <epiparameter> class — print.epiparameter","text":"Invisibly returns <epiparameter>. Called side-effects.","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/print.epiparameter.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Print method for <epiparameter> class — print.epiparameter","text":"","code":"epiparameter <- epiparameter( disease = \"ebola\", epi_name = \"incubation_period\", prob_distribution = create_prob_distribution( prob_distribution = \"gamma\", prob_distribution_params = c(shape = 1, scale = 1) ) ) #> Citation cannot be created as author, year, journal or title is missing epiparameter #> Disease: ebola #> Pathogen: NA #> Epi Parameter: incubation period #> Study: (????). “No citation.” #> Distribution: gamma (NA) #> Parameters: #> shape: 1.000 #> scale: 1.000"},{"path":"https://epiverse-trace.github.io/epiparameter/reference/print.multi_epiparameter.html","id":null,"dir":"Reference","previous_headings":"","what":"Print method for <multi_epiparameter> class — print.multi_epiparameter","title":"Print method for <multi_epiparameter> class — print.multi_epiparameter","text":"Print method <multi_epiparameter> class","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/print.multi_epiparameter.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Print method for <multi_epiparameter> class — print.multi_epiparameter","text":"","code":"# S3 method for class 'multi_epiparameter' print(x, ..., n = NULL)"},{"path":"https://epiverse-trace.github.io/epiparameter/reference/print.multi_epiparameter.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Print method for <multi_epiparameter> class — print.multi_epiparameter","text":"x <multi_epiparameter> object. ... dots Extra arguments passed method. n numeric specifying many <epiparameter> objects print. argument passed head() list printing. Default NULL number elements print controlled package options().","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/print.multi_epiparameter.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Print method for <multi_epiparameter> class — print.multi_epiparameter","text":"Invisibly returns <multi_epiparameter>. Called side-effects.","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/print.multi_epiparameter.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Print method for <multi_epiparameter> class — print.multi_epiparameter","text":"","code":"# entire database db <- epiparameter_db() #> Returning 125 results that match the criteria (100 are parameterised). #> Use subset to filter by entry variables or single_epiparameter to return a single entry. #> To retrieve the citation for each use the 'get_citation' function db #> # List of 125 <epiparameter> objects #> Number of diseases: 23 #> ❯ Adenovirus ❯ COVID-19 ❯ Chikungunya ❯ Dengue ❯ Ebola Virus Disease ❯ Hantavirus Pulmonary Syndrome ❯ Human Coronavirus ❯ Influenza ❯ Japanese Encephalitis ❯ MERS ❯ Marburg Virus Disease ❯ Measles ❯ Mpox ❯ Parainfluenza ❯ Pneumonic Plague ❯ RSV ❯ Rhinovirus ❯ Rift Valley Fever ❯ SARS ❯ Smallpox ❯ West Nile Fever ❯ Yellow Fever ❯ Zika Virus Disease #> Number of epi parameters: 13 #> ❯ case fatality risk ❯ generation time ❯ hospitalisation to death ❯ hospitalisation to discharge ❯ incubation period ❯ notification to death ❯ notification to discharge ❯ offspring distribution ❯ onset to death ❯ onset to discharge ❯ onset to hospitalisation ❯ onset to ventilation ❯ serial interval #> [[1]] #> Disease: Adenovirus #> Pathogen: Adenovirus #> Epi Parameter: incubation period #> Study: Lessler J, Reich N, Brookmeyer R, Perl T, Nelson K, Cummings D (2009). #> “Incubation periods of acute respiratory viral infections: a systematic #> review.” _The Lancet Infectious Diseases_. #> doi:10.1016/S1473-3099(09)70069-6 #> <https://doi.org/10.1016/S1473-3099%2809%2970069-6>. #> Distribution: lnorm (days) #> Parameters: #> meanlog: 1.723 #> sdlog: 0.231 #> #> [[2]] #> Disease: Human Coronavirus #> Pathogen: Human_Cov #> Epi Parameter: incubation period #> Study: Lessler J, Reich N, Brookmeyer R, Perl T, Nelson K, Cummings D (2009). #> “Incubation periods of acute respiratory viral infections: a systematic #> review.” _The Lancet Infectious Diseases_. #> doi:10.1016/S1473-3099(09)70069-7 #> <https://doi.org/10.1016/S1473-3099%2809%2970069-7>. #> Distribution: lnorm (days) #> Parameters: #> meanlog: 1.163 #> sdlog: 0.140 #> #> [[3]] #> Disease: SARS #> Pathogen: SARS-Cov-1 #> Epi Parameter: incubation period #> Study: Lessler J, Reich N, Brookmeyer R, Perl T, Nelson K, Cummings D (2009). #> “Incubation periods of acute respiratory viral infections: a systematic #> review.” _The Lancet Infectious Diseases_. #> doi:10.1016/S1473-3099(09)70069-8 #> <https://doi.org/10.1016/S1473-3099%2809%2970069-8>. #> Distribution: lnorm (days) #> Parameters: #> meanlog: 1.386 #> sdlog: 0.593 #> #> # ℹ 122 more elements #> # ℹ Use `print(n = ...)` to see more elements. #> # ℹ Use `parameter_tbl()` to see a summary table of the parameters. #> # ℹ Explore database online at: https://epiverse-trace.github.io/epiparameter/articles/database.html # a single disease db <- epiparameter_db(disease = \"Ebola\") #> Returning 17 results that match the criteria (17 are parameterised). #> Use subset to filter by entry variables or single_epiparameter to return a single entry. #> To retrieve the citation for each use the 'get_citation' function db #> # List of 17 <epiparameter> objects #> Number of diseases: 1 #> ❯ Ebola Virus Disease #> Number of epi parameters: 9 #> ❯ hospitalisation to death ❯ hospitalisation to discharge ❯ incubation period ❯ notification to death ❯ notification to discharge ❯ offspring distribution ❯ onset to death ❯ onset to discharge ❯ serial interval #> [[1]] #> Disease: Ebola Virus Disease #> Pathogen: Ebola Virus #> Epi Parameter: offspring distribution #> Study: Lloyd-Smith J, Schreiber S, Kopp P, Getz W (2005). “Superspreading and #> the effect of individual variation on disease emergence.” _Nature_. #> doi:10.1038/nature04153 <https://doi.org/10.1038/nature04153>. #> Distribution: nbinom (No units) #> Parameters: #> mean: 1.500 #> dispersion: 5.100 #> #> [[2]] #> Disease: Ebola Virus Disease #> Pathogen: Ebola Virus-Zaire Subtype #> Epi Parameter: incubation period #> Study: Eichner M, Dowell S, Firese N (2011). “Incubation period of ebola #> hemorrhagic virus subtype zaire.” _Osong Public Health and Research #> Perspectives_. doi:10.1016/j.phrp.2011.04.001 #> <https://doi.org/10.1016/j.phrp.2011.04.001>. #> Distribution: lnorm (days) #> Parameters: #> meanlog: 2.487 #> sdlog: 0.330 #> #> [[3]] #> Disease: Ebola Virus Disease #> Pathogen: Ebola Virus-Zaire Subtype #> Epi Parameter: onset to death #> Study: The Ebola Outbreak Epidemiology Team, Barry A, Ahuka-Mundeke S, Ali #> Ahmed Y, Allarangar Y, Anoko J, Archer B, Abedi A, Bagaria J, Belizaire #> M, Bhatia S, Bokenge T, Bruni E, Cori A, Dabire E, Diallo A, Diallo B, #> Donnelly C, Dorigatti I, Dorji T, Waeber A, Fall I, Ferguson N, #> FitzJohn R, Tengomo G, Formenty P, Forna A, Fortin A, Garske T, #> Gaythorpe K, Gurry C, Hamblion E, Djingarey M, Haskew C, Hugonnet S, #> Imai N, Impouma B, Kabongo G, Kalenga O, Kibangou E, Lee T, Lukoya C, #> Ly O, Makiala-Mandanda S, Mamba A, Mbala-Kingebeni P, Mboussou F, #> Mlanda T, Makuma V, Morgan O, Mulumba A, Kakoni P, Mukadi-Bamuleka D, #> Muyembe J, Bathé N, Ndumbi Ngamala P, Ngom R, Ngoy G, Nouvellet P, Nsio #> J, Ousman K, Peron E, Polonsky J, Ryan M, Touré A, Towner R, Tshapenda #> G, Van De Weerdt R, Van Kerkhove M, Wendland A, Yao N, Yoti Z, Yuma E, #> Kalambayi Kabamba G, Mwati J, Mbuy G, Lubula L, Mutombo A, Mavila O, #> Lay Y, Kitenge E (2018). “Outbreak of Ebola virus disease in the #> Democratic Republic of the Congo, April–May, 2018: an epidemiological #> study.” _The Lancet_. doi:10.1016/S0140-6736(18)31387-4 #> <https://doi.org/10.1016/S0140-6736%2818%2931387-4>. #> Distribution: gamma (days) #> Parameters: #> shape: 2.400 #> scale: 3.333 #> #> # ℹ 14 more elements #> # ℹ Use `print(n = ...)` to see more elements. #> # ℹ Use `parameter_tbl()` to see a summary table of the parameters. #> # ℹ Explore database online at: https://epiverse-trace.github.io/epiparameter/articles/database.html # a single epi parameter db <- epiparameter_db(epi_name = \"offspring distribution\") #> Returning 10 results that match the criteria (10 are parameterised). #> Use subset to filter by entry variables or single_epiparameter to return a single entry. #> To retrieve the citation for each use the 'get_citation' function db #> # List of 10 <epiparameter> objects #> Number of diseases: 6 #> ❯ Ebola Virus Disease ❯ Hantavirus Pulmonary Syndrome ❯ Mpox ❯ Pneumonic Plague ❯ SARS ❯ Smallpox #> Number of epi parameters: 1 #> ❯ offspring distribution #> [[1]] #> Disease: SARS #> Pathogen: SARS-Cov-1 #> Epi Parameter: offspring distribution #> Study: Lloyd-Smith J, Schreiber S, Kopp P, Getz W (2005). “Superspreading and #> the effect of individual variation on disease emergence.” _Nature_. #> doi:10.1038/nature04153 <https://doi.org/10.1038/nature04153>. #> Distribution: nbinom (No units) #> Parameters: #> mean: 1.630 #> dispersion: 0.160 #> #> [[2]] #> Disease: SARS #> Pathogen: SARS-Cov-1 #> Epi Parameter: offspring distribution #> Study: Lloyd-Smith J, Schreiber S, Kopp P, Getz W (2005). “Superspreading and #> the effect of individual variation on disease emergence.” _Nature_. #> doi:10.1038/nature04153 <https://doi.org/10.1038/nature04153>. #> Distribution: nbinom (No units) #> Parameters: #> mean: 0.940 #> dispersion: 0.170 #> #> [[3]] #> Disease: Smallpox #> Pathogen: Smallpox-Variola-Major #> Epi Parameter: offspring distribution #> Study: Lloyd-Smith J, Schreiber S, Kopp P, Getz W (2005). “Superspreading and #> the effect of individual variation on disease emergence.” _Nature_. #> doi:10.1038/nature04153 <https://doi.org/10.1038/nature04153>. #> Distribution: nbinom (No units) #> Parameters: #> mean: 3.190 #> dispersion: 0.370 #> #> # ℹ 7 more elements #> # ℹ Use `print(n = ...)` to see more elements. #> # ℹ Use `parameter_tbl()` to see a summary table of the parameters. #> # ℹ Explore database online at: https://epiverse-trace.github.io/epiparameter/articles/database.html"},{"path":"https://epiverse-trace.github.io/epiparameter/reference/reexports.html","id":null,"dir":"Reference","previous_headings":"","what":"Objects exported from other packages — reexports","title":"Objects exported from other packages — reexports","text":"objects imported packages. Follow links see documentation. distributional cdf, generate","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/test_epiparameter.html","id":null,"dir":"Reference","previous_headings":"","what":"Test whether an object is a valid <epiparameter> object — test_epiparameter","title":"Test whether an object is a valid <epiparameter> object — test_epiparameter","text":"Test whether object valid <epiparameter> object","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/test_epiparameter.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Test whether an object is a valid <epiparameter> object — test_epiparameter","text":"","code":"test_epiparameter(x)"},{"path":"https://epiverse-trace.github.io/epiparameter/reference/test_epiparameter.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Test whether an object is a valid <epiparameter> object — test_epiparameter","text":"x R object.","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/test_epiparameter.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Test whether an object is a valid <epiparameter> object — test_epiparameter","text":"boolean logical whether object valid <epiparameter> object (prints message invalid <epiparameter> object provided).","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/test_epiparameter.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Test whether an object is a valid <epiparameter> object — test_epiparameter","text":"","code":"ep <- epiparameter_db(single_epiparameter = TRUE) #> Using Linton N, Kobayashi T, Yang Y, Hayashi K, Akhmetzhanov A, Jung S, Yuan #> B, Kinoshita R, Nishiura H (2020). “Incubation Period and Other #> Epidemiological Characteristics of 2019 Novel Coronavirus Infections #> with Right Truncation: A Statistical Analysis of Publicly Available #> Case Data.” _Journal of Clinical Medicine_. doi:10.3390/jcm9020538 #> <https://doi.org/10.3390/jcm9020538>.. #> To retrieve the citation use the 'get_citation' function test_epiparameter(ep) #> [1] TRUE # example with invalid <epiparameter> ep$disease <- NULL test_epiparameter(ep) #> <epiparameter> is invalid due to: #> - <epiparameter> must contain $disease. #> - <epiparameter> must contain one disease. #> [1] FALSE"},{"path":"https://epiverse-trace.github.io/epiparameter/news/index.html","id":"epiparameter-development-version","dir":"Changelog","previous_headings":"","what":"epiparameter (development version)","title":"epiparameter (development version)","text":"library epidemiological parameters (parameters.json) removed {epiparameter} package moved {epiparameterDB} R package taken dependency. {epiparameter} package licensed solely MIT dual licensing CC0 removed (#415). data dictionary (data_dictionary.json) JSON validation workflow (validate-json.yaml) removed package (#415).","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/news/index.html","id":"epiparameter-030","dir":"Changelog","previous_headings":"","what":"epiparameter 0.3.0","title":"epiparameter 0.3.0","text":"third minor release {epiparameter} R package contains range updates improvements package. principal aim release simplify, clarify enhance classes class methods working epidemiological parameters R. large number breaking changes release, primarily functions function arguments renamed restructured, see Breaking changes section overview.","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/news/index.html","id":"new-features-0-3-0","dir":"Changelog","previous_headings":"","what":"New features","title":"epiparameter 0.3.0","text":"library epidemiological parameters updated include 3 new Chikungunya parameter entries. Mpox parameters previously missing Guzzetta et al. entry added (#346 & #374). c() method added <epiparameter> <multi_epiparameter> objects (#368). aggregate() method added <multi_epiparameter> enable consensus distributions built utilising mixture distribution class {distributional} (#388). Infrastructure added package allow translations messages/warnings/errors printed console. (@Bisaloo, #367). convert_summary_stats_to_params() can now convert median dispersion lognormal distribution (#378). data_dictionary.json enhanced improve validation library epidemiological parameters (parameters.json) (#379). interactive network showing <epiparameter> S3 methods added design_principles.Rmd vignette (#383). data_from_epireview.Rmd article improved updated new changes {epireview} (@CarmenTamayo & @cm401 & @kellymccain28, #305 & #373). Parameter units added every entry {epiparameter} library (parameters.json) $metadata element <epiparameter> objects. create_metadata() function now units argument construct metadata lists (#391). Improved database.Rmd vignette adding short citation reference column (@jamesmbaazam, #348). family() method <epiparameter> improved allow access distribution names transformed (e.g. mixture truncated distributions) untransformed (e.g. gamma lognormal) distributions new argument base_dist new internal function .distributional_family() (#398). as_epiparameter() can now work SARS parameters {epireview} (#407).","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/news/index.html","id":"breaking-changes-0-3-0","dir":"Changelog","previous_headings":"","what":"Breaking changes","title":"epiparameter 0.3.0","text":"<epidist> class renamed <epiparameter> avoid confusion similar R package {epidist} (#360). Many functions used epidist names renamed use epiparameter due renaming class (#360). function signatures epiparameter() new_epiparameter() functions (previously epidist() new_epidist()) updated collapse prob_dist, prob_dist_params, discretise truncation arguments prob_distribution, accepts output create_prob_distribution() (#381). epi_dist argument renamed epi_name. clarify {epiparameter} can work epidemiological parameters take variety forms (e.g. point estimates, ranges, probability distributions, etc.) (#390). <vb_epidist> class ’s methods removed package. used increasing complexity maintenance load package (#359). create_prob_dist() renamed create_prob_distribution() (#381). validate_epiparameter() (previously validate_epidist()) renamed assert_epiparameter(), test_epiparameter() added, aim harmonise design {contactmatrix} messages errors improved (#366 & #402). minimum version R required package now 4.1.0 due use base R pipe (|>) dependencies, R-CMD-check workflow GitHub actions now explicitly runs minimum version R stated DESCRIPTION (#384 & #405).","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/news/index.html","id":"bug-fixes-0-3-0","dir":"Changelog","previous_headings":"","what":"Bug fixes","title":"epiparameter 0.3.0","text":"Epidemiological parameter entries library stored lognormal distributions, parameterised median dispersion now converted meanlog sdlog correctly creating <epiparameter> (auto_calc_params = TRUE) (#381).","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/news/index.html","id":"deprecated-and-defunct-0-3-0","dir":"Changelog","previous_headings":"","what":"Deprecated and defunct","title":"epiparameter 0.3.0","text":"epidist_db() deprecated. replaced epiparameter_db() (#360 & #399).","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/news/index.html","id":"epiparameter-020","dir":"Changelog","previous_headings":"","what":"epiparameter 0.2.0","title":"epiparameter 0.2.0","text":"second release {epiparameter} R package focuses interoperability {epireview} R package. Several functions refactored enhanced. release benefited feedback participants EpiParameter Community workshop hosted World Health Organisation.","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/news/index.html","id":"new-features-0-2-0","dir":"Changelog","previous_headings":"","what":"New features","title":"epiparameter 0.2.0","text":"as_epidist() S3 generic added package coercion R objects <epidist> objects. as_epidist.data.frame() method added, well internal functions is_epireview() determines <data.frame> {epireview}, epireview_to_epidist() performs conversion (#298, #334 & #335) epireview_core_cols.rda data added package. used determine whether input as_epidist.data.frame() parameter table {epireview} objects recognisable class attribute (#298). new website vignette (.e. article) data_from_epireview.Rmd added explains use as_epidist() data {epireview} (#298 & #335). new vignette database.Rmd added package provide web interface {epiparameter} library epidemiological parameters. Contributed @sbfnk (#311). plotting method <epidist> objects (plot.epidist()) improved better differentiate continuous discrete discretised distributions (#315). epidist_db(..., single_epidist = TRUE) now prioritises parameter entries account right truncation (#323). create_epidist_prob_dist() (previously named create_prob_dist()) now exported enables control discretisation settings allowing arguments passed distcrete::distcrete() via ... (#324). <multi_epidist> print method (print.multi_epidist()) improved provides object information print header, first elements list elements list short, extra links advice print footer. design print method follows design pattern {pillar} (#326). <epidist> objects functions work <epidist> objects now work exponential distributions (#333). package now explicit data license: CC0 LICENSE file.","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/news/index.html","id":"breaking-changes-0-2-0","dir":"Changelog","previous_headings":"","what":"Breaking changes","title":"epiparameter 0.2.0","text":"list_distributions() replaced parameter_tbl() enhances printing leveraging {pillar} (#321). <vb_epidist> plotting method (plot.vb_epidist()) removed package. provided minimal functionality unnecessarily complicating function signature plot.epidist() (#315).","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/news/index.html","id":"bug-fixes-0-2-0","dir":"Changelog","previous_headings":"","what":"Bug fixes","title":"epiparameter 0.2.0","text":"DOI PMID lowercase throughout package resolve issues older versions R (see issue #301) (#317).","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/news/index.html","id":"deprecated-and-defunct-0-2-0","dir":"Changelog","previous_headings":"","what":"Deprecated and defunct","title":"epiparameter 0.2.0","text":"None","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/news/index.html","id":"epiparameter-010","dir":"Changelog","previous_headings":"","what":"epiparameter 0.1.0","title":"epiparameter 0.1.0","text":"Initial release {epiparameter} R package. {epiparameter} provides: library epidemiological parameters extracted literature range diseases. Functions classes (class methods) work epidemiological parameters distributions.","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/news/index.html","id":"new-features-0-1-0","dir":"Changelog","previous_headings":"","what":"New features","title":"epiparameter 0.1.0","text":"library 122 epidemiological parameter set epidemiological literature. accessible package system data (sysdata.rda, epiparameter::multi_epidist) internal data (inst/extdata/parameters.json). epidist_db() function loads epidemiological parameters library. Distribution parameter conversion extraction functions (convert_params_to_summary_stats() & convert_summary_stats_to_params(), extract_param()). S3 class work epidemiological parameters <epidist>. class S3 methods aid users easily work data structures. include printing, plotting, distribution functions PDF/PMF, CDF, random number generation distribution quantiles. <epidist> class constructor function, validator function, accessors (get_*()), checkers (is_*()). also <vb_epidist> S3 class vector-borne parameters, internal <multi_epidist> class improved printing lists <epidist> objects. package contains utility functions. list_distributions() helper function provide information list <epidist> objects tabular form. calc_disc_dist_quantile() calculates quantiles probability distribution based vector probabilities time data. Five vignettes included initial release. One introduction package (epiparameter.Rmd), one tutorial converting extracting parameters (extract_convert.Rmd), one protocol used collect entries library epidemiological parameters (data_protocol.Rmd), design vignette (design_principles.Rmd), supplementary vignette quantifies bias using parameter extraction (extract_param()) {epiparameter} (extract-bias.Rmd). Unit tests documentation files. Continuous integration workflows R package checks, rendering README.md, calculating test coverage, deploying pkgdown website, updates package citation, linting package code, checking package system dependency changes, updating copyright year, validating parameter library JSON file.","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/news/index.html","id":"breaking-changes-0-1-0","dir":"Changelog","previous_headings":"","what":"Breaking changes","title":"epiparameter 0.1.0","text":"None","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/news/index.html","id":"bug-fixes-0-1-0","dir":"Changelog","previous_headings":"","what":"Bug fixes","title":"epiparameter 0.1.0","text":"None","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/news/index.html","id":"deprecated-and-defunct-0-1-0","dir":"Changelog","previous_headings":"","what":"Deprecated and defunct","title":"epiparameter 0.1.0","text":"None","code":""}]
+[{"path":"https://epiverse-trace.github.io/epiparameter/LICENSE.html","id":null,"dir":"","previous_headings":"","what":"MIT License","title":"MIT License","text":"Copyright (c) 2022-2025 epiparameter authors Permission hereby granted, free charge, person obtaining copy software associated documentation files (“Software”), deal Software without restriction, including without limitation rights use, copy, modify, merge, publish, distribute, sublicense, /sell copies Software, permit persons Software furnished , subject following conditions: copyright notice permission notice shall included copies substantial portions Software. SOFTWARE PROVIDED “”, WITHOUT WARRANTY KIND, EXPRESS IMPLIED, INCLUDING LIMITED WARRANTIES MERCHANTABILITY, FITNESS PARTICULAR PURPOSE NONINFRINGEMENT. EVENT SHALL AUTHORS COPYRIGHT HOLDERS LIABLE CLAIM, DAMAGES LIABILITY, WHETHER ACTION CONTRACT, TORT OTHERWISE, ARISING , CONNECTION SOFTWARE USE DEALINGS SOFTWARE.","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/articles/data_from_epireview.html","id":"converting-from-epireview-entries-into-an-epiparameter-object","dir":"Articles","previous_headings":"","what":"Converting from {epireview} entries into an <epiparameter> object","title":"Using {epireview} with {epiparameter}","text":"{epireview} package nicely provides epidemiological parameter data systematically reviewing literature, {epiparameter} provides custom data structures working epidemiological data R. Therefore, reading data {epireview} R package converting <epiparameter> object provide greatest utility applied outbreak analytics. start simple example reading Marburg data {epireview} converting <epiparameter> object using as_epiparameter() function {epiparameter} package. loads list four tables, specifically tibbles, contain bibliographic information ($articles), epidemiological parameters ($params), epidemiological models ($models), outbreak information ($outbreaks). start just using epidemiological parameter table convert information <epiparameter>. parameters, subset data keep rows contain incubation periods Marburg. select first entry use first example: can simply pass epidemiological parameter set as_epiparameter() conversion. resulting <epiparameter> contain parameterised probability distribution, instead contains range incubation period ($summary_stats), $metadata shows single case South Africa.","code":"marburg_data <- load_epidata(\"marburg\") #> Warning: There is 1 article with missing first author surname. #> Warning: There is 1 article with missing first author surname and first author first #> name. #> Warning: There is 1 article with missing year of publication. #> Warning: Unknown or uninitialised column: `other_delay_start`. #> Warning: Unknown or uninitialised column: `other_delay_end`. #> Note: the params dataframe does not have a covidence_id column #> Note: the models dataframe does not have a covidence_id column #> Note: the outbreaks dataframe does not have a covidence_id column #> ✔ Data loaded for marburg names(marburg_data) #> [1] \"articles\" \"params\" \"models\" \"outbreaks\" marburg_params <- marburg_data$params marburg_incubation_period <- marburg_params[ marburg_params$parameter_type_short == \"incubation_period\", ] marburg_incubation_period #> # A tibble: 2 × 61 #> parameter_data_id article_id parameter_type parameter_value parameter_unit #> <chr> <int> <chr> <dbl> <chr> #> 1 c2a35e68034b72580654… 6 Human delay -… NA Days #> 2 0106582cf5ed3c52d5e9… 20 Human delay -… NA Days #> # ℹ 56 more variables: parameter_lower_bound <dbl>, #> # parameter_upper_bound <dbl>, parameter_value_type <chr>, #> # parameter_uncertainty_single_value <dbl>, #> # parameter_uncertainty_singe_type <chr>, #> # parameter_uncertainty_lower_value <dbl>, #> # parameter_uncertainty_upper_value <dbl>, parameter_uncertainty_type <chr>, #> # cfr_ifr_numerator <int>, cfr_ifr_denominator <int>, … marburg_incub <- marburg_incubation_period[1, ] marburg_incub #> # A tibble: 1 × 61 #> parameter_data_id article_id parameter_type parameter_value parameter_unit #> <chr> <int> <chr> <dbl> <chr> #> 1 c2a35e68034b72580654… 6 Human delay -… NA Days #> # ℹ 56 more variables: parameter_lower_bound <dbl>, #> # parameter_upper_bound <dbl>, parameter_value_type <chr>, #> # parameter_uncertainty_single_value <dbl>, #> # parameter_uncertainty_singe_type <chr>, #> # parameter_uncertainty_lower_value <dbl>, #> # parameter_uncertainty_upper_value <dbl>, parameter_uncertainty_type <chr>, #> # cfr_ifr_numerator <int>, cfr_ifr_denominator <int>, … marburg_incub_epiparameter <- as_epiparameter(marburg_incub) #> Using Gear (1975). \"<title not available>.\" _<journal not available>_. #> To retrieve the citation use the 'get_citation' function #> Warning: Cannot create full citation for epidemiological parameters without bibliographic information #> see ?as_epiparameter for help. #> No adequate summary statistics available to calculate the parameters of the NA distribution #> Unparameterised <epiparameter> object marburg_incub_epiparameter #> Disease: Marburg Virus Disease #> Pathogen: Marburg virus #> Epi Parameter: human delay incubation period #> Study: Gear (1975). \"<title not available>.\" _<journal not available>_. #> Distribution: NA #> Mean: NA [NA% CI: NA, NA] (Days) #> Median: NA [NA% CI: NA, NA] (Days) #> Range: [c(7, 8)] (Days) marburg_incub_epiparameter$summary_stats #> $mean #> [1] NA #> #> $mean_ci_limits #> [1] NA NA #> #> $mean_ci #> [1] NA #> #> $sd #> [1] NA #> #> $sd_ci_limits #> [1] NA NA #> #> $sd_ci #> [1] NA #> #> $median #> [1] NA #> #> $median_ci_limits #> [1] NA NA #> #> $median_ci #> [1] NA #> #> $dispersion #> [1] NA #> #> $dispersion_ci_limits #> [1] NA NA #> #> $dispersion_ci #> [1] NA #> #> $quantiles #> [1] NA #> #> $range #> [1] 7 8 marburg_incub_epiparameter$metadata #> $units #> [1] \"Days\" #> #> $sample_size #> [1] 1 #> #> $region #> [1] \"Johannesburg, South Africa\" #> #> $transmission_mode #> [1] NA #> #> $vector #> [1] NA #> #> $extrinsic #> [1] FALSE #> #> $inference_method #> [1] NA"},{"path":"https://epiverse-trace.github.io/epiparameter/articles/data_from_epireview.html","id":"creating-an-epiparameter-with-full-citation","dir":"Articles","previous_headings":"","what":"Creating an <epiparameter> with full citation","title":"Using {epireview} with {epiparameter}","text":"last example showed convert epidemiological parameter information, however, may noticed citation created contain information full citation. order provide complete citation <epiparameter> object, highly recommended know provenance parameters can correctly attribute original authors, need also provide bibliographic information {epireview} well epidemiological parameters. article data needs loaded {epireview} using epireview::load_epidata_raw() rather epireview::load_data() load_data() subsets bibliographic information provide: \"id\", \"first_author_surname\", \"year_publication\", \"article_label\" columns. need match entry epidemiological parameter table citation information article table ensure using correct citation parameter set. Thankfully, can easily achieved {epireview} provides unique IDs table link entries. Now can repeat example converting <epiparameter> shown , time pass bibliographic information well epidemiological parameter information create full citation. bibliographic information needs passed article argument. as_epiparameter() function S3 generic. familiar S3 object-oriented programming R, detail important, however, mean article argument explicitly function definition as_epiparameter() (.e. show autocomplete typing function shown read function help page ?as_epiparameter()). Instead, argument specified part ... argument. article argument required converting data {epireview} <epiparameter>, data can converted <epiparameter> objects require argument.","code":"marburg_incub_epiparameter$citation #> Gear (1975). \"<title not available>.\" _<journal not available>_. marburg_articles <- load_epidata_raw( pathogen = \"marburg\", table = \"article\" ) marburg_articles #> # A tibble: 58 × 25 #> article_id pathogen covidence_id first_author_first_n…¹ article_title doi #> <dbl> <chr> <int> <chr> <chr> <chr> #> 1 1 Marburg v… 2059 G A Haemorrhagic… NA #> 2 2 Marburg v… 2042 Christian Antibodies t… NA #> 3 3 Marburg v… 1649 Y The origin a… 10.1… #> 4 4 Marburg v… 1692 D.H. Marburg-Viru… NA #> 5 5 Marburg v… 2597 E. D. Filovirus ac… NA #> 6 6 Marburg v… 3795 JS Outbreak of … 10.1… #> 7 7 Marburg v… 2596 E.D. Haemorrhagic… NA #> 8 8 Marburg v… 1615 O Viral hemorr… 10.4… #> 9 9 Marburg v… 1693 Smiley Suspected Ex… 10.1… #> 10 10 Marburg v… 1692 D Marburg-viru… NA #> # ℹ 48 more rows #> # ℹ abbreviated name: ¹first_author_first_name #> # ℹ 19 more variables: journal <chr>, year_publication <int>, volume <int>, #> # issue <int>, page_first <int>, page_last <int>, paper_copy_only <lgl>, #> # notes <chr>, first_author_surname <chr>, double_extracted <dbl>, #> # qa_m1 <chr>, qa_m2 <chr>, qa_a3 <chr>, qa_a4 <chr>, qa_d5 <chr>, #> # qa_d6 <chr>, qa_d7 <chr>, score <dbl>, id <chr> article_row <- match(marburg_incub$id, marburg_articles$id) article_row #> [1] 6 marburg_incub_article <- marburg_articles[article_row, ] marburg_incub_article #> # A tibble: 1 × 25 #> article_id pathogen covidence_id first_author_first_n…¹ article_title doi #> <dbl> <chr> <int> <chr> <chr> <chr> #> 1 6 Marburg vi… 3795 JS Outbreak of … 10.1… #> # ℹ abbreviated name: ¹first_author_first_name #> # ℹ 19 more variables: journal <chr>, year_publication <int>, volume <int>, #> # issue <int>, page_first <int>, page_last <int>, paper_copy_only <lgl>, #> # notes <chr>, first_author_surname <chr>, double_extracted <dbl>, #> # qa_m1 <chr>, qa_m2 <chr>, qa_a3 <chr>, qa_a4 <chr>, qa_d5 <chr>, #> # qa_d6 <chr>, qa_d7 <chr>, score <dbl>, id <chr> marburg_incub_epiparameter <- as_epiparameter( marburg_incub, article = marburg_incub_article ) #> Using Gear (1975). \"Outbreak of Marburg virus disease in Johannesburg.\" _The #> British Medical Journal_. doi:10.1136/bmj.4.5995.489 #> <https://doi.org/10.1136/bmj.4.5995.489>. #> To retrieve the citation use the 'get_citation' function #> No adequate summary statistics available to calculate the parameters of the NA distribution #> Unparameterised <epiparameter> object marburg_incub_epiparameter #> Disease: Marburg Virus Disease #> Pathogen: Marburg virus #> Epi Parameter: human delay incubation period #> Study: Gear (1975). \"Outbreak of Marburg virus disease in Johannesburg.\" _The #> British Medical Journal_. doi:10.1136/bmj.4.5995.489 #> <https://doi.org/10.1136/bmj.4.5995.489>. #> Distribution: NA #> Mean: NA [NA% CI: NA, NA] (Days) #> Median: NA [NA% CI: NA, NA] (Days) #> Range: [c(7, 8)] (Days) marburg_incub_epiparameter$citation #> Gear (1975). \"Outbreak of Marburg virus disease in Johannesburg.\" _The #> British Medical Journal_. doi:10.1136/bmj.4.5995.489 #> <https://doi.org/10.1136/bmj.4.5995.489>."},{"path":"https://epiverse-trace.github.io/epiparameter/articles/data_from_epireview.html","id":"multi-row-epireview-entries","dir":"Articles","previous_headings":"","what":"Multi-row {epireview} entries","title":"Using {epireview} with {epiparameter}","text":"general, required values parameter represented single entry epireview. cases, e.g. Marburg Virus Disease Ebola Virus Disease (first pathogens PERG team extracted), values captured parameter multiple rows. trying avoid linking entries challenging, still cases linked parameters different rows. provide information limitations section . way {epireview} data stored means epidemiological parameter entries require multiple rows. can , example, contain two summary statistics (e.g. mean standard deviation) kept separate rows. order create <epiparameter> objects contains full information entry multiple rows epidemiological parameters table {epireview} can given as_epiparameter() create single <epiparameter> object. can search entries data multiple rows checking duplicated parameter types IDs. Remember possible convert delay distributions epiparameter objects (.e. known Human delay parameter types {epireview}). case two studies Marburg one entry (row) {epireview} database. studies select mean standard deviation. case, know mean standard deviation chosen rows correspond estimation process read corresponding article. However, currently identifiers {epireview} params database Marburg, Ebola Lassa directly identify two rows mean values correspond standard deviation. {epireview} team currently working rectifying issue. Therefore, encourage readers manually verify data subsets, ensure entries selected indeed multiple rows reported epidemiological parameter. future {epireview} pathogens (excluding SARS) mean standard deviation estimates match form one row $params database. Current software development {epireview} working ensuring compatibility formats. can now convert <epiparameter>.","code":"multi_row_entries <- duplicated(marburg_params$parameter_type) & duplicated(marburg_params$id) multi_row_ids <- marburg_params$id[multi_row_entries] multi_row_marburg_params <- marburg_params[marburg_params$id %in% multi_row_ids, ] multi_row_marburg_params #> # A tibble: 42 × 61 #> parameter_data_id article_id parameter_type parameter_value parameter_unit #> <chr> <int> <chr> <dbl> <chr> #> 1 0106582cf5ed3c52d5e… 20 Human delay -… NA Days #> 2 ce78f707a585d8bb23a… 22 Seroprevalenc… 0 Percentage (%) #> 3 ca720828fd6ccb18844… 22 Seroprevalenc… 0 NA #> 4 61fbb9dfc021abf5bd1… 22 Seroprevalenc… 0 Percentage (%) #> 5 29c8ca74306713a990c… 20 Severity - ca… NA NA #> 6 056a8d6b5f9aee3622d… 27 Human delay -… 9 Days #> 7 ce3976e2e15df3f6fb9… 27 Human delay -… 5.4 Days #> 8 3bf73665fa67a6ba7f7… 27 Human delay -… 7 Days #> 9 ba019f18acac9c5b0b7… 27 Human delay -… 9.3 Days #> 10 71798b4154011dcd008… 27 Human delay -… 9 Days #> # ℹ 32 more rows #> # ℹ 56 more variables: parameter_lower_bound <dbl>, #> # parameter_upper_bound <dbl>, parameter_value_type <chr>, #> # parameter_uncertainty_single_value <dbl>, #> # parameter_uncertainty_singe_type <chr>, #> # parameter_uncertainty_lower_value <dbl>, #> # parameter_uncertainty_upper_value <dbl>, … multi_row_marburg_params$parameter_value_type #> [1] NA NA NA #> [4] NA NA \"Mean\" #> [7] \"Standard Deviation\" \"Median\" \"Mean\" #> [10] \"Median\" \"Mean\" \"Mean\" #> [13] \"Mean\" \"Mean\" \"Mean\" #> [16] NA NA NA #> [19] NA \"Mean\" \"Mean\" #> [22] \"Mean\" \"Mean\" NA #> [25] NA \"Median\" NA #> [28] NA NA NA #> [31] \"Other\" NA NA #> [34] NA \"Other\" \"Other\" #> [37] \"Other\" \"Other\" \"Other\" #> [40] \"Other\" \"Mean\" \"Other\" marburg_gt <- multi_row_marburg_params[ multi_row_marburg_params$parameter_data_id %in% c(\"056a8d6b5f9aee3622d3bd8b715d4296\", \"ce3976e2e15df3f6fb92f6deb2db2a29\"), ] marburg_gt #> # A tibble: 2 × 61 #> parameter_data_id article_id parameter_type parameter_value parameter_unit #> <chr> <int> <chr> <dbl> <chr> #> 1 056a8d6b5f9aee3622d3… 27 Human delay -… 9 Days #> 2 ce3976e2e15df3f6fb92… 27 Human delay -… 5.4 Days #> # ℹ 56 more variables: parameter_lower_bound <dbl>, #> # parameter_upper_bound <dbl>, parameter_value_type <chr>, #> # parameter_uncertainty_single_value <dbl>, #> # parameter_uncertainty_singe_type <chr>, #> # parameter_uncertainty_lower_value <dbl>, #> # parameter_uncertainty_upper_value <dbl>, parameter_uncertainty_type <chr>, #> # cfr_ifr_numerator <int>, cfr_ifr_denominator <int>, … marburg_gt_epiparameter <- as_epiparameter(marburg_gt) #> Using Ajelli (2012). \"<title not available>.\" _<journal not available>_. #> To retrieve the citation use the 'get_citation' function #> Warning: Cannot create full citation for epidemiological parameters without bibliographic information #> see ?as_epiparameter for help. #> No adequate summary statistics available to calculate the parameters of the NA distribution #> Unparameterised <epiparameter> object marburg_gt_epiparameter #> Disease: Marburg Virus Disease #> Pathogen: Marburg virus #> Epi Parameter: human delay generation time #> Study: Ajelli (2012). \"<title not available>.\" _<journal not available>_. #> Distribution: NA #> Mean: 9 [95% CI: 8.2, 10] (Days) #> Median: NA [NA% CI: NA, NA] (Days) #> Range: [c(NA, NA)] (Days) marburg_gt_epiparameter$summary_stats #> $mean #> [1] 9 #> #> $mean_ci_limits #> [1] 8.2 10.0 #> #> $mean_ci #> [1] 95 #> #> $sd #> [1] 5.4 #> #> $sd_ci_limits #> [1] 3.9 8.6 #> #> $sd_ci #> [1] 95 #> #> $median #> [1] NA #> #> $median_ci_limits #> [1] NA NA #> #> $median_ci #> [1] NA #> #> $dispersion #> [1] NA #> #> $dispersion_ci_limits #> [1] NA NA #> #> $dispersion_ci #> [1] NA #> #> $quantiles #> [1] NA #> #> $range #> [1] NA NA"},{"path":"https://epiverse-trace.github.io/epiparameter/articles/data_from_epireview.html","id":"entries-with-probability-distributions","dir":"Articles","previous_headings":"","what":"Entries with probability distributions","title":"Using {epireview} with {epiparameter}","text":"example load Ebola epidemiological parameters {epireview} package (entries Marburg parametric distributions). subset data just use epidemiological parameter table, select rows containing serial interval. select entry estimated reported Weibull distribution: can now convert <epiparameter> object. probability distribution serial interval can utilise <epiparameter> methods. illustrate checking <epiparameter> parameterised, plotting PDF CDF, generating 10 random numbers sampling distribution.","code":"ebola_data <- load_epidata(\"ebola\") #> ℹ ebola does not have any extracted outbreaks #> information. Outbreaks will be set to NULL. #> ✔ Data loaded for ebola ebola_params <- ebola_data$params ebola_si_rows <- ebola_params[ ebola_params$parameter_type_short == \"serial_interval\", ] ebola_si_rows #> # A tibble: 19 × 78 #> id parameter_data_id covidence_id pathogen parameter_type parameter_value #> <chr> <chr> <int> <chr> <chr> <dbl> #> 1 f49a9… 466f684ff8286fbd… 506 Ebola v… Human delay -… 12 #> 2 c1e68… cb37cc4599953d47… 1471 Ebola v… Human delay -… 19.4 #> 3 08e06… 20eb9e7d7714183c… 1876 Ebola v… Human delay -… 11 #> 4 5a250… 115c169147af31f7… 1891 Ebola v… Human delay -… 11.1 #> 5 54159… 6fca288e3bca7dc0… 3138 Ebola v… Human delay -… 16.1 #> 6 f044b… 89e334ec3622ed27… 3776 Ebola v… Human delay -… 14 #> 7 df908… e62da97ac8648211… 4966 Ebola v… Human delay -… 14.2 #> 8 df908… d46ff8b0c2ff67b7… 4966 Ebola v… Human delay -… 7.1 #> 9 1b9d9… abb8b6aabf43ac86… 5924 Ebola v… Human delay -… 13.7 #> 10 39354… 2b270d400af4fcce… 5939 Ebola v… Human delay -… NA #> 11 39354… 8a18cde4823cf9f7… 5939 Ebola v… Human delay -… NA #> 12 39354… 10f3384f1550a778… 5939 Ebola v… Human delay -… NA #> 13 50dea… 631ec65830a82fbe… 6346 Ebola v… Human delay -… 15.3 #> 14 86e39… 5c8d68c39d1c3b98… 15896 Ebola v… Human delay -… 15.3 #> 15 40a29… 7f4ab651c48511df… 17077 Ebola v… Human delay -… 15.3 #> 16 b76dc… 0c3e02f80addfccc… 17730 Ebola v… Human delay -… 12 #> 17 b76dc… c2e0739d6bc652e9… 17730 Ebola v… Human delay -… 11.7 #> 18 74b62… e2a59f5aa40ddbdf… 18536 Ebola v… Human delay -… 12.3 #> 19 66e1b… 4da557e3c2c22a10… 19083 Ebola v… Human delay -… NA #> # ℹ 72 more variables: exponent <dbl>, parameter_unit <chr>, #> # parameter_lower_bound <dbl>, parameter_upper_bound <dbl>, #> # parameter_value_type <chr>, parameter_uncertainty_single_value <dbl>, #> # parameter_uncertainty_singe_type <chr>, #> # parameter_uncertainty_lower_value <dbl>, #> # parameter_uncertainty_upper_value <dbl>, parameter_uncertainty_type <chr>, #> # cfr_ifr_numerator <int>, cfr_ifr_denominator <int>, … ebola_si <- ebola_si_rows[ ebola_si_rows$parameter_data_id == \"0c3e02f80addfccc1017fa619fba76c5\", ] ebola_si #> # A tibble: 1 × 78 #> id parameter_data_id covidence_id pathogen parameter_type parameter_value #> <chr> <chr> <int> <chr> <chr> <dbl> #> 1 b76dcc… 0c3e02f80addfccc… 17730 Ebola v… Human delay -… 12 #> # ℹ 72 more variables: exponent <dbl>, parameter_unit <chr>, #> # parameter_lower_bound <dbl>, parameter_upper_bound <dbl>, #> # parameter_value_type <chr>, parameter_uncertainty_single_value <dbl>, #> # parameter_uncertainty_singe_type <chr>, #> # parameter_uncertainty_lower_value <dbl>, #> # parameter_uncertainty_upper_value <dbl>, parameter_uncertainty_type <chr>, #> # cfr_ifr_numerator <int>, cfr_ifr_denominator <int>, … ebola_si_epiparameter <- as_epiparameter(ebola_si) #> Using Marziano (2023). \"<title not available>.\" _<journal not available>_. #> To retrieve the citation use the 'get_citation' function #> Warning: Cannot create full citation for epidemiological parameters without bibliographic information #> see ?as_epiparameter for help. ebola_si_epiparameter #> Disease: Ebola Virus Disease #> Pathogen: Ebola virus #> Epi Parameter: human delay serial interval #> Study: Marziano (2023). \"<title not available>.\" _<journal not available>_. #> Distribution: weibull (Days) #> Parameters: #> shape: 1.760 #> scale: 10.140 is_parameterised(ebola_si_epiparameter) #> [1] TRUE plot(ebola_si_epiparameter) generate(ebola_si_epiparameter, times = 10) #> [1] 8.1913357 17.4324678 1.4003823 6.3030243 0.9521153 7.7520633 #> [7] 4.8497066 7.3824799 4.6173973 6.4234107"},{"path":"https://epiverse-trace.github.io/epiparameter/articles/data_from_epireview.html","id":"specifying-the-probability-distribution-if-unknown","dir":"Articles","previous_headings":"","what":"Specifying the probability distribution if unknown","title":"Using {epireview} with {epiparameter}","text":"may instances delay distribution reported literature, either probability distribution fit data, reported probability distribution parameters correspond . Therefore, probability distribution specified {epireview} data. cases, parametric probability distribution required particular epidemiological task assuming probability distribution can useful. Please use feature caution. Assuming incorrect probability distribution applying epidemiological method can lead erroneous results. Additionally, probability distribution specified user overwrite probability distribution specified input data (e.g. {epireview} parameter data) can lead error distribution name supplied parameters input incompatible See ?as_epiparameter details information. Just example load Ebola parameters using epireview::load_epidata() function subset just parameters ($params). use serial interval Ebola reported Faye et al. (2015). stored, two rows {epireview} parameter table, mean standard deviation, probability distribution specified. code chunk subsets Ebola parameter table just return serial interval Faye et al. (2015). supply data as_epiparameter() get unparameterised <epiparameter> object probability distribution stated. Given can convert mean standard deviation parameters probability distribution assume distribution form, can supply data as_epiparameter(). uses parameter conversion functions {epiparameter} (see vignette(\"extract_convert\", package = \"epiparameter\")). Ebola serial interval <epiparameter> can now used various probability distribution methods.","code":"ebola_data <- load_epidata(\"ebola\") #> ℹ ebola does not have any extracted outbreaks #> information. Outbreaks will be set to NULL. #> ✔ Data loaded for ebola ebola_params <- ebola_data$params ebola_si <- ebola_params[ which( grepl(pattern = \"Faye\", x = ebola_params$article_label, fixed = TRUE) & grepl(pattern = \"serial\", ebola_params$parameter_type, fixed = TRUE) ), ] ebola_si_epiparameter <- as_epiparameter(ebola_si) #> Using Faye (2015). \"<title not available>.\" _<journal not available>_. #> To retrieve the citation use the 'get_citation' function #> Warning: Cannot create full citation for epidemiological parameters without bibliographic information #> see ?as_epiparameter for help. #> No adequate summary statistics available to calculate the parameters of the NA distribution #> Unparameterised <epiparameter> object ebola_si_epiparameter #> Disease: Ebola Virus Disease #> Pathogen: Ebola virus #> Epi Parameter: human delay serial interval #> Study: Faye (2015). \"<title not available>.\" _<journal not available>_. #> Distribution: NA #> Mean: 14.2 [95% CI: 13.1, 15.5] (Days) #> Median: NA [NA% CI: NA, NA] (Days) #> Range: [c(NA, NA)] (Days) is_parameterised(ebola_si_epiparameter) #> [1] FALSE ebola_si_epiparameter <- as_epiparameter(ebola_si, prob_distribution = \"gamma\") #> Using Faye (2015). \"<title not available>.\" _<journal not available>_. #> To retrieve the citation use the 'get_citation' function #> Warning: Cannot create full citation for epidemiological parameters without bibliographic information #> see ?as_epiparameter for help. #> Parameterising the probability distribution with the summary statistics. #> Probability distribution is assumed not to be discretised or truncated. ebola_si_epiparameter #> Disease: Ebola Virus Disease #> Pathogen: Ebola virus #> Epi Parameter: human delay serial interval #> Study: Faye (2015). \"<title not available>.\" _<journal not available>_. #> Distribution: gamma (Days) #> Parameters: #> shape: 4.000 #> scale: 3.550 is_parameterised(ebola_si_epiparameter) #> [1] TRUE get_parameters(ebola_si_epiparameter) #> shape scale #> 4.00 3.55 density(ebola_si_epiparameter, at = 20) #> [1] 0.03001206 plot(ebola_si_epiparameter) cdf(ebola_si_epiparameter, q = 10) #> [1] 0.3118251 plot(ebola_si_epiparameter, cumulative = TRUE) quantile(ebola_si_epiparameter, p = 0.5) #> [1] 13.03582 generate(ebola_si_epiparameter, times = 10) #> [1] 10.462339 11.714033 16.448346 14.659046 14.979571 21.061653 6.958792 #> [8] 20.288048 15.635075 1.645704"},{"path":"https://epiverse-trace.github.io/epiparameter/articles/data_from_epireview.html","id":"limitations","dir":"Articles","previous_headings":"","what":"Limitations","title":"Using {epireview} with {epiparameter}","text":"database schema {epireview} evolved time Imperial PERG team extracted pathogens. list parameter types available {epireview} package important differentiate variability sample (e.g. sample standard deviation) uncertainty estimate (e.g. 95% confidence interval credible interval). database version {epireview} Zika, PERG team explicitly expose remove ambiguity extracted data. Please note Marburg, Lassa, Ebola datasets, may ambiguity variability uncertainty. functionality {epiparameter} {epireview} developed improved coming months.","code":""},{"path":[]},{"path":"https://epiverse-trace.github.io/epiparameter/articles/data_protocol.html","id":"about-the-package","dir":"Articles","previous_headings":"","what":"About the package","title":"Data Collation and Synthesis Protocol","text":"{epiparameter} R package contains library epidemiological parameter data functions read handle data. delay distributions describe time two events epidemiology, example incubation period, serial interval onset--death; offspring distributions describe number secondary infections primary infection disease transmission. library compiled process collecting, reviewing extracting data peer-reviewed literature1, including research articles, systematic reviews meta-analyses. epiparameter package act ‘living systematic review’ (sensu Elliott et al. (2014)) actively updated maintained provide reliable source data epidemiological distributions. prevent bias collection assessment data, well-defined methodology searching refining required. document aims provide transparency methodology used epiparameter maintainers outlining steps taken stage data handling. can also serve guide contributors wanting search provide epidemiological parameters currently missing library. protocol also facilitate reproducibility searches, results appraisal steps. large body work methods best conduct literature searches data collection part systematic reviews meta-analyses2, use basis protocol. sources : Cochrane Handbook (Higgins et al. 2022) PRISMA (Page et al. 2021)","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/articles/data_protocol.html","id":"objective-of-epiparameter","dir":"Articles","previous_headings":"","what":"Objective of {epiparameter}","title":"Data Collation and Synthesis Protocol","text":"defined PRISMA guidelines, clearly stated objective helps refine goal project. epiparameter’s objective provide information collection distributions range infectious diseases accurate, unbiased comprehensive possible. distributions enable outbreak analysts easily access distributions routine analysis. example, delay distributions necessary : calculating case fatality rates adjusting delay outcome, quantifying implications different screening measures quarantine periods, estimating reproduction numbers, scenario modelling using transmission dynamic models.","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/articles/data_protocol.html","id":"contributing-to-the-package","dir":"Articles","previous_headings":"","what":"Contributing to the package","title":"Data Collation and Synthesis Protocol","text":"contribute epiparameter library epidemiological parameter information, added data google sheet. integrated epiparameter library package maintainers, information accessible epiparameter package users.","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/articles/data_protocol.html","id":"scope-of-package","dir":"Articles","previous_headings":"","what":"Scope of package","title":"Data Collation and Synthesis Protocol","text":"epiparameter package spans range infectious diseases, including several distributions disease available. pathogens diseases currently systematically searched included package library : distributions currently included literature search pathogen/disease :","code":"#> Returning 125 results that match the criteria (100 are parameterised). #> Use subset to filter by entry variables or single_epiparameter to return a single entry. #> To retrieve the citation for each use the 'get_citation' function #> Returning 125 results that match the criteria (100 are parameterised). #> Use subset to filter by entry variables or single_epiparameter to return a single entry. #> To retrieve the citation for each use the 'get_citation' function"},{"path":"https://epiverse-trace.github.io/epiparameter/articles/data_protocol.html","id":"guide-to-identifying-distributions-in-the-literature","dir":"Articles","previous_headings":"","what":"Guide to identifying distributions in the literature","title":"Data Collation and Synthesis Protocol","text":"Key word searches: searching literature, use specific search phrases ensure correct literature procured required. use search schema includes searching pathogen disease, desired distribution. search phrase can optionally include specific variant/strain/subtype. search constrained based year publication. Examples searches: “SARS-CoV-2 incubation period” “ebola serial interval” “influenza H7N9 onset admission” However, simple search phrases can return large number irrelevant papers. Using specific search schema depending search engine used. example, using Google Scholar schema like: (“Middle East Respiratory Syndrome” MERS) “onset death” (estimation inference calculation) (ebola EVD) “onset death” (estimation inference calculation) Web Science used: (“Middle East Respiratory Syndrome” MERS) “onset death” estimat* (ebola EVD) “onset death” estimat* refine results suitable set literature. Literature search engines: using selection search engines prevent one source potentially omitting papers. Suggested search sites : Google Scholar, Web Science, PubMed, Scopus. Adding papers: addition database entries papers identified literature search, entries can supplemented recommendations (.e. community) cited paper literature search. Papers may recommended experts research public health communities. plan use two methods community engagement. Firstly open-access Google sheet allows people add distribution data reviewed one epiparameter maintainers incorporated meets quality checks. second method - yet implemented - involves community members uploading data zenodo, can read loaded R using epiparameter checked. Language restrictions: papers English Spanish currently supported epiparameter. Papers written another language verified expert can also included database. However, evaluated review process described result flagged user loaded epiparameter.","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/articles/data_protocol.html","id":"guide-to-data-refinement-once-sources-identified","dir":"Articles","previous_headings":"","what":"Guide to data refinement once sources identified","title":"Data Collation and Synthesis Protocol","text":"Removing duplicates: library parameters contain duplicates studies, multiple entries per study can included paper reports multiple results (e.g. full data set subset data). Studies use data, subsets supersets data used papers library included. Abstract methods screening: number unique sources identified, reviewed suitability reviewing abstract searching words phrases paper indicate reports parameters summary statistics distribution, can include searching methods section words types distributions (e.g. lognormal), fitting procedures (e.g. maximum likelihood bayesian), searching results parameter estimates. epiparameter library includes entries parameters summary statistics reported distribution specified, entries distribution specified parameters reported. database unsuitable papers kept remind maintainers papers included aids updating database (see ) preventing redundant reviewing previously rejected paper. Stopping criteria: many searches, number results far larger reasonably evaluated outside full systematic review. refining papers contain required information (abstract methods screening), around 10 papers per pathogen screened search (per search round, see updating section details). number papers pass abstract methods screening fewer 10, suitable papers reviewed. Full paper screening: abstract methods screening, papers excluded reviewed full verify indeed contain required information distribution parameters information methodology used. acceptable include secondary source contains information delay distribution primary source unavailable report distribution. inference delay distribution primary subject research article, example inferred used estimation R0R_0 can still included database. Additionally, distribution parameters based illustrative values use simulations - rather inferred data - considered unsuitable excluded. , papers excluded stage recorded database unsuitable sources reasoning prevent reassess updating database. Post hoc removal: epiparameter parameters later identified inappropriate can removed database. cases unlikely limitations can appended onto data entries make users aware limitations (e.g. around assumptions used infer distribution), extreme cases data completely removed database. Note: systematic reviews focusing effect sizes can subject publication bias (e.g. positive significant results literature). However, distribution inference focus significance testing effect sizes, bias considered collection process.","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/articles/data_protocol.html","id":"guide-to-extracting-parameters","dir":"Articles","previous_headings":"","what":"Guide to extracting parameters","title":"Data Collation and Synthesis Protocol","text":"Extracting parameters: underlying distributions (e.g. gamma, lognormal), parameters (e.g. shape/scale, meanlog/sdlog), summary statistics (e.g. mean, standard deviation, median, range quantiles) given paper, values recorded verbatim paper database. read R using epiparameter package, aspects distribution automatically calculated available. example mean standard deviation gamma distribution reported serial interval values stored database. R, shape scale parameters gamma distribution automatically reconstructed resulting distribution available use. epiparameter library exactly reflects literature. mean information present paper imputed prior knowledge (e.g. vector disease known stated), performing calculating reported values. prevents issue clear provenance data library. requirements entry database defined data dictionary. outline minimal dataset required included epiparameter library : Name disease Type distribution Citation information (author(s) paper, year publication, publication title journal, DOI) Whether distribution extrinsic (e.g. extrinsic incubation period). disease vector-borne NA. Whether distribution fitted discretised, boolean (true false). information database entry non-essential. See data dictionary included epiparameter database fields description range possible values field can take.","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/articles/data_protocol.html","id":"data-quality-assessment-in-epiparameter","dir":"Articles","previous_headings":"","what":"Data quality assessment in {epiparameter}","title":"Data Collation and Synthesis Protocol","text":"inference parameters delay distribution often requires methodological adjustments correct factors otherwise bias estimates. includes accounting interval-censoring data timing event (e.g. exposure pathogen) know certainty, rather within time window. adjusting phase bias distribution estimated growing shrinking stage epidemic. aim epiparameter make judgement parameters ‘better’ others, notify warn user potential limitations data. aspects assessed : 1) whether method includes single double interval-censoring exposure onset times known certainty (.e. single day); 2) method adjust phase bias outbreak ascending descending phase. indicated boolean values indicate whether reported paper users recommended refer back paper determine whether estimates biased.","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/articles/data_protocol.html","id":"guide-to-the-epiparameter-review-process","dir":"Articles","previous_headings":"","what":"Guide to the {epiparameter} review process","title":"Data Collation and Synthesis Protocol","text":"set parameters included database must pass abstract methods screening full screening subsequently review one epiparameter maintainers. process involves running diagnostic checks cross-referencing reported parameters paper ensure match exactly results plot PDF/CDF/PMF matches anything plotted paper, available. prevents possible misinterpretation (e.g. serial interval incubation period). check also includes making sure unique identifiers paper match author’s name, publication year data recorded database.","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/articles/data_protocol.html","id":"updating-parameters-in-the-database","dir":"Articles","previous_headings":"","what":"Updating parameters in the database","title":"Data Collation and Synthesis Protocol","text":"search review stages time consuming continuously carried , aim keep epiparameter library --date living data library conducting regular searches (.e. every 3-4 months) fill missing papers new publication since last search. epidemiological literature can expand rapidly, especially new outbreak. Therefore can optionally include new studies use epidemiological community regular updates. small additions still subject data quality assessment diagnostics ensure accuracy, likely picked subsequent literature searches. likely existing pathogens major increase incidence since last update new papers reporting delay distributions. cases papers previously reviewed due limited reviewing time round updates now checked. particularly value community contributions database, everyone can benefit analysis already conducted, duplicated effort reduced.","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/articles/data_protocol.html","id":"database-of-excluded-papers","dir":"Articles","previous_headings":"","what":"Database of excluded papers","title":"Data Collation and Synthesis Protocol","text":"papers returned search results suitable, either stage abstract screening, reviewing entirety paper, recorded database following information: First author’s last name Unique identifier, ideally DOI Journal, pre-print server, host website One several reasons deemed unsuitable Date recording","code":""},{"path":[]},{"path":"https://epiverse-trace.github.io/epiparameter/articles/design_principles.html","id":"scope","dir":"Articles","previous_headings":"","what":"Scope","title":"Design Principles for {epiparameter}","text":"{epiparameter} R package library epidemiological parameters, provides class (.e. data structure) helper functions working epidemiological parameters distributions. <epiparameter> class main functional object working epidemiological parameters can hold information delay distributions (e.g. incubation period, serial interval, onset--death distribution) offspring distributions. class number methods, including allowing user easily calculate PDF, CDF, quantile, generate random numbers, calculate distribution mean, plot distribution. <epiparameter> object can created constructor function epiparameter(), uncertain whether object <epiparameter>, can validated assert_epiparameter(). package also converts distribution parameters summary statistics, vice versa. achieved either conversion extraction methods functions used explained Parameter extraction conversion {epiparameter} vignette.","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/articles/design_principles.html","id":"output","dir":"Articles","previous_headings":"","what":"Output","title":"Design Principles for {epiparameter}","text":"output epiparameter() constructor function <epiparameter> object. list nine elements, element either single type (e.g. character), non-nested list another class. Classes <epiparameter> elements used existing well developed infrastructure handling certain data types. $prod_dist element uses distribution class – parameterised distribution available – using either <distribution> class {distributional} <distcrete> class {distcrete}. $citation handled using <bibentry> class {utils} package (included part base R recommended packages). functions return simplest type possible, may atomic vector (including single element vectors), un-nested lists.","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/articles/design_principles.html","id":"package-architecture","dir":"Articles","previous_headings":"","what":"Package architecture","title":"Design Principles for {epiparameter}","text":"Much {epiparameter} package centred around <epiparameter> class. diagram showing class ’s S3 methods (diagram interactive can adjusted labels overlapping).","code":"#> This diagram is out of date, as new methods have been added to the package which are not included."},{"path":"https://epiverse-trace.github.io/epiparameter/articles/design_principles.html","id":"design-decisions","dir":"Articles","previous_headings":"","what":"Design decisions","title":"Design Principles for {epiparameter}","text":"<epiparameter> class designed core unit working epidemiological parameters. designed parallel epidemiological data structures <contactmatrix> class {contactmatrix} R package. design principles <epiparameter> class aligned <contactmatrix> design principles. include: new_*<class>() constructor assert_<class>() test_<class>() is_<class>() checker determine object given class (without checking validity class) Coercion generic as_<class>(). conversion functions (convert_*) S3 generic functions methods provided {epiparameter} character <epiparameter> input. follows design pattern packages, {dplyr}, export key data transformation functions S3 generics allow developers extend conversions data objects. conversion functions designed single function exported user summary statistics parameters, another function exported parameters summary statistics. functions use switch() dispatch internal conversion functions. provides minimal number conversion functions package namespace compared exporting conversion function every distribution. large number entries returned reading epidemiological parameters library using epiparameter_db() function, can flood console, due default list printing R. reasoning <multi_epiparameter> object minimal class enable cleaner descriptive printing large list <epiparameter> objects. print.multi_epiparameter() prints header metadata number <epiparameter> objects number diseases epidemiological distributions list. also lists diseases epidemiological parameters returned. footer print() function states number <epiparameter> objects shown, guides use print(n = ...) parameter_tbl() link online database vignette (database.Rmd). Information header footer considered metadata advice prefixed #. package uses S3 classes S3 dispatch exported functions, switch() .call() dispatching internal functions. easier develop debug internal functions use S3 dispatch avoids ensure S3 methods registered. Examples S3 dispatch exported functions get_parameters() convert_summary_stats_to_params(). Examples internal dispatch using switch() .call() clean_params() convert_params_to_summary_stats.character(). function naming convention internal functions dot (.) prefix (e.g. .convert_params_lnorm()). function breaks convention new_epiparameter() advanced users package may want call internal low-level constructor, adding dot prefix function may make harder users find.","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/articles/design_principles.html","id":"dependencies","dir":"Articles","previous_headings":"","what":"Dependencies","title":"Design Principles for {epiparameter}","text":"aim restrict number dependencies minimal required set ease maintenance. current hard dependencies : {checkmate} {distributional} {distcrete} {stats} {utils} {stats} {utils} distributed R language viewed lightweight dependencies, already installed user’s machine R. {checkmate} input checking package widely used across Epiverse-TRACE packages. {distributional} {distcrete} used import S3 classes handling working distributions. required {distcrete} can handle discretised distributions. Currently {epiparameter} deviates Epiverse policy number previous R versions supports. {epiparameter} package requires R version >= 4.1.0 includes current version last three minor R versions rather policy four minor versions, September 2024. reasons change enable usage base R pipe (|>).","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/articles/design_principles.html","id":"contribute","dir":"Articles","previous_headings":"","what":"Contribute","title":"Design Principles for {epiparameter}","text":"addition package contributing guide, please refer {epiparameter} specific contributing guidelines adding epidemiological parameter package library.","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/articles/epiparameter.html","id":"use-case","dir":"Articles","previous_headings":"","what":"Use case","title":"Getting Started with {epiparameter}","text":"outbreak known potentially novel pathogen detected key parameters delay distributions (e.g. incubation period serial interval) required interpret early data. {epiparameter} can provide distributions selection published sources, past analysis similar pathogen, order provide relevant epidemiological parameters new analysis.","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/articles/epiparameter.html","id":"library-of-epidemiological-parameters","dir":"Articles","previous_headings":"","what":"Library of epidemiological parameters","title":"Getting Started with {epiparameter}","text":"First, introduce library, database, epidemiological parameters available {epiparameter}. library stored internally can read R using epiparameter_db() function. default entries library returned. output list <epiparameter> objects, element list corresponds entry parameter database. see full list diseases distributions stored library use parameter_tbl() function. show first six rows output. parameter_tbl() can also subset database supplied function. details data collation library parameters can found Data Collation Synthesis Protocol vignette.","code":"db <- epiparameter_db() #> Returning 125 results that match the criteria (100 are parameterised). #> Use subset to filter by entry variables or single_epiparameter to return a single entry. #> To retrieve the citation for each use the 'get_citation' function db #> # List of 125 <epiparameter> objects #> Number of diseases: 23 #> ❯ Adenovirus ❯ Chikungunya ❯ COVID-19 ❯ Dengue ❯ Ebola Virus Disease ❯ Hantavirus Pulmonary Syndrome ❯ Human Coronavirus ❯ Influenza ❯ Japanese Encephalitis ❯ Marburg Virus Disease ❯ Measles ❯ MERS ❯ Mpox ❯ Parainfluenza ❯ Pneumonic Plague ❯ Rhinovirus ❯ Rift Valley Fever ❯ RSV ❯ SARS ❯ Smallpox ❯ West Nile Fever ❯ Yellow Fever ❯ Zika Virus Disease #> Number of epi parameters: 13 #> ❯ case fatality risk ❯ generation time ❯ hospitalisation to death ❯ hospitalisation to discharge ❯ incubation period ❯ notification to death ❯ notification to discharge ❯ offspring distribution ❯ onset to death ❯ onset to discharge ❯ onset to hospitalisation ❯ onset to ventilation ❯ serial interval #> [[1]] #> Disease: Adenovirus #> Pathogen: Adenovirus #> Epi Parameter: incubation period #> Study: Lessler J, Reich N, Brookmeyer R, Perl T, Nelson K, Cummings D (2009). #> \"Incubation periods of acute respiratory viral infections: a systematic #> review.\" _The Lancet Infectious Diseases_. #> doi:10.1016/S1473-3099(09)70069-6 #> <https://doi.org/10.1016/S1473-3099%2809%2970069-6>. #> Distribution: lnorm (days) #> Parameters: #> meanlog: 1.723 #> sdlog: 0.231 #> #> [[2]] #> Disease: Human Coronavirus #> Pathogen: Human_Cov #> Epi Parameter: incubation period #> Study: Lessler J, Reich N, Brookmeyer R, Perl T, Nelson K, Cummings D (2009). #> \"Incubation periods of acute respiratory viral infections: a systematic #> review.\" _The Lancet Infectious Diseases_. #> doi:10.1016/S1473-3099(09)70069-7 #> <https://doi.org/10.1016/S1473-3099%2809%2970069-7>. #> Distribution: lnorm (days) #> Parameters: #> meanlog: 1.163 #> sdlog: 0.140 #> #> [[3]] #> Disease: SARS #> Pathogen: SARS-Cov-1 #> Epi Parameter: incubation period #> Study: Lessler J, Reich N, Brookmeyer R, Perl T, Nelson K, Cummings D (2009). #> \"Incubation periods of acute respiratory viral infections: a systematic #> review.\" _The Lancet Infectious Diseases_. #> doi:10.1016/S1473-3099(09)70069-8 #> <https://doi.org/10.1016/S1473-3099%2809%2970069-8>. #> Distribution: lnorm (days) #> Parameters: #> meanlog: 1.386 #> sdlog: 0.593 #> #> # ℹ 122 more elements #> # ℹ Use `print(n = ...)` to see more elements. #> # ℹ Use `parameter_tbl()` to see a summary table of the parameters. #> # ℹ Explore database online at: https://epiverse-trace.github.io/epiparameter/articles/database.html parameter_tbl(multi_epiparameter = db) #> # Parameter table: #> # A data frame: 125 × 7 #> disease pathogen epi_name prob_distribution author year sample_size #> <chr> <chr> <chr> <chr> <chr> <dbl> <dbl> #> 1 Adenovirus Adenovi… incubat… lnorm Lessl… 2009 14 #> 2 Human Coronavir… Human_C… incubat… lnorm Lessl… 2009 13 #> 3 SARS SARS-Co… incubat… lnorm Lessl… 2009 157 #> 4 Influenza Influen… incubat… lnorm Lessl… 2009 151 #> 5 Influenza Influen… incubat… lnorm Lessl… 2009 90 #> 6 Influenza Influen… incubat… lnorm Lessl… 2009 78 #> 7 Measles Measles… incubat… lnorm Lessl… 2009 55 #> 8 Parainfluenza Parainf… incubat… lnorm Lessl… 2009 11 #> 9 RSV RSV incubat… lnorm Lessl… 2009 24 #> 10 Rhinovirus Rhinovi… incubat… lnorm Lessl… 2009 28 #> # ℹ 115 more rows parameter_tbl(multi_epiparameter = db, disease = \"Ebola\") #> # Parameter table: #> # A data frame: 17 × 7 #> disease pathogen epi_name prob_distribution author year sample_size #> <chr> <chr> <chr> <chr> <chr> <dbl> <dbl> #> 1 Ebola Virus Dis… Ebola V… offspri… nbinom Lloyd… 2005 13 #> 2 Ebola Virus Dis… Ebola V… incubat… lnorm Eichn… 2011 196 #> 3 Ebola Virus Dis… Ebola V… onset t… gamma The E… 2018 14 #> 4 Ebola Virus Dis… Ebola V… incubat… gamma WHO E… 2015 1798 #> 5 Ebola Virus Dis… Ebola V… incubat… gamma WHO E… 2015 49 #> 6 Ebola Virus Dis… Ebola V… incubat… gamma WHO E… 2015 957 #> 7 Ebola Virus Dis… Ebola V… incubat… gamma WHO E… 2015 792 #> 8 Ebola Virus Dis… Ebola V… serial … gamma WHO E… 2015 305 #> 9 Ebola Virus Dis… Ebola V… serial … gamma WHO E… 2015 37 #> 10 Ebola Virus Dis… Ebola V… serial … gamma WHO E… 2015 147 #> 11 Ebola Virus Dis… Ebola V… serial … gamma WHO E… 2015 112 #> 12 Ebola Virus Dis… Ebola V… hospita… gamma WHO E… 2015 1167 #> 13 Ebola Virus Dis… Ebola V… hospita… gamma WHO E… 2015 1004 #> 14 Ebola Virus Dis… Ebola V… notific… gamma WHO E… 2015 2536 #> 15 Ebola Virus Dis… Ebola V… notific… gamma WHO E… 2015 1324 #> 16 Ebola Virus Dis… Ebola V… onset t… gamma WHO E… 2015 2741 #> 17 Ebola Virus Dis… Ebola V… onset t… gamma WHO E… 2015 1335"},{"path":"https://epiverse-trace.github.io/epiparameter/articles/epiparameter.html","id":"single-set-of-epidemiological-parameters","dir":"Articles","previous_headings":"","what":"Single set of epidemiological parameters","title":"Getting Started with {epiparameter}","text":"{epiparameter} introduces new class working epidemiological parameters R: <epiparameter>, contains name disease, name epidemiological distribution, parameters (available) citation information parameter source, well information. core data structure {epiparameter} package holds single set epidemiological parameters. <epiparameter> object can : Pulled database (epiparameter_db()) Created manually (using class constructor function: epiparameter()) arguments specified example using class constructor (epiparameter()) , example metadata parameter uncertainty (uncertainty) provided. See help documentation epiparameter() function using ?epiparameter see argument. Also see documentation <epiparameter> helper functions, e.g., ?create_citation(). Manually creating <epiparameter> objects can especially useful new parameter estimates become available yet incorporated {epiparameter} library. seen examples vignette, <epiparameter> class custom printing method shows disease, pathogen (known), epidemiological distribution, citation study parameters probability distribution parameter distribution (available).","code":"# <epiparameter> from database # fetch <epiparameter> for COVID-19 incubation period from database # return only a single <epiparameter> covid_incubation <- epiparameter_db( disease = \"COVID-19\", epi_name = \"incubation period\", single_epiparameter = TRUE ) #> Using Linton N, Kobayashi T, Yang Y, Hayashi K, Akhmetzhanov A, Jung S, Yuan #> B, Kinoshita R, Nishiura H (2020). \"Incubation Period and Other #> Epidemiological Characteristics of 2019 Novel Coronavirus Infections #> with Right Truncation: A Statistical Analysis of Publicly Available #> Case Data.\" _Journal of Clinical Medicine_. doi:10.3390/jcm9020538 #> <https://doi.org/10.3390/jcm9020538>.. #> To retrieve the citation use the 'get_citation' function covid_incubation #> Disease: COVID-19 #> Pathogen: SARS-CoV-2 #> Epi Parameter: incubation period #> Study: Linton N, Kobayashi T, Yang Y, Hayashi K, Akhmetzhanov A, Jung S, Yuan #> B, Kinoshita R, Nishiura H (2020). \"Incubation Period and Other #> Epidemiological Characteristics of 2019 Novel Coronavirus Infections #> with Right Truncation: A Statistical Analysis of Publicly Available #> Case Data.\" _Journal of Clinical Medicine_. doi:10.3390/jcm9020538 #> <https://doi.org/10.3390/jcm9020538>. #> Distribution: lnorm (days) #> Parameters: #> meanlog: 1.525 #> sdlog: 0.629 # <epiparameter> using constructor function covid_incubation <- epiparameter( disease = \"COVID-19\", pathogen = \"SARS-CoV-2\", epi_name = \"incubation period\", prob_distribution = create_prob_distribution( prob_distribution = \"gamma\", prob_distribution_params = c(shape = 2, scale = 1) ), summary_stats = create_summary_stats(mean = 2), citation = create_citation( author = person( given = list(\"John\", \"Amy\"), family = list(\"Smith\", \"Jones\") ), year = 2022, title = \"COVID Incubation Period\", journal = \"Epi Journal\", doi = \"10.27861182.x\" ) ) #> Using Smith J, Jones A (2022). \"COVID Incubation Period.\" _Epi Journal_. #> doi:10.27861182.x <https://doi.org/10.27861182.x>. #> To retrieve the citation use the 'get_citation' function covid_incubation #> Disease: COVID-19 #> Pathogen: SARS-CoV-2 #> Epi Parameter: incubation period #> Study: Smith J, Jones A (2022). \"COVID Incubation Period.\" _Epi Journal_. #> doi:10.27861182.x <https://doi.org/10.27861182.x>. #> Distribution: gamma (NA) #> Parameters: #> shape: 2.000 #> scale: 1.000"},{"path":"https://epiverse-trace.github.io/epiparameter/articles/epiparameter.html","id":"benefit-of-epiparameter","dir":"Articles","previous_headings":"","what":"Benefit of <epiparameter>","title":"Getting Started with {epiparameter}","text":"providing consistent robust object store epidemiological parameters, <epiparameter> objects can applied epidemiological pipelines, example {episoap}. data contained within object (e.g. parameter values, pathogen type, etc.) can modified pipeline continue operate class unchanged.","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/articles/epiparameter.html","id":"subsetting-database","dir":"Articles","previous_headings":"","what":"Subsetting database","title":"Getting Started with {epiparameter}","text":"database can subset directly epiparameter_db(). results can subset author. recommended use family name first author instead full name. first author matched entry source multiple authors. results can subset using subset argument, example subset = sample_size > 100 return entries sample size greater 100. See ?epiparameter_db() details use argument subset database entries get returned. single <epiparameter> required single_epiparameter argument can set TRUE return single set epidemiological parameters (.e. one delay distribution), available. multiple entries parameter library match search criteria (e.g. disease type) entries parameterised (.e. distribution parameters known), account right truncation inferred, estimated largest sample size preferentially selected (order).","code":"epiparameter_db( disease = \"COVID-19\", epi_name = \"incubation period\", author = \"Linton\" ) #> Returning 3 results that match the criteria (3 are parameterised). #> Use subset to filter by entry variables or single_epiparameter to return a single entry. #> To retrieve the citation for each use the 'get_citation' function #> # List of 3 <epiparameter> objects #> Number of diseases: 1 #> ❯ COVID-19 #> Number of epi parameters: 1 #> ❯ incubation period #> [[1]] #> Disease: COVID-19 #> Pathogen: SARS-CoV-2 #> Epi Parameter: incubation period #> Study: Linton N, Kobayashi T, Yang Y, Hayashi K, Akhmetzhanov A, Jung S, Yuan #> B, Kinoshita R, Nishiura H (2020). \"Incubation Period and Other #> Epidemiological Characteristics of 2019 Novel Coronavirus Infections #> with Right Truncation: A Statistical Analysis of Publicly Available #> Case Data.\" _Journal of Clinical Medicine_. doi:10.3390/jcm9020538 #> <https://doi.org/10.3390/jcm9020538>. #> Distribution: lnorm (days) #> Parameters: #> meanlog: 1.456 #> sdlog: 0.555 #> #> [[2]] #> Disease: COVID-19 #> Pathogen: SARS-CoV-2 #> Epi Parameter: incubation period #> Study: Linton N, Kobayashi T, Yang Y, Hayashi K, Akhmetzhanov A, Jung S, Yuan #> B, Kinoshita R, Nishiura H (2020). \"Incubation Period and Other #> Epidemiological Characteristics of 2019 Novel Coronavirus Infections #> with Right Truncation: A Statistical Analysis of Publicly Available #> Case Data.\" _Journal of Clinical Medicine_. doi:10.3390/jcm9020538 #> <https://doi.org/10.3390/jcm9020538>. #> Distribution: lnorm (days) #> Parameters: #> meanlog: 1.611 #> sdlog: 0.472 #> #> [[3]] #> Disease: COVID-19 #> Pathogen: SARS-CoV-2 #> Epi Parameter: incubation period #> Study: Linton N, Kobayashi T, Yang Y, Hayashi K, Akhmetzhanov A, Jung S, Yuan #> B, Kinoshita R, Nishiura H (2020). \"Incubation Period and Other #> Epidemiological Characteristics of 2019 Novel Coronavirus Infections #> with Right Truncation: A Statistical Analysis of Publicly Available #> Case Data.\" _Journal of Clinical Medicine_. doi:10.3390/jcm9020538 #> <https://doi.org/10.3390/jcm9020538>. #> Distribution: lnorm (days) #> Parameters: #> meanlog: 1.525 #> sdlog: 0.629 #> #> # ℹ Use `parameter_tbl()` to see a summary table of the parameters. #> # ℹ Explore database online at: https://epiverse-trace.github.io/epiparameter/articles/database.html epiparameter_db(disease = \"SARS\", single_epiparameter = TRUE) #> Using Lessler J, Reich N, Brookmeyer R, Perl T, Nelson K, Cummings D (2009). #> \"Incubation periods of acute respiratory viral infections: a systematic #> review.\" _The Lancet Infectious Diseases_. #> doi:10.1016/S1473-3099(09)70069-8 #> <https://doi.org/10.1016/S1473-3099%2809%2970069-8>.. #> To retrieve the citation use the 'get_citation' function #> Disease: SARS #> Pathogen: SARS-Cov-1 #> Epi Parameter: incubation period #> Study: Lessler J, Reich N, Brookmeyer R, Perl T, Nelson K, Cummings D (2009). #> \"Incubation periods of acute respiratory viral infections: a systematic #> review.\" _The Lancet Infectious Diseases_. #> doi:10.1016/S1473-3099(09)70069-8 #> <https://doi.org/10.1016/S1473-3099%2809%2970069-8>. #> Distribution: lnorm (days) #> Parameters: #> meanlog: 1.386 #> sdlog: 0.593"},{"path":"https://epiverse-trace.github.io/epiparameter/articles/epiparameter.html","id":"distribution-functions","dir":"Articles","previous_headings":"","what":"Distribution functions","title":"Getting Started with {epiparameter}","text":"<epiparameter> objects store distributions, mathematical functions distribution can easily extracted directly . often useful access probability density function, cumulative distribution function, quantiles distribution, generate random numbers distribution <epiparameter> object. distribution functions {epiparameter} allow users easily use .","code":"ebola_incubation <- epiparameter_db( disease = \"Ebola\", epi_name = \"incubation period\", single_epiparameter = TRUE ) #> Using WHO Ebola Response Team, Agua-Agum J, Ariyarajah A, Aylward B, Blake I, #> Brennan R, Cori A, Donnelly C, Dorigatti I, Dye C, Eckmanns T, Ferguson #> N, Formenty P, Fraser C, Garcia E, Garske T, Hinsley W, Holmes D, #> Hugonnet S, Iyengar S, Jombart T, Krishnan R, Meijers S, Mills H, #> Mohamed Y, Nedjati-Gilani G, Newton E, Nouvellet P, Pelletier L, #> Perkins D, Riley S, Sagrado M, Schnitzler J, Schumacher D, Shah A, Van #> Kerkhove M, Varsaneux O, Kannangarage N (2015). \"West African Ebola #> Epidemic after One Year — Slowing but Not Yet under Control.\" _The New #> England Journal of Medicine_. doi:10.1056/NEJMc1414992 #> <https://doi.org/10.1056/NEJMc1414992>.. #> To retrieve the citation use the 'get_citation' function density(ebola_incubation, at = 0.5) #> [1] 0.03608013 cdf(ebola_incubation, q = 0.5) #> [1] 0.01178094 quantile(ebola_incubation, p = 0.5) #> [1] 8.224347 generate(ebola_incubation, times = 10) #> [1] 7.089471 25.516370 29.272107 12.667909 13.194056 5.102120 6.318984 #> [8] 11.372114 9.391204 9.740407"},{"path":"https://epiverse-trace.github.io/epiparameter/articles/epiparameter.html","id":"plotting-epidemiological-distributions","dir":"Articles","previous_headings":"","what":"Plotting epidemiological distributions","title":"Getting Started with {epiparameter}","text":"<epiparameter> objects can easily plotted see PDF CDF distribution. default plotting range time since infection zero 99th quantile distribution. can altered specifying xlim argument plotting <epiparameter> object. plotting function can useful visually comparing epidemiological distributions different publications disease. addition, plotting distribution manually creating <epiparameter> help check parameters sensible produce expected distribution.","code":"plot(ebola_incubation) plot(ebola_incubation, xlim = c(1, 25))"},{"path":"https://epiverse-trace.github.io/epiparameter/articles/epiparameter.html","id":"accessors","dir":"Articles","previous_headings":"Plotting epidemiological distributions","what":"Accessors","title":"Getting Started with {epiparameter}","text":"<epiparameter> class also accessor functions can help access elements object standardised format.","code":"get_parameters(ebola_incubation) #> shape scale #> 1.577781 6.528155 get_citation(ebola_incubation) #> WHO Ebola Response Team, Agua-Agum J, Ariyarajah A, Aylward B, Blake I, #> Brennan R, Cori A, Donnelly C, Dorigatti I, Dye C, Eckmanns T, Ferguson #> N, Formenty P, Fraser C, Garcia E, Garske T, Hinsley W, Holmes D, #> Hugonnet S, Iyengar S, Jombart T, Krishnan R, Meijers S, Mills H, #> Mohamed Y, Nedjati-Gilani G, Newton E, Nouvellet P, Pelletier L, #> Perkins D, Riley S, Sagrado M, Schnitzler J, Schumacher D, Shah A, Van #> Kerkhove M, Varsaneux O, Kannangarage N (2015). \"West African Ebola #> Epidemic after One Year — Slowing but Not Yet under Control.\" _The New #> England Journal of Medicine_. doi:10.1056/NEJMc1414992 #> <https://doi.org/10.1056/NEJMc1414992>."},{"path":[]},{"path":"https://epiverse-trace.github.io/epiparameter/articles/epiparameter.html","id":"conversion","dir":"Articles","previous_headings":"Parameter conversion and extraction","what":"Conversion","title":"Getting Started with {epiparameter}","text":"Parameters often reported literature mean standard deviation (variance). summary statistics can often (analytically) converted parameters distribution using conversion function package (convert_summary_stats_to_params()). also provide conversion functions opposite direction, parameters summary statistics (convert_params_to_summary_stats()).","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/articles/epiparameter.html","id":"extraction","dir":"Articles","previous_headings":"Parameter conversion and extraction","what":"Extraction","title":"Getting Started with {epiparameter}","text":"functions extract_param() handles extraction parameter estimates summary statistics. two extractions currently supported {epiparameter} percentiles median range.","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/articles/epiparameter.html","id":"adding-library-entries-and-contributing-to-epiparameter","dir":"Articles","previous_headings":"","what":"Adding library entries and contributing to {epiparameter}","title":"Getting Started with {epiparameter}","text":"set epidemiological parameter inferred known user yet incorporated {epiparameter} database, parameters can manually added library. Note adds parameters library environment, save database file package. Hence, restart R session, lose changes. library epidemiological parameters living database, new studies published hope incorporate . Searching recording parameters database extremely time-consuming, welcome contributions new parameters either making pull request package adding information contributing spreadsheet. incorporated database package maintainers contributions acknowledged. See Data Collation Synthesis Protocol vignette information contributing library epidemiological parameters.","code":"# wrap <epiparameter> in list to append to database new_db <- append(db, covid_incubation) tail(new_db, n = 3) #> [[1]] #> Disease: Chikungunya #> Pathogen: Chikungunya Virus #> Epi Parameter: generation time #> Study: Guzzetta G, Vairo F, Mammone A, Lanini S, Poletti P, Manica M, Rosa R, #> Caputo B, Solimini A, della Torre A, Scognamiglio P, Zumla A, Ippolito #> G, Merler S (2020). \"Spatial modes for transmission of chikungunya #> virus during a large chikungunya outbreak in Italy: a modeling #> analysis.\" _BMC Medicine_. doi:10.1186/s12916-020-01674-y #> <https://doi.org/10.1186/s12916-020-01674-y>. #> Distribution: gamma (days) #> Parameters: #> shape: 8.633 #> scale: 1.447 #> #> [[2]] #> Disease: Chikungunya #> Pathogen: Chikungunya Virus #> Epi Parameter: case fatality risk #> Study: de Souza W, de Lima S, Mello L, Candido D, Buss L, Whittaker C, Claro #> I, Chandradeva N, Granja F, de Jesus R, Lemos P, Toledo-Teixeira D, #> Barbosa P, Firmino A, Amorim M, Duarte L, Pessoa Jr I, Forato J, #> Vasconcelos I, Maximo A, Araújo E, Mello L, Sabino E, Proença-Módena J, #> Faria N, Weaver S (2023). \"Spatiotemporal dynamics and recurrence of #> chikungunya virus in Brazil: an epidemiological study.\" _The Lancet #> Microbe_. doi:10.1016/S2666-5247(23)00033-2 #> <https://doi.org/10.1016/S2666-5247%2823%2900033-2>. #> Parameters: <no parameters> #> Mean: 1.3 (deaths per 1000 cases) #> #> [[3]] #> Disease: COVID-19 #> Pathogen: SARS-CoV-2 #> Epi Parameter: incubation period #> Study: Smith J, Jones A (2022). \"COVID Incubation Period.\" _Epi Journal_. #> doi:10.27861182.x <https://doi.org/10.27861182.x>. #> Distribution: gamma (NA) #> Parameters: #> shape: 2.000 #> scale: 1.000"},{"path":[]},{"path":"https://epiverse-trace.github.io/epiparameter/articles/extract-bias.html","id":"extraction-by-percentiles","dir":"Articles","previous_headings":"Extraction Bias","what":"Extraction by percentiles","title":"{epiparameter} Extraction Bias Analysis","text":"First explore extraction percentiles. study reports percentiles distribution, usually symmetrical (e.g. 5th 95th, 2.5th 97.5th). However, instances, asymmetrical percentiles available. test whether asymmetry varying degrees influences bias parameter extraction distributions. set parameter space explore: Now can run extraction point parameter space. set seed control stochasticity estimating parameters, however changing removing seed drastically change results interpretation. extract_param() function re-runs optimisation convergence set tolerance achieved (maximum number iterations reached) reliably return global optimum. theory, help minimise bias instability parameter estimation. See function documentation (?extract_param()) Conversion Extraction vignette details. extraction bias can explored:","code":"distributions <- c(\"gamma\", \"lnorm\", \"weibull\") dist_parameters <- seq(0.5, 2, 0.5) lower_percentiles <- c(2.5, 5, 25, 40) upper_percentiles <- c(60, 95, 97.5) parameters_perc <- expand.grid( dist = distributions, param_1 = dist_parameters, param_2 = dist_parameters, lower = lower_percentiles, upper = upper_percentiles ) # calculate the degree of asymmetry for each percentile combination lw_interval_diff <- abs(0 - parameters_perc$lower) up_interval_diff <- abs(100 - parameters_perc$upper) deg_asym <- abs(lw_interval_diff - up_interval_diff) # add degree of asymmetry to percentiles parameters_perc <- cbind(parameters_perc, deg_asym) # divide percentiles by 100 to make them probabilities for quantile functions parameters_perc$lower <- parameters_perc$lower / 100 parameters_perc$upper <- parameters_perc$upper / 100 set.seed(1) estim_params <- vector(\"list\", nrow(parameters_perc)) # Loop through parameter space estimating parameters for (params_idx in seq_len(nrow(parameters_perc))) { dist <- as.character(parameters_perc[params_idx, \"dist\"]) percen <- unname(unlist(parameters_perc[params_idx, c(\"lower\", \"upper\")])) if (dist == \"lnorm\") { true_values <- do.call( paste0(\"q\", dist), list( p = percen, meanlog = parameters_perc[params_idx, \"param_1\"], sdlog = parameters_perc[params_idx, \"param_2\"] ) ) } else { true_values <- do.call( paste0(\"q\", dist), list( p = percen, shape = parameters_perc[params_idx, \"param_1\"], scale = parameters_perc[params_idx, \"param_2\"] ) ) } # message about stochastic optimisation suppressed estim_params[[params_idx]] <- suppressMessages( extract_param( type = \"percentiles\", values = true_values, distribution = dist, percentiles = percen ) ) } # combine results results <- cbind(parameters_perc, do.call(rbind, estim_params)) colnames(results) <- c( \"dist\", \"param_1\", \"param_2\", \"lower\", \"upper\", \"deg_asym\", \"estim_param_1\", \"estim_param_2\" ) # calculate absolute difference between true parameter and estimated value results <- cbind( results, diff_param_1 = abs(results$param_1 - results$estim_param_1), diff_param_2 = abs(results$param_2 - results$estim_param_2) ) # plot differences by distribution ggplot(data = results) + geom_point(mapping = aes( x = diff_param_1, y = diff_param_2, colour = deg_asym )) + scale_x_continuous(name = \"Parameter 1 Difference (|true - estimated|)\") + scale_y_continuous(name = \"Parameter 2 Difference (|true - estimated|)\") + labs(colour = \"Percentile Asym.\") + theme_bw() + scale_color_viridis_c() + facet_wrap(facets = vars(dist), scales = \"free\") + theme( strip.background = element_blank(), axis.text.x = element_text(angle = 30, vjust = 0.5) )"},{"path":"https://epiverse-trace.github.io/epiparameter/articles/extract-bias.html","id":"extraction-by-median-and-range","dir":"Articles","previous_headings":"Extraction Bias","what":"Extraction by median and range","title":"{epiparameter} Extraction Bias Analysis","text":"analysis can repeated, time using summary statistic possibly reported studies: median range data. extraction number samples used infer distribution required can impact possible range exhibited data. Set parameter space: Plot results:","code":"n_samples <- c(10, 50, 100) parameters_range <- expand.grid( dist = distributions, # same as above param_1 = dist_parameters, # same as above param_2 = dist_parameters, # same as above n_samples = n_samples ) estim_params <- vector(\"list\", nrow(parameters_range)) # Loop through parameter space estimating parameters for (params_idx in seq_len(nrow(parameters_range))) { dist <- as.character(parameters_range[params_idx, \"dist\"]) n_samples <- parameters_range[params_idx, \"n_samples\"] # while loop to ensure values are min < median < max resample_values <- TRUE while (resample_values) { if (dist == \"lnorm\") { true_median <- do.call( paste0(\"q\", dist), list( p = 0.5, meanlog = parameters_range[params_idx, \"param_1\"], sdlog = parameters_range[params_idx, \"param_2\"] ) ) true_range <- do.call( paste0(\"r\", dist), list( n = n_samples, meanlog = parameters_range[params_idx, \"param_1\"], sdlog = parameters_range[params_idx, \"param_2\"] ) ) true_range <- c(min(true_range), max(true_range)) } else { true_median <- do.call( paste0(\"q\", dist), list( p = 0.5, shape = parameters_range[params_idx, \"param_1\"], scale = parameters_range[params_idx, \"param_2\"] ) ) true_range <- do.call( paste0(\"r\", dist), list( n = n_samples, shape = parameters_range[params_idx, \"param_1\"], scale = parameters_range[params_idx, \"param_2\"] ) ) true_range <- c(min(true_range), max(true_range)) } true_values <- c(true_median, true_range) if (true_values[2] < true_values[1] && true_values[1] < true_values[3]) { resample_values <- FALSE } } # message about stochastic optimisation suppressed estim_params[[params_idx]] <- suppressMessages( expr = extract_param( type = \"range\", values = true_values, distribution = dist, samples = n_samples ) ) } #> Warning: Maximum optimisation iterations reached, returning result early. #> Result may not be reliable. # combine results results <- cbind(parameters_range, do.call(rbind, estim_params)) colnames(results) <- c( \"dist\", \"param_1\", \"param_2\", \"n_samples\", \"estim_param_1\", \"estim_param_2\" ) # calculate absolute difference between true parameter and estimated value results <- cbind( results, diff_param_1 = abs(results$param_1 - results$estim_param_1), diff_param_2 = abs(results$param_2 - results$estim_param_2) ) # plot differences by distribution ggplot(data = results) + geom_point( mapping = aes( x = diff_param_1, y = diff_param_2, colour = n_samples ) ) + scale_x_continuous(name = \"Parameter 1 Difference (|true - estimated|)\") + scale_y_continuous(name = \"Parameter 2 Difference (|true - estimated|)\") + labs(colour = \"No. Samples\") + theme_bw() + scale_color_viridis_c() + facet_wrap(facets = vars(dist), scales = \"free\") + theme( strip.background = element_blank(), axis.text.x = element_text(angle = 30, vjust = 0.5) )"},{"path":[]},{"path":"https://epiverse-trace.github.io/epiparameter/articles/extract-bias.html","id":"extraction-by-percentiles-1","dir":"Articles","previous_headings":"Extraction precision","what":"Extraction by percentiles","title":"{epiparameter} Extraction Bias Analysis","text":"two analyses used single extraction (replicate), however, may estimation parameters unstable given set percentiles median range. Therefore, finish test whether repeated extraction parameters single percentile large variance indicate parameter extraction unstable, imprecise, potentially untrustworthy. use parameter space percentiles defined (parameters_perc). Now can run extraction set replicates compute variance parameter estimates replicates.","code":"estim_param_var <- vector(\"list\", nrow(parameters_perc)) # Loop through parameter space estimating parameters for (params_idx in seq_len(nrow(parameters_perc))) { dist <- as.character(parameters_perc[params_idx, \"dist\"]) percen <- unname(unlist(parameters_perc[params_idx, c(\"lower\", \"upper\")])) if (dist == \"lnorm\") { true_values <- do.call( paste0(\"q\", dist), list( p = percen, meanlog = parameters_perc[params_idx, \"param_1\"], sdlog = parameters_perc[params_idx, \"param_2\"] ) ) } else { true_values <- do.call( paste0(\"q\", dist), list( p = percen, shape = parameters_perc[params_idx, \"param_1\"], scale = parameters_perc[params_idx, \"param_2\"] ) ) } # message about stochastic optimisation suppressed estim <- suppressMessages( replicate( n = 5, expr = extract_param( type = \"percentiles\", values = true_values, distribution = dist, percentiles = percen ) ) ) estim_param_var[[params_idx]] <- apply(estim, MARGIN = 1, FUN = var) } # combine results results <- cbind(parameters_perc, do.call(rbind, estim_param_var)) colnames(results) <- c( \"dist\", \"param_1\", \"param_2\", \"lower\", \"upper\", \"deg_asym\", \"estim_param_1_var\", \"estim_param_2_var\" ) ggplot(data = results) + geom_point(mapping = aes( x = estim_param_1_var, y = estim_param_2_var, colour = deg_asym )) + scale_x_continuous(name = \"Parameter 1 Variance\") + scale_y_continuous(name = \"Parameter 2 Variance\") + labs(colour = \"Percentile Asym.\") + theme_bw() + scale_color_viridis_c() + facet_wrap(facets = vars(dist), scales = \"free\") + theme( strip.background = element_blank(), axis.text.x = element_text(angle = 30, vjust = 0.5) )"},{"path":"https://epiverse-trace.github.io/epiparameter/articles/extract-bias.html","id":"extraction-by-median-and-range-1","dir":"Articles","previous_headings":"Extraction precision","what":"Extraction by median and range","title":"{epiparameter} Extraction Bias Analysis","text":"test estimation precision can performed extraction median range. plots vignette, bias low precision high extracting parameters gamma, lognormal Weibull distributions percentiles distribution median range data set. asymmetry percentiles sample size data noticeably influence bias parameter extraction. However, ensure reliable extract use cases extract_param() function recommend checking output spurious results.","code":"estim_param_var <- vector(\"list\", nrow(parameters_range)) # Loop through parameter space estimating parameters for (params_idx in seq_len(nrow(parameters_range))) { dist <- as.character(parameters_range[params_idx, \"dist\"]) n_samples <- parameters_range[params_idx, \"n_samples\"] # while loop to ensure values are min < median < max resample_values <- TRUE while (resample_values) { if (dist == \"lnorm\") { true_median <- do.call( paste0(\"q\", dist), list( p = 0.5, meanlog = parameters_range[params_idx, \"param_1\"], sdlog = parameters_range[params_idx, \"param_2\"] ) ) true_range <- do.call( paste0(\"r\", dist), list( n = n_samples, meanlog = parameters_range[params_idx, \"param_1\"], sdlog = parameters_range[params_idx, \"param_2\"] ) ) true_range <- c(min(true_range), max(true_range)) } else { true_median <- do.call( paste0(\"q\", dist), list( p = 0.5, shape = parameters_range[params_idx, \"param_1\"], scale = parameters_range[params_idx, \"param_2\"] ) ) true_range <- do.call( paste0(\"r\", dist), list( n = n_samples, shape = parameters_range[params_idx, \"param_1\"], scale = parameters_range[params_idx, \"param_2\"] ) ) true_range <- c(min(true_range), max(true_range)) } true_values <- c(true_median, true_range) if (true_values[2] < true_values[1] && true_values[1] < true_values[3]) { resample_values <- FALSE } } # message about stochastic optimisation suppressed estim <- suppressMessages( replicate( n = 5, expr = extract_param( type = \"range\", values = true_values, distribution = dist, samples = n_samples ) ) ) estim_param_var[[params_idx]] <- apply(estim, MARGIN = 1, FUN = var) } #> Warning: Maximum optimisation iterations reached, returning result early. #> Result may not be reliable. # combine results results <- cbind(parameters_range, do.call(rbind, estim_param_var)) colnames(results) <- c( \"dist\", \"param_1\", \"param_2\", \"n_samples\", \"estim_param_1_var\", \"estim_param_2_var\" ) ggplot(data = results) + geom_point(mapping = aes( x = estim_param_1_var, y = estim_param_2_var, colour = n_samples )) + scale_x_continuous(name = \"Parameter 1 Variance\") + scale_y_continuous(name = \"Parameter 2 Variance\") + labs(colour = \"No. Samples\") + theme_bw() + scale_color_viridis_c() + facet_wrap(facets = vars(dist), scales = \"free\") + theme( strip.background = element_blank(), axis.text.x = element_text(angle = 30, vjust = 0.5) )"},{"path":"https://epiverse-trace.github.io/epiparameter/articles/extract_convert.html","id":"conversion-versus-extraction","dir":"Articles","previous_headings":"","what":"Conversion versus extraction","title":"Parameter extraction and conversion in {epiparameter}","text":"Use conversion possible extraction avoid possible limitations associated numerical optimisation used extraction function extract_param().","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/articles/extract_convert.html","id":"conversions","dir":"Articles","previous_headings":"","what":"Conversions","title":"Parameter extraction and conversion in {epiparameter}","text":"two conversion functions {epiparameter}: convert_params_to_summary_stats() convert_summary_stats_to_params(). convert_params_to_summary_stats() converts one set statistical distribution parameters common summary statistics, convert_summary_stats_to_params() converts summary statistics set parameters.","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/articles/extract_convert.html","id":"conversion-functions","dir":"Articles","previous_headings":"Conversions","what":"Conversion functions","title":"Parameter extraction and conversion in {epiparameter}","text":"conversion functions can take two types inputs first argument: character string distribution <epiparameter> object. conversion functions two arguments. first (x) defines distribution want use second (...) lets put many named parameters summary statistics required. arguments passed ... matched name, therefore need match exactly names expected. See function documentation (?convert_params_to_summary_stats ?convert_summary_stats_to_params names). case <epiparameter> object supplied, parameters summary statistics required conversion nothing needs given extra arguments (.e. ...). currently supported summary statistic conversions {epiparameter} given distribution.","code":""},{"path":[]},{"path":"https://epiverse-trace.github.io/epiparameter/articles/extract_convert.html","id":"using-a-character-string-to-name-distribution","dir":"Articles","previous_headings":"Conversions > Conversion functions","what":"Using a character string to name distribution","title":"Parameter extraction and conversion in {epiparameter}","text":"","code":"convert_params_to_summary_stats(\"gamma\", shape = 2.5, scale = 1.5) #> $mean #> [1] 3.75 #> #> $median #> [1] 1.450487 #> #> $mode #> [1] 2.25 #> #> $var #> [1] 5.625 #> #> $sd #> [1] 2.371708 #> #> $cv #> [1] 0.6324555 #> #> $skewness #> [1] 1.264911 #> #> $ex_kurtosis #> [1] 2.4 convert_summary_stats_to_params(\"gamma\", mean = 2, sd = 2) #> $shape #> [1] 1 #> #> $scale #> [1] 2 convert_summary_stats_to_params(\"gamma\", mean = 2, var = 2) #> $shape #> [1] 2 #> #> $scale #> [1] 1 convert_summary_stats_to_params(\"gamma\", mean = 2, cv = 2) #> $shape #> [1] 0.25 #> #> $scale #> [1] 8"},{"path":"https://epiverse-trace.github.io/epiparameter/articles/extract_convert.html","id":"using-an-epiparameter","dir":"Articles","previous_headings":"Conversions > Conversion functions","what":"Using an <epiparameter>","title":"Parameter extraction and conversion in {epiparameter}","text":"example parameters provided <epiparameter> example <epiparameter> missing parameters supplied ... example summary statistics provided <epiparameter> example <epiparameter> missing summary statistics supplied ... usage <epiparameter> repeated every distribution conversion available {epiparameter}. conversions shown distribution also available using <epiparameter> object, either parameters summary statistics stored <epiparameter> supplied via named arguments.","code":"ep <- epiparameter( disease = \"<Disease name>\", pathogen = \"<Pathogen name>\", epi_name = \"<Epidemilogical Distribution name>\", prob_distribution = create_prob_distribution( prob_distribution = \"gamma\", prob_distribution_params = c(shape = 2.5, scale = 1.5) ) ) #> Citation cannot be created as author, year, journal or title is missing convert_params_to_summary_stats(ep) #> $mean #> [1] 3.75 #> #> $median #> [1] 1.450487 #> #> $mode #> [1] 2.25 #> #> $var #> [1] 5.625 #> #> $sd #> [1] 2.371708 #> #> $cv #> [1] 0.6324555 #> #> $skewness #> [1] 1.264911 #> #> $ex_kurtosis #> [1] 2.4 ep <- epiparameter( disease = \"<Disease name>\", pathogen = \"<Pathogen name>\", epi_name = \"<Epidemilogical Distribution name>\", prob_distribution = \"gamma\" ) #> Citation cannot be created as author, year, journal or title is missing #> No adequate summary statistics available to calculate the parameters of the gamma distribution #> Unparameterised <epiparameter> object convert_params_to_summary_stats(ep, shape = 2.5, scale = 1.5) #> $mean #> [1] 3.75 #> #> $median #> [1] 1.450487 #> #> $mode #> [1] 2.25 #> #> $var #> [1] 5.625 #> #> $sd #> [1] 2.371708 #> #> $cv #> [1] 0.6324555 #> #> $skewness #> [1] 1.264911 #> #> $ex_kurtosis #> [1] 2.4 ep <- epiparameter( disease = \"<Disease name>\", pathogen = \"<Pathogen name>\", epi_name = \"<Epidemilogical Distribution name>\", prob_distribution = \"gamma\", summary_stats = create_summary_stats(mean = 3.75, sd = 2.37) ) #> Citation cannot be created as author, year, journal or title is missing #> Parameterising the probability distribution with the summary statistics. #> Probability distribution is assumed not to be discretised or truncated. convert_summary_stats_to_params(ep) #> $shape #> [1] 2.503605 #> #> $scale #> [1] 1.49784 ep <- epiparameter( disease = \"<Disease name>\", pathogen = \"<Pathogen name>\", epi_name = \"<Epidemilogical Distribution name>\", prob_distribution = \"gamma\" ) #> Citation cannot be created as author, year, journal or title is missing #> No adequate summary statistics available to calculate the parameters of the gamma distribution #> Unparameterised <epiparameter> object convert_summary_stats_to_params(ep, mean = 3.75, sd = 2.37) #> $shape #> [1] 2.503605 #> #> $scale #> [1] 1.49784"},{"path":"https://epiverse-trace.github.io/epiparameter/articles/extract_convert.html","id":"lognormal-distribution","dir":"Articles","previous_headings":"Conversions > Conversion functions","what":"Lognormal distribution","title":"Parameter extraction and conversion in {epiparameter}","text":"","code":"convert_params_to_summary_stats(\"lnorm\", meanlog = 2.5, sdlog = 1.5) #> $mean #> [1] 37.52472 #> #> $median #> [1] 12.18249 #> #> $mode #> [1] 1.284025 #> #> $var #> [1] 11951.62 #> #> $sd #> [1] 109.3235 #> #> $cv #> [1] 2.913372 #> #> $skewness #> [1] 33.46805 #> #> $ex_kurtosis #> [1] 10075.25 convert_summary_stats_to_params(\"lnorm\", mean = 2, sd = 2) #> $meanlog #> [1] 0.3465736 #> #> $sdlog #> [1] 0.8325546 convert_summary_stats_to_params(\"lnorm\", mean = 2, var = 2) #> $meanlog #> [1] 0.4904146 #> #> $sdlog #> [1] 0.6367614 convert_summary_stats_to_params(\"lnorm\", mean = 2, cv = 2) #> $meanlog #> [1] -0.1115718 #> #> $sdlog #> [1] 1.268636 convert_summary_stats_to_params(\"lnorm\", median = 2, sd = 2) #> $meanlog #> [1] 0.3465736 #> #> $sdlog #> [1] 0.8325546 convert_summary_stats_to_params(\"lnorm\", median = 2, var = 2) #> $meanlog #> [1] 0.4904146 #> #> $sdlog #> [1] 0.6367614"},{"path":"https://epiverse-trace.github.io/epiparameter/articles/extract_convert.html","id":"weibull-distribution","dir":"Articles","previous_headings":"Conversions > Conversion functions","what":"Weibull distribution","title":"Parameter extraction and conversion in {epiparameter}","text":"","code":"convert_params_to_summary_stats(\"weibull\", shape = 2.5, scale = 1.5) #> $mean #> [1] 1.330896 #> #> $median #> [1] 1.295452 #> #> $mode #> [1] 1.22279 #> #> $var #> [1] 0.3243301 #> #> $sd #> [1] 0.5694998 #> #> $cv #> [1] 0.4279072 #> #> $skewness #> [1] 0.3586318 #> #> $ex_kurtosis #> [1] -0.1432169 convert_summary_stats_to_params(\"weibull\", mean = 2, sd = 2) #> Numerical approximation used, results may be unreliable. #> $shape #> [1] 1.000016 #> #> $scale #> [1] 2.000014 convert_summary_stats_to_params(\"weibull\", mean = 2, var = 2) #> Numerical approximation used, results may be unreliable. #> $shape #> [1] 1.435521 #> #> $scale #> [1] 2.202641 convert_summary_stats_to_params(\"weibull\", mean = 2, cv = 2) #> Numerical approximation used, results may be unreliable. #> $shape #> [1] 0.5427068 #> #> $scale #> [1] 1.150547"},{"path":"https://epiverse-trace.github.io/epiparameter/articles/extract_convert.html","id":"negative-binomial-distribution","dir":"Articles","previous_headings":"Conversions > Conversion functions","what":"Negative binomial distribution","title":"Parameter extraction and conversion in {epiparameter}","text":"","code":"convert_params_to_summary_stats(\"nbinom\", prob = 0.5, dispersion = 0.5) #> $mean #> [1] 0.5 #> #> $median #> [1] 0 #> #> $mode #> [1] 0 #> #> $var #> [1] 1 #> #> $sd #> [1] 1 #> #> $cv #> [1] 2 #> #> $skewness #> [1] 3 #> #> $ex_kurtosis #> [1] 12.25 convert_summary_stats_to_params(\"nbinom\", mean = 1, sd = 1) #> $prob #> [1] 1 #> #> $dispersion #> [1] Inf convert_summary_stats_to_params(\"nbinom\", mean = 1, var = 1) #> $prob #> [1] 1 #> #> $dispersion #> [1] Inf convert_summary_stats_to_params(\"nbinom\", mean = 1, cv = 1) #> $prob #> [1] 1 #> #> $dispersion #> [1] Inf"},{"path":"https://epiverse-trace.github.io/epiparameter/articles/extract_convert.html","id":"geometric-distribution","dir":"Articles","previous_headings":"Conversions > Conversion functions","what":"Geometric distribution","title":"Parameter extraction and conversion in {epiparameter}","text":"","code":"convert_params_to_summary_stats(\"geom\", prob = 0.5) #> $mean #> [1] 1 #> #> $median #> [1] 0 #> #> $mode #> [1] 0 #> #> $var #> [1] 2 #> #> $sd #> [1] 1.414214 #> #> $cv #> [1] 1.414214 #> #> $skewness #> [1] 2.12132 #> #> $ex_kurtosis #> [1] 6.5 convert_summary_stats_to_params(\"geom\", mean = 1) #> $prob #> [1] 0.5"},{"path":"https://epiverse-trace.github.io/epiparameter/articles/extract_convert.html","id":"extraction","dir":"Articles","previous_headings":"","what":"Extraction","title":"Parameter extraction and conversion in {epiparameter}","text":"two methods extraction implemented {epiparameter}. One estimate parameters given values two percentiles, estimate parameters given median range data. extractions implemented extract_param() function. demonstrate extraction using percentiles. type \"percentiles\", values values reported percentiles, given vector. percentiles, given 0 1, specified vector percentiles. example uses values 1 10 2.5th 97.5th percentile, respectively. example estimate parameters gamma distribution, extraction also implemented lognormal, normal Weibull distributions, specifying \"lnorm\", \"norm\" \"weibull\". message shown running extract_param() make user aware estimates completely reliable due use numerical optimisation. Rerunning function finding parameters returned indicates successfully converged. issue mostly overcome internal setup extract_param() function searches convergence consistent parameter estimates returning user. alternative extraction, median range, can achieved specifying type = \"range\" using samples argument instead percentiles argument. using type = \"percentiles\" samples argument ignored using type = \"range\" percentiles argument ignored. section mentioned extract_param() internal mechanism check parameters consistently converged estimates several optimisation iterations. tolerance convergence number times optimisation can repeated specified control argument extract_param(). set default (tolerance = 1e-5 max_iter = 1000), thus need specified user (shown examples). case maximum number optimisation iterations reached, calculation terminates returning recent optimisation result user along warning message. reasoning default maximum number iterations limit computation time prevent function cycling optimisation routines without converging consistent answer. runtime important parameter accuracy paramount maximum number iterations can increased tolerance decreased. control settings work identically extracting percentiles median range. Donnelly et al. (2003) provides mean variance gamma distribution incubation period SARS. conversion can achieved using general conversion function (convert_summary_stats_to_params()).","code":"extract_param( type = \"percentiles\", values = c(1, 10), distribution = \"gamma\", percentiles = c(0.025, 0.975) ) #> Stochastic numerical optimisation used. #> Rerun function multiple times to check global optimum is found #> shape scale #> 3.358202 1.284186 extract_param( type = \"range\", values = c(10, 5, 15), distribution = \"lnorm\", samples = 25 ) #> Stochastic numerical optimisation used. #> Rerun function multiple times to check global optimum is found #> meanlog sdlog #> 2.302584 3.939920 # set seed to ensure warning is produced set.seed(1) # lower maximum iteration to show warning extract_param( type = \"range\", values = c(10, 1, 25), distribution = \"lnorm\", samples = 100, control = list(max_iter = 100) ) #> Warning: Maximum optimisation iterations reached, returning result early. #> Result may not be reliable. #> Stochastic numerical optimisation used. #> Rerun function multiple times to check global optimum is found #> meanlog sdlog #> 2.3025851 0.7942061 # SARS gamma mean and var to shape and scale convert_summary_stats_to_params(\"gamma\", mean = 6.37, var = 16.7) #> $shape #> [1] 2.429754 #> #> $scale #> [1] 2.621664"},{"path":"https://epiverse-trace.github.io/epiparameter/articles/extract_convert.html","id":"use-cases","dir":"Articles","previous_headings":"Extraction","what":"Use cases","title":"Parameter extraction and conversion in {epiparameter}","text":"present examples published epidemiological parameters distributions functions outlined can applied get parameters distribution. 75th percentiles reported lognormal distribution Nolen et al. (2016) incubation period mpox (monkeypox). median range provided Thornhill et al. (2022) mpox, want calculate parameters lognormal distribution.","code":"# Mpox lnorm from 75th percentiles in WHO data extract_param( type = \"percentiles\", values = c(6, 13), distribution = \"lnorm\", percentiles = c(0.125, 0.875) ) #> Stochastic numerical optimisation used. #> Rerun function multiple times to check global optimum is found #> meanlog sdlog #> 2.1783544 0.3360684 # Mpox lnorm from median and range in 2022: extract_param( type = \"range\", values = c(7, 3, 20), distribution = \"lnorm\", samples = 23 ) #> Stochastic numerical optimisation used. #> Rerun function multiple times to check global optimum is found #> meanlog sdlog #> 1.945910 4.735285"},{"path":"https://epiverse-trace.github.io/epiparameter/articles/extract_convert.html","id":"assuming-distributions","dir":"Articles","previous_headings":"Extraction","what":"Assuming distributions","title":"Parameter extraction and conversion in {epiparameter}","text":"can case study report summary statistics unspecified distribution just raw data. cases parameterised distribution required downstream analysis functional, parametric, form may assumed. distribution delay distribution (.e. serial interval incubation period) can often sensible assume right-skewed distribution : gamma, lognormal Weibull distributions. also commonly fit distributions epidemiological analysis delay distributions. However, one take care assuming distribution may drastically influence interpretation application epidemiological parameters.","code":""},{"path":[]},{"path":"https://epiverse-trace.github.io/epiparameter/authors.html","id":null,"dir":"","previous_headings":"","what":"Authors","title":"Authors and Citation","text":"Joshua W. Lambert. Author, maintainer, copyright holder. Adam Kucharski. Author, copyright holder. Carmen Tamayo. Author. Hugo Gruson. Contributor, reviewer. Sebastian Funk. Contributor. Pratik Gupte. Reviewer. James M. Azam. Reviewer. Chris Hartgerink. Reviewer. Tim Taylor. Reviewer.","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/authors.html","id":"citation","dir":"","previous_headings":"","what":"Citation","title":"Authors and Citation","text":"Lambert J, Kucharski , Tamayo C (2025). epiparameter: Library Epidemiological Parameters Helper Functions Classes. doi:10.5281/zenodo.11110881, https://epiverse-trace.github.io/epiparameter/.","code":"@Manual{, title = {epiparameter: Library of Epidemiological Parameters with Helper Functions and Classes}, author = {Joshua W. Lambert and Adam Kucharski and Carmen Tamayo}, year = {2025}, doi = {10.5281/zenodo.11110881}, url = {https://epiverse-trace.github.io/epiparameter/}, }"},{"path":"https://epiverse-trace.github.io/epiparameter/index.html","id":"epiparameter-","dir":"","previous_headings":"","what":"Library of Epidemiological Parameters with Helper Functions and Classes","title":"Library of Epidemiological Parameters with Helper Functions and Classes","text":"epiparameter R package contains library epidemiological parameters infectious diseases well classes helper functions work data. also includes functions extract convert parameters reported summary statistics. epiparameter developed Centre Mathematical Modelling Infectious Diseases London School Hygiene Tropical Medicine part Epiverse-TRACE.","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/index.html","id":"installation","dir":"","previous_headings":"","what":"Installation","title":"Library of Epidemiological Parameters with Helper Functions and Classes","text":"development version epiparameter can installed GitHub using pak package: Alternatively, install pre-compiled binaries Epiverse TRACE R-universe","code":"# check whether {pak} is installed if(!require(\"pak\")) install.packages(\"pak\") pak::pak(\"epiverse-trace/epiparameter\") install.packages(\"epiparameter\", repos = c(\"https://epiverse-trace.r-universe.dev\", \"https://cloud.r-project.org\"))"},{"path":"https://epiverse-trace.github.io/epiparameter/index.html","id":"quick-start","dir":"","previous_headings":"","what":"Quick start","title":"Library of Epidemiological Parameters with Helper Functions and Classes","text":"load library epidemiological parameters R: results list database entries. entry library <epiparameter> object. Alternatively, library epiparameters can viewed vignette locally (vignette(\"database\", package = \"epiparameter\")) {epiparameter} website. results can filtered disease epidemiological distribution. set single_epiparameter = TRUE want single database entry returned, default (single_epiparameter = FALSE) return database entries match disease (disease) epidemiological parameter (epi_name). quickly view list epidemiological distributions returned epiparameter_db() table, parameter_tbl() gives summary data, offers ability subset data disease, pathogen epidemiological parameter (epi_name). <epiparameter> object can plotted. CDF can also plotted setting cumulative = TRUE.","code":"library(epiparameter) epiparameters <- epiparameter_db() #> Returning 125 results that match the criteria (100 are parameterised). #> Use subset to filter by entry variables or single_epiparameter to return a single entry. #> To retrieve the citation for each use the 'get_citation' function epiparameters #> # List of 125 <epiparameter> objects #> Number of diseases: 23 #> ❯ Adenovirus ❯ Chikungunya ❯ COVID-19 ❯ Dengue ❯ Ebola Virus Disease ❯ Hantavirus Pulmonary Syndrome ❯ Human Coronavirus ❯ Influenza ❯ Japanese Encephalitis ❯ Marburg Virus Disease ❯ Measles ❯ MERS ❯ Mpox ❯ Parainfluenza ❯ Pneumonic Plague ❯ Rhinovirus ❯ Rift Valley Fever ❯ RSV ❯ SARS ❯ Smallpox ❯ West Nile Fever ❯ Yellow Fever ❯ Zika Virus Disease #> Number of epi parameters: 13 #> ❯ case fatality risk ❯ generation time ❯ hospitalisation to death ❯ hospitalisation to discharge ❯ incubation period ❯ notification to death ❯ notification to discharge ❯ offspring distribution ❯ onset to death ❯ onset to discharge ❯ onset to hospitalisation ❯ onset to ventilation ❯ serial interval #> [[1]] #> Disease: Adenovirus #> Pathogen: Adenovirus #> Epi Parameter: incubation period #> Study: Lessler J, Reich N, Brookmeyer R, Perl T, Nelson K, Cummings D (2009). #> \"Incubation periods of acute respiratory viral infections: a systematic #> review.\" _The Lancet Infectious Diseases_. #> doi:10.1016/S1473-3099(09)70069-6 #> <https://doi.org/10.1016/S1473-3099%2809%2970069-6>. #> Distribution: lnorm #> Parameters: #> meanlog: 1.723 #> sdlog: 0.231 #> #> [[2]] #> Disease: Human Coronavirus #> Pathogen: Human_Cov #> Epi Parameter: incubation period #> Study: Lessler J, Reich N, Brookmeyer R, Perl T, Nelson K, Cummings D (2009). #> \"Incubation periods of acute respiratory viral infections: a systematic #> review.\" _The Lancet Infectious Diseases_. #> doi:10.1016/S1473-3099(09)70069-7 #> <https://doi.org/10.1016/S1473-3099%2809%2970069-7>. #> Distribution: lnorm #> Parameters: #> meanlog: 1.163 #> sdlog: 0.140 #> #> [[3]] #> Disease: SARS #> Pathogen: SARS-Cov-1 #> Epi Parameter: incubation period #> Study: Lessler J, Reich N, Brookmeyer R, Perl T, Nelson K, Cummings D (2009). #> \"Incubation periods of acute respiratory viral infections: a systematic #> review.\" _The Lancet Infectious Diseases_. #> doi:10.1016/S1473-3099(09)70069-8 #> <https://doi.org/10.1016/S1473-3099%2809%2970069-8>. #> Distribution: lnorm #> Parameters: #> meanlog: 1.386 #> sdlog: 0.593 #> #> # ℹ 122 more elements #> # ℹ Use `print(n = ...)` to see more elements. #> # ℹ Use `parameter_tbl()` to see a summary table of the parameters. #> # ℹ Explore database online at: https://epiverse-trace.github.io/epiparameter/articles/database.html influenza_incubation <- epiparameter_db( disease = \"influenza\", epi_name = \"incubation period\", single_epiparameter = TRUE ) #> Using Virlogeux V, Li M, Tsang T, Feng L, Fang V, Jiang H, Wu P, Zheng J, Lau #> E, Cao Y, Qin Y, Liao Q, Yu H, Cowling B (2015). \"Estimating the #> Distribution of the Incubation Periods of Human Avian Influenza A(H7N9) #> Virus Infections.\" _American Journal of Epidemiology_. #> doi:10.1093/aje/kwv115 <https://doi.org/10.1093/aje/kwv115>.. #> To retrieve the citation use the 'get_citation' function influenza_incubation #> Disease: Influenza #> Pathogen: Influenza-A-H7N9 #> Epi Parameter: incubation period #> Study: Virlogeux V, Li M, Tsang T, Feng L, Fang V, Jiang H, Wu P, Zheng J, Lau #> E, Cao Y, Qin Y, Liao Q, Yu H, Cowling B (2015). \"Estimating the #> Distribution of the Incubation Periods of Human Avian Influenza A(H7N9) #> Virus Infections.\" _American Journal of Epidemiology_. #> doi:10.1093/aje/kwv115 <https://doi.org/10.1093/aje/kwv115>. #> Distribution: weibull #> Parameters: #> shape: 2.101 #> scale: 3.839 parameter_tbl(epiparameters) #> # Parameter table: #> # A data frame: 125 × 7 #> disease pathogen epi_name prob_distribution author year sample_size #> <chr> <chr> <chr> <chr> <chr> <dbl> <dbl> #> 1 Adenovirus Adenovi… incubat… lnorm Lessl… 2009 14 #> 2 Human Coronavir… Human_C… incubat… lnorm Lessl… 2009 13 #> 3 SARS SARS-Co… incubat… lnorm Lessl… 2009 157 #> 4 Influenza Influen… incubat… lnorm Lessl… 2009 151 #> 5 Influenza Influen… incubat… lnorm Lessl… 2009 90 #> 6 Influenza Influen… incubat… lnorm Lessl… 2009 78 #> 7 Measles Measles… incubat… lnorm Lessl… 2009 55 #> 8 Parainfluenza Parainf… incubat… lnorm Lessl… 2009 11 #> 9 RSV RSV incubat… lnorm Lessl… 2009 24 #> 10 Rhinovirus Rhinovi… incubat… lnorm Lessl… 2009 28 #> # ℹ 115 more rows parameter_tbl( epiparameters, epi_name = \"onset to hospitalisation\" ) #> # Parameter table: #> # A data frame: 5 × 7 #> disease pathogen epi_name prob_distribution author year sample_size #> <chr> <chr> <chr> <chr> <chr> <dbl> <dbl> #> 1 MERS MERS-Cov onset to hospi… <NA> Assir… 2013 23 #> 2 COVID-19 SARS-CoV-2 onset to hospi… gamma Linto… 2020 155 #> 3 COVID-19 SARS-CoV-2 onset to hospi… gamma Linto… 2020 34 #> 4 COVID-19 SARS-CoV-2 onset to hospi… lnorm Linto… 2020 155 #> 5 COVID-19 SARS-CoV-2 onset to hospi… lnorm Linto… 2020 34 plot(influenza_incubation) plot(influenza_incubation, cumulative = TRUE)"},{"path":"https://epiverse-trace.github.io/epiparameter/index.html","id":"parameter-conversion-and-extraction","dir":"","previous_headings":"Quick start","what":"Parameter conversion and extraction","title":"Library of Epidemiological Parameters with Helper Functions and Classes","text":"parameters distribution can converted mean standard deviation. epiparameter implement variety distributions: gamma lognormal Weibull negative binomial geometric parameters probability distribution can also extracted summary statistics, example, percentiles distribution, median range data. can done : gamma lognormal Weibull normal","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/index.html","id":"contributing-to-library-of-epidemiological-parameters","dir":"","previous_headings":"","what":"Contributing to library of epidemiological parameters","title":"Library of Epidemiological Parameters with Helper Functions and Classes","text":"like contribute different epidemiological parameters stored epiparameter package, can add data public google sheet. spreadsheet contains two example entries guide fields can accept. monitoring sheet new entries subsequently included package. Alternatively, parameters can added JSON file holding data base directly via Pull Request. can find explanation accepted entries column data dictionary.","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/index.html","id":"help","dir":"","previous_headings":"","what":"Help","title":"Library of Epidemiological Parameters with Helper Functions and Classes","text":"report bug please open issue","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/index.html","id":"contribute","dir":"","previous_headings":"","what":"Contribute","title":"Library of Epidemiological Parameters with Helper Functions and Classes","text":"Contributions epiparameter welcomed. package contributing guide.","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/index.html","id":"code-of-conduct","dir":"","previous_headings":"","what":"Code of Conduct","title":"Library of Epidemiological Parameters with Helper Functions and Classes","text":"Please note epiparameter project released Contributor Code Conduct. contributing project, agree abide terms.","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/index.html","id":"citing-this-package","dir":"","previous_headings":"","what":"Citing this package","title":"Library of Epidemiological Parameters with Helper Functions and Classes","text":"","code":"citation(\"epiparameter\") #> To cite package 'epiparameter' in publications use: #> #> Lambert J, Kucharski A, Tamayo C (2024). _epiparameter: Library of #> Epidemiological Parameters with Helper Functions and Classes_. #> doi:10.5281/zenodo.11110881 #> <https://doi.org/10.5281/zenodo.11110881>, #> <https://epiverse-trace.github.io/epiparameter/>. #> #> A BibTeX entry for LaTeX users is #> #> @Manual{, #> title = {epiparameter: Library of Epidemiological Parameters with Helper Functions and Classes}, #> author = {Joshua W. Lambert and Adam Kucharski and Carmen Tamayo}, #> year = {2024}, #> doi = {10.5281/zenodo.11110881}, #> url = {https://epiverse-trace.github.io/epiparameter/}, #> }"},{"path":"https://epiverse-trace.github.io/epiparameter/reference/aggregate.multi_epiparameter.html","id":null,"dir":"Reference","previous_headings":"","what":"Aggregate multiple <epiparameter> objects into a single <epiparameter> object. — aggregate.multi_epiparameter","title":"Aggregate multiple <epiparameter> objects into a single <epiparameter> object. — aggregate.multi_epiparameter","text":"Combine list <epiparameter> objects single <epiparameter> mixture distribution [see distributional::dist_mixture()]. aggregated <epiparameter> returned aggregate() can used density(), cdf(), quantile() generate() methods combined distributions.","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/aggregate.multi_epiparameter.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Aggregate multiple <epiparameter> objects into a single <epiparameter> object. — aggregate.multi_epiparameter","text":"","code":"# S3 method for class 'multi_epiparameter' aggregate(x, weighting = c(\"equal\", \"sample_size\", \"custom\"), ..., weights)"},{"path":"https://epiverse-trace.github.io/epiparameter/reference/aggregate.multi_epiparameter.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Aggregate multiple <epiparameter> objects into a single <epiparameter> object. — aggregate.multi_epiparameter","text":"x <multi_epiparameter> object. weighting character string type weighting use create mixture distribution. Options : \"equal\" equal weighting across distributions, \"sample_size\" using sample size <epiparameter> object weight distribution (sample sizes normalised), \"custom\" allows vector weights passed weights argument custom weighting. ... dots used, warn extra arguments passed function. weights numeric vector equal length number <epiparameter> objects passed x. weights required weighting = \"custom\".","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/aggregate.multi_epiparameter.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Aggregate multiple <epiparameter> objects into a single <epiparameter> object. — aggregate.multi_epiparameter","text":"<epiparameter> object","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/aggregate.multi_epiparameter.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Aggregate multiple <epiparameter> objects into a single <epiparameter> object. — aggregate.multi_epiparameter","text":"aggregate() method requires <epiparameter> objects parameterised <distribution> objects (distributional package). means unparameterised (see is_parameterised()) discretised (see discretise()) distributions aggregated function error.","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/aggregate.multi_epiparameter.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Aggregate multiple <epiparameter> objects into a single <epiparameter> object. — aggregate.multi_epiparameter","text":"","code":"ebola_si <- epiparameter_db(epi_name = \"serial interval\", disease = \"ebola\") #> Returning 4 results that match the criteria (4 are parameterised). #> Use subset to filter by entry variables or single_epiparameter to return a single entry. #> To retrieve the citation for each use the 'get_citation' function aggregate(ebola_si) #> Disease: Ebola Virus Disease #> Pathogen: Ebola Virus #> Epi Parameter: serial interval #> Study: WHO Ebola Response Team, Agua-Agum J, Ariyarajah A, Aylward B, Blake I, #> Brennan R, Cori A, Donnelly C, Dorigatti I, Dye C, Eckmanns T, Ferguson #> N, Formenty P, Fraser C, Garcia E, Garske T, Hinsley W, Holmes D, #> Hugonnet S, Iyengar S, Jombart T, Krishnan R, Meijers S, Mills H, #> Mohamed Y, Nedjati-Gilani G, Newton E, Nouvellet P, Pelletier L, #> Perkins D, Riley S, Sagrado M, Schnitzler J, Schumacher D, Shah A, Van #> Kerkhove M, Varsaneux O, Kannangarage N (2015). “West African Ebola #> Epidemic after One Year — Slowing but Not Yet under Control.” _The New #> England Journal of Medicine_. doi:10.1056/NEJMc1414992 #> <https://doi.org/10.1056/NEJMc1414992>. #> Distribution: mixture: gamma, gamma, gamma, gamma (days) #> Distribution: mixture: gamma, gamma, gamma, gamma (days) #> Distribution: mixture: gamma, gamma, gamma, gamma (days) #> Distribution: mixture: gamma, gamma, gamma, gamma (days) #> Parameters: #> dist.shape: 2.188 #> dist.rate: 0.154 #> dist.shape: 4.903 #> dist.rate: 0.316 #> dist.shape: 2.068 #> dist.rate: 0.137 #> dist.shape: 1.898 #> dist.rate: 0.153 #> w1: 0.250 #> w2: 0.250 #> w3: 0.250 #> w4: 0.250"},{"path":"https://epiverse-trace.github.io/epiparameter/reference/as.data.frame.epiparameter.html","id":null,"dir":"Reference","previous_headings":"","what":"as.data.frame() method for <epiparameter> class — as.data.frame.epiparameter","title":"as.data.frame() method for <epiparameter> class — as.data.frame.epiparameter","text":".data.frame() method <epiparameter> class","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/as.data.frame.epiparameter.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"as.data.frame() method for <epiparameter> class — as.data.frame.epiparameter","text":"","code":"# S3 method for class 'epiparameter' as.data.frame(x, ...)"},{"path":"https://epiverse-trace.github.io/epiparameter/reference/as.data.frame.epiparameter.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"as.data.frame() method for <epiparameter> class — as.data.frame.epiparameter","text":"x <epiparameter> object. ... dots used, extra arguments supplied cause warning.","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/as.data.frame.epiparameter.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"as.data.frame() method for <epiparameter> class — as.data.frame.epiparameter","text":"<data.frame> single row.","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/as.data.frame.epiparameter.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"as.data.frame() method for <epiparameter> class — as.data.frame.epiparameter","text":"<data.frame> returned contain atomic columns (.e. one object per row), columns lists (.e. multiple objects per row). list columns can contain lists S3 objects (e.g. <bibentry> object citation column).","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/as.data.frame.epiparameter.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"as.data.frame() method for <epiparameter> class — as.data.frame.epiparameter","text":"","code":"ep <- epiparameter_db(single_epiparameter = TRUE) #> Using Linton N, Kobayashi T, Yang Y, Hayashi K, Akhmetzhanov A, Jung S, Yuan #> B, Kinoshita R, Nishiura H (2020). “Incubation Period and Other #> Epidemiological Characteristics of 2019 Novel Coronavirus Infections #> with Right Truncation: A Statistical Analysis of Publicly Available #> Case Data.” _Journal of Clinical Medicine_. doi:10.3390/jcm9020538 #> <https://doi.org/10.3390/jcm9020538>.. #> To retrieve the citation use the 'get_citation' function as.data.frame(ep) #> disease pathogen epi_name prob_distribution uncertainty #> 1 COVID-19 SARS-CoV-2 onset to hospitalisation lN(0.95,.... list(unc.... #> summary_stats citation metadata method_assess #> 1 9.7, c(5.... list(aut.... days, 15.... TRUE, TR.... #> notes #> 1 This dataset includes only surviving patients. This method applies right-truncation but only fits a lognormal distribution."},{"path":"https://epiverse-trace.github.io/epiparameter/reference/as.data.frame.multi_epiparameter.html","id":null,"dir":"Reference","previous_headings":"","what":"as.data.frame() method for <multi_epiparameter> class — as.data.frame.multi_epiparameter","title":"as.data.frame() method for <multi_epiparameter> class — as.data.frame.multi_epiparameter","text":".data.frame() method <multi_epiparameter> class","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/as.data.frame.multi_epiparameter.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"as.data.frame() method for <multi_epiparameter> class — as.data.frame.multi_epiparameter","text":"","code":"# S3 method for class 'multi_epiparameter' as.data.frame(x, ...)"},{"path":"https://epiverse-trace.github.io/epiparameter/reference/as.data.frame.multi_epiparameter.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"as.data.frame() method for <multi_epiparameter> class — as.data.frame.multi_epiparameter","text":"x <multi_epiparameter> object. ... dots used, extra arguments supplied cause warning.","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/as.data.frame.multi_epiparameter.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"as.data.frame() method for <multi_epiparameter> class — as.data.frame.multi_epiparameter","text":"<data.frame> many rows length input list.","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/as.data.frame.multi_epiparameter.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"as.data.frame() method for <multi_epiparameter> class — as.data.frame.multi_epiparameter","text":"<data.frame> returned contain atomic columns (.e. one object per row), columns lists (.e. multiple objects per row). list columns can contain lists S3 objects (e.g. <bibentry> object citation column).","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/as.data.frame.multi_epiparameter.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"as.data.frame() method for <multi_epiparameter> class — as.data.frame.multi_epiparameter","text":"","code":"db <- epiparameter_db() #> Returning 125 results that match the criteria (100 are parameterised). #> Use subset to filter by entry variables or single_epiparameter to return a single entry. #> To retrieve the citation for each use the 'get_citation' function as.data.frame(db) #> disease pathogen #> 1 Adenovirus Adenovirus #> 2 Human Coronavirus Human_Cov #> 3 SARS SARS-Cov-1 #> 4 Influenza Influenza-A #> 5 Influenza Influenza-A #> 6 Influenza Influenza-B #> 7 Measles Measles Virus #> 8 Parainfluenza Parainfluenza Virus #> 9 RSV RSV #> 10 Rhinovirus Rhinovirus #> 11 Influenza Influenza-A #> 12 Influenza Influenza-A #> 13 RSV RSV #> 14 RSV RSV #> 15 Influenza Influenza-A-H1N1 #> 16 Influenza Influenza-A-H1N1 #> 17 Influenza Influenza-A-H7N9 #> 18 Influenza Influenza-A-H7N9 #> 19 Influenza Influenza-A-H7N9 #> 20 Influenza Influenza-A-H7N9 #> 21 Influenza Influenza-A-H7N9 #> 22 Influenza Influenza-A-H1N1 #> 23 Influenza Influenza-A-H1N1Pdm #> 24 Influenza Influenza-A-H1N1Pdm #> 25 Influenza Influenza-A-H1N1 #> 26 Influenza Influenza-A-H1N1 #> 27 Marburg Virus Disease Marburg Virus #> 28 Marburg Virus Disease Marburg Virus #> 29 Marburg Virus Disease Marburg Virus #> 30 Marburg Virus Disease Marburg Virus #> 31 Marburg Virus Disease Marburg Virus #> 32 SARS SARS-Cov-1 #> 33 SARS SARS-Cov-1 #> 34 Smallpox Smallpox-Variola-Major #> 35 Smallpox Smallpox-Variola-Major #> 36 Smallpox Smallpox-Variola-Minor #> 37 Smallpox Smallpox-Variola-Minor #> 38 Mpox Monkeypox Virus #> 39 Pneumonic Plague Yersinia Pestis #> 40 Hantavirus Pulmonary Syndrome Hantavirus (Andes Virus) #> 41 Ebola Virus Disease Ebola Virus #> 42 Dengue Dengue Virus #> 43 Dengue Dengue Virus #> 44 Dengue Dengue Virus #> 45 Zika Virus Disease Zika Virus #> 46 Chikungunya Chikungunya Virus #> 47 Dengue Dengue Virus #> 48 Dengue Dengue Virus #> 49 Japanese Encephalitis Japanese Encephalitis Virus #> 50 Rift Valley Fever Rift Valley Fever Virus #> 51 West Nile Fever West Nile Virus #> 52 West Nile Fever West Nile Virus #> 53 West Nile Fever West Nile Virus #> 54 Yellow Fever Yellow Fever Viruses #> 55 Yellow Fever Yellow Fever Viruses #> 56 Mpox Mpox Virus #> 57 Mpox Mpox Virus #> 58 Mpox Mpox Virus #> 59 Mpox Mpox Virus #> 60 Mpox Mpox Virus #> 61 Mpox Mpox Virus #> 62 Mpox Mpox Virus #> 63 Ebola Virus Disease Ebola Virus-Zaire Subtype #> 64 Ebola Virus Disease Ebola Virus-Zaire Subtype #> 65 Ebola Virus Disease Ebola Virus #> 66 Ebola Virus Disease Ebola Virus #> 67 Ebola Virus Disease Ebola Virus #> 68 Ebola Virus Disease Ebola Virus #> 69 Ebola Virus Disease Ebola Virus #> 70 Ebola Virus Disease Ebola Virus #> 71 Ebola Virus Disease Ebola Virus #> 72 Ebola Virus Disease Ebola Virus #> 73 Ebola Virus Disease Ebola Virus #> 74 Ebola Virus Disease Ebola Virus #> 75 Ebola Virus Disease Ebola Virus #> 76 Ebola Virus Disease Ebola Virus #> 77 Ebola Virus Disease Ebola Virus #> 78 Ebola Virus Disease Ebola Virus #> 79 MERS MERS-Cov #> 80 MERS MERS-Cov #> 81 MERS MERS-Cov #> 82 MERS MERS-Cov #> 83 MERS MERS-Cov #> 84 MERS MERS-Cov #> 85 MERS MERS-Cov #> 86 MERS MERS-Cov #> 87 COVID-19 SARS-CoV-2 #> 88 COVID-19 SARS-CoV-2 #> 89 COVID-19 SARS-CoV-2 #> 90 COVID-19 SARS-CoV-2 #> 91 COVID-19 SARS-CoV-2 #> 92 COVID-19 SARS-CoV-2 #> 93 COVID-19 SARS-CoV-2 #> 94 COVID-19 SARS-CoV-2 #> 95 COVID-19 SARS-CoV-2 #> 96 COVID-19 SARS-CoV-2 #> 97 COVID-19 SARS-CoV-2 #> 98 COVID-19 SARS-CoV-2 #> 99 COVID-19 SARS-CoV-2 #> 100 COVID-19 SARS-CoV-2 #> 101 COVID-19 SARS-CoV-2 #> 102 COVID-19 SARS-CoV-2 #> 103 COVID-19 SARS-CoV-2 #> 104 COVID-19 SARS-CoV-2 #> 105 COVID-19 SARS-CoV-2 #> 106 COVID-19 SARS-CoV-2 #> 107 COVID-19 SARS-CoV-2 #> 108 COVID-19 SARS-CoV-2 #> 109 COVID-19 SARS-CoV-2 #> 110 COVID-19 SARS-CoV-2 #> 111 COVID-19 SARS-CoV-2 #> 112 COVID-19 SARS-CoV-2 #> 113 COVID-19 SARS-CoV-2 #> 114 Mpox Mpox Virus #> 115 Mpox Mpox Virus Clade I #> 116 Mpox Mpox Virus #> 117 Mpox Mpox Virus Clade I #> 118 Mpox Mpox Virus Clade IIa #> 119 Mpox Mpox Virus Clade IIb #> 120 Mpox Mpox Virus #> 121 Mpox Mpox Virus #> 122 Mpox Mpox Virus #> 123 Chikungunya Chikungunya Virus #> 124 Chikungunya Chikungunya Virus #> 125 Chikungunya Chikungunya Virus #> epi_name prob_distribution uncertainty summary_stats #> 1 incubation period lN(1.7, .... list(unc.... c(`25` =.... #> 2 incubation period lN(1.2, .... list(unc.... c(`25` =.... #> 3 incubation period lN(1.4, .... list(unc.... c(`5` = .... #> 4 incubation period lN(0.34,.... list(unc.... c(`5` = .... #> 5 incubation period lN(0.64,.... list(unc.... c(`5` = .... #> 6 incubation period lN(-0.51.... list(unc.... c(`5` = .... #> 7 incubation period lN(2.5, .... list(unc.... c(`5` = .... #> 8 incubation period lN(0.96,.... list(unc.... c(`25` =.... #> 9 incubation period lN(1.5, .... list(unc.... c(`5` = .... #> 10 incubation period lN(0.64,.... list(unc.... c(`5` = .... #> 11 incubation period lN(0.38,.... list(unc.... c(`5` = .... #> 12 incubation period lN(0.36,.... list(unc.... c(`5` = .... #> 13 incubation period lN(1.5, .... list(unc.... c(`5` = .... #> 14 incubation period lN(1.5, .... list(unc.... c(`5` = .... #> 15 incubation period Γ(3.3, 2) list(ci_.... c(`95` =.... #> 16 incubation period Weibull(.... list(ci_.... c(`95` =.... #> 17 incubation period Weibull(.... list(unc.... 3.4, c(3.... #> 18 incubation period Γ(1.9, 0.41) list(unc.... 4.5, c(2.... #> 19 incubation period weibull list(ci_.... 3.5, c(3.... #> 20 incubation period Weibull(.... list(ci_.... 3.7, c(3.... #> 21 incubation period Weibull(.... list(ci_.... 3.3, c(2.... #> 22 incubation period lnorm list(ci_.... 4.3, c(2.... #> 23 incubation period Γ(18, 8.5) list(unc.... 2.05, 0.49 #> 24 serial interval Γ(2.6, 1) list(unc.... 2.51, 1.55 #> 25 incubation period lN(0.34,.... list(unc.... c(`5` = .... #> 26 generation time Weibull(.... list(ci_.... c(`5` = .... #> 27 incubation period NA list(ci_.... 2, 26 #> 28 incubation period NA list(ci_.... 7, 2, 13 #> 29 serial interval NA list(ci_.... c(`25` =.... #> 30 onset to death NA list(ci_.... 8, 2, 16 #> 31 serial interval Γ(2.8, 0.31) list(unc.... 9, c(8.2.... #> 32 offspring distribution NB(0.16,.... list(ci_.... #> 33 offspring distribution NB(0.17,.... list(ci_.... #> 34 offspring distribution NB(0.37,.... list(ci_.... #> 35 offspring distribution NB(0.32,.... list(ci_.... #> 36 offspring distribution NB(0.65,.... list(ci_.... #> 37 offspring distribution NB(0.72,.... list(ci_.... #> 38 offspring distribution 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Demonstration of two types of illness characterized by long and short incubation periods and diff erent clinical features. J Clin Invest 1947; 26: 957–82. #> 2 Analysis on data from Bradburne AF, Bynoe ML, Tyrrell DA. Eff ects of a “new” human respiratory virus in volunteers. Br Med J 1967; 3: 767–69. #> 3 Pooled analysis on several data sets, see Lessler et al. 2009 for references of datasets #> 4 Pooled analysis on several data sets, see Lessler et al. 2009 for references of datasets #> 5 These estimates for the incubation period of influenza A from Lessler et al. 2009 are different from the estimates from the complete data, as they remove Henle et al. 1945 J Immunol, as it is an outlier in the dataset (n=61). Values found at the bottom Table 3. #> 6 Pooled analysis on several data sets, see Lessler et al. 2009 for references of datasets #> 7 Pooled analysis on several data sets, see Lessler et al. 2009 for references of datasets #> 8 Pooled analysis on several data sets, see Lessler et al. 2009 for references of datasets #> 9 Pooled analysis on several data sets, see Lessler et al. 2009 for references of datasets #> 10 Pooled analysis on several data sets, see Lessler et al. 2009 for references of datasets #> 11 Data from Lessler et al 2009 using double interval-censored analysis. Not open source #> 12 Data from Lessler et al 2009 using single interval-censored analysis. Not open source #> 13 Data from Lessler et al 2009 using double interval-censored analysis. Not open source #> 14 Data from Lessler et al 2009 using single interval-censored analysis. Not open source #> 15 Gamma and weibull distributions had equally good fit to the data. This entry is the gamma distribution. Gamma, exponential. Not open source. #> 16 Gamma and weibull distributions had equally good fit to the data. This entry is the weibull distribution. Weibull, exponential #> 17 This study used an original data set and a modified data set. This weibull distribution was fitted to the modified data set and it is recommended to use this one. #> 18 This study used an original data set and a modified data set. This gamma distribution was fitted to the original data set and it is recommended to use the weibull distribution that was fitted to the modified data set. #> 19 This study fit the weibull distribution to estimate the parameters for the complete data set, those who had a fatal outcome and those with a non-fatal outcome. This is the distribution fit to the complete unpartitioned data. #> 20 This study fit the weibull distribution to estimate the parameters for the complete data set, those who had a fatal outcome and those with a non-fatal outcome. This is the distribution fit to the fatal outcome data. #> 21 This study fit the weibull distribution to estimate the parameters for the complete data set, those who had a fatal outcome and those with a non-fatal outcome. This is the distribution fit to the non-fatal outcome data. #> 22 The mid-point of the exposure time was used to approximate an exact exposure time instead of interval-censoring. This can lead to a possible bias (overestimation) in incubation times. It was ambiguously reported whether the mean is the mean of the distribution or the meanlog parameter of the lognormal distribution. #> 23 No additional notes #> 24 No additional notes #> 25 No additional notes. #> 26 The parameters of the weibull are stated without reporting the uncertainty around them. The parameter estimates and sample size is reported in the supplementary appendix. #> 27 This paper did not fit a distribution to the incubation period data and only reported a lower and upper range of the data. This is present in the database as there are no other studies that report the incubation period for Marburg virus. There is another incubation period reported from the same paper for a subset of the data which report the median and interquartile range but again do not fit a distribution to the data. #> 28 This paper did not fit a distribution to the incubation period data and only reported a median and range for a subset of the data. This is present in the database as there are no other studies that report the incubation period for Marburg virus. This paper also reports the maximum and minimum for the complete data set. #> 29 This paper did not fit a distribution to the serial interval data and only reported a median and interquartile range. This is present in the database as there are no other studies that report the serial interval for Marburg virus. #> 30 This paper reports the median and range of the symptom onset to death delay but did not fit a parametric distribution to the data. This is included in the database as it is the only reported symptom onset to death reported in the literature #> 31 The generation time is estimated from non-human viral load data. This paper reports the generation time but assumes the generation time and serial interval are the same it is classified as serial interval here based on Van Kerkove et al. 2015 <10.1038/sdata.2015.19>. The sample size is take from Van Kerkove et al. 2015. #> 32 Parameter estimates are retrieved from the supplementary tables. #> 33 No additional notes #> 34 No additional notes #> 35 No additional notes #> 36 No additional notes #> 37 Estimate of R0 taken from original study and CI of dispersion calculated mean of Z and proportion of zeros known #> 38 In the model comparison the geometric model was the better fit to the monkeypox data, however, only the parameters of the negative binomial were reported as so are stored in the database. #> 39 In the model comparison the geometric model was the better fit to the Pneumonic Plague data, however, only the parameters of the negative binomial were reported as so are stored in the database. #> 40 In the model comparison the geometric model was the better fit to the Hantavirus data, however, only the parameters of the negative binomial were reported as so are stored in the database. The uncertainty for the dispersion parameter is currently not stored in the database as the upper bound for the confidence interval is infinite, and currently infinite values are not supported. #> 41 In the model comparison the poisson model was the better fit to the Ebola data, however, only the parameters of the negative binomial were reported as so are stored in the database. The uncertainty for the dispersion parameter is currently not stored in the database as the upper bound for the confidence interval is infinite, and currently infinite values are not supported. #> 42 Extrinsic incubation period for data at 25 degrees celcius #> 43 Extrinsic incubation period for data at 30 degrees celcius #> 44 Standard deviation, meanlog and sdlog is taken from Siraj et al. 2017 <10.1371/journal.pntd.0005797> #> 45 Pooled analysis on several data sets, see Lessler et al. 2016 for references of datasets #> 46 Pooled analysis on several data sets, see Rudolph et al 2014 for references of datasets #> 47 Pooled analysis on several data sets, see Rudolph et al 2014 for references of datasets #> 48 Pooled analysis on several data sets, see Rudolph et al 2014 for references of datasets. This is a subset of data containing only mosquito-transmitted infections #> 49 Pooled analysis on several data sets, see Rudolph et al 2014 for references of datasets #> 50 Pooled analysis on several data sets, see Rudolph et al 2014 for references of datasets. Of the 18 samples at least 17 of them are not trasmitted by mosquitoes #> 51 Pooled analysis on several data sets, see Rudolph et al 2014 for references of datasets #> 52 Pooled analysis on several data sets, see Rudolph et al 2014 for references of datasets. This is a subset of data containing only mosquito-transmitted infections #> 53 Pooled analysis on several data sets, see Rudolph et al 2014 for references of datasets. This is a subset of data containing only tramsission by transplant or transfusion. #> 54 Pooled analysis on several data sets, see Rudolph et al 2014 for references of datasets #> 55 Pooled analysis on several data sets, see Rudolph et al 2014 for references of datasets. This is a subset of data containing only mosquito-transmitted infections #> 56 No additional notes #> 57 Uses the methods described by Lessler (10.2471/BLT.16.174540) and Reich (10.1002/sim.3659). Estimated from time from exposure to first symptom onset #> 58 No additional notes #> 59 Meanlog, sdlog, and fitted distribution from supplementary material. Uses cases from Charniga 2022 + extra cases. Incubation period as exposure to rash onset. #> 60 Meanlog, sdlog, and fitted distribution from supplementary material. Uses cases from Charniga 2022 + extra cases. Incubation period as exposure to symptom onset. #> 61 Shape and scale from supp. material. Serial interval as exposure to symptom onset #> 62 Shape and scale from supp. material. Serial interval as exposure to rash onset #> 63 The paper reports lower and upper supported ranges for the mean and standard deviation but it is not clear if these are confidence intervals or not so are not included in the database #> 64 Data extracted from Appendix. The mean, sd, shape and scale are taken from the paper, the conversion between the two does not match exactly. The data used to estimate the onset-to-death distribution is not from the DRC outbreak but from the west african outbreak. #> 65 Data extracted from Appendix. This data comes from the entire period of the west africa ebola outbreak up to the point the paper was published (Dec 2013 - 25 Nov 2014). The methods fitting are reported in another paper: DOI: 10.1056/NEJMoa1411100 #> 66 This data comes from the entire period of the Guinea ebola outbreak up to the point the paper was published (Dec 2013 - 25 Nov 2014). The methods fitting are reported in another paper: DOI: 10.1056/NEJMoa1411100 #> 67 This data comes from the entire period of the Liberia ebola outbreak up to the point the paper was published (Dec 2013 - 25 Nov 2014). The methods fitting are reported in another paper: DOI: 10.1056/NEJMoa1411100 #> 68 This data comes from the entire period of the Seirra Leone ebola outbreak up to the point the paper was published (Dec 2013 - 25 Nov 2014). The methods fitting are reported in another paper: DOI: 10.1056/NEJMoa1411100 #> 69 This data comes from the entire period of the west africa ebola outbreak up to the point the paper was published (Dec 2013 - 25 Nov 2014). The methods fitting are reported in another paper: DOI: 10.1056/NEJMoa1411100 #> 70 This data comes from the entire period of the Guinea ebola outbreak up to the point the paper was published (Dec 2013 - 25 Nov 2014). The methods fitting are reported in another paper: DOI: 10.1056/NEJMoa1411100 #> 71 This data comes from the entire period of the Liberia ebola outbreak up to the point the paper was published (Dec 2013 - 25 Nov 2014). The methods fitting are reported in another paper: DOI: 10.1056/NEJMoa1411100 #> 72 This data comes from the entire period of the Sierra Leone ebola outbreak up to the point the paper was published (Dec 2013 - 25 Nov 2014). The methods fitting are reported in another paper: DOI: 10.1056/NEJMoa1411100 #> 73 This data comes from the entire period of the west africa ebola outbreak up to the point the paper was published (Dec 2013 - 25 Nov 2014). The methods fitting are reported in another paper: DOI: 10.1056/NEJMoa1411100 #> 74 This data comes from the entire period of the west africa ebola outbreak up to the point the paper was published (Dec 2013 - 25 Nov 2014). The methods fitting are reported in another paper: DOI: 10.1056/NEJMoa1411100 #> 75 This data comes from the entire period of the west africa ebola outbreak up to the point the paper was published (Dec 2013 - 25 Nov 2014). The methods fitting are reported in another paper: DOI: 10.1056/NEJMoa1411100 #> 76 This data comes from the entire period of the west africa ebola outbreak up to the point the paper was published (Dec 2013 - 25 Nov 2014). The methods fitting are reported in another paper: DOI: 10.1056/NEJMoa1411100 #> 77 This data comes from the entire period of the west africa ebola outbreak up to the point the paper was published (Dec 2013 - 25 Nov 2014). The methods fitting are reported in another paper: DOI: 10.1056/NEJMoa1411100 #> 78 This data comes from the entire period of the west africa ebola outbreak up to the point the paper was published (Dec 2013 - 25 Nov 2014). The methods fitting are reported in another paper: DOI: 10.1056/NEJMoa1411100 #> 79 The sample size is not explicitly stated. The number of confirmed cases is 23 and there are 2 suspected cases, therefore it is not clear whether the 2 suspected cases were included in the estimation, the sample size is assumed to be 23. #> 80 The sample size is not explicitly stated. The number of confirmed cases is 23 and there are 2 suspected cases, therefore it is not clear whether the 2 suspected cases were included in the estimation, the sample size is assumed to be 23. #> 81 A distribution was not fitted to the data, instead the median and range observed are reported. The sample size is not explicitly stated. The number of confirmed cases is 23 and there are 2 suspected cases, therefore it is not clear whether the 2 suspected cases were included in the estimation, the sample size is assumed to be 23. #> 82 A distribution was not fitted to the data, instead the median and range observed are reported. The sample size is not explicitly stated. The number of confirmed cases is 23 and there are 2 suspected cases, therefore it is not clear whether the 2 suspected cases were included in the estimation, the sample size is assumed to be 23. #> 83 A distribution was not fitted to the data, instead the median and range observed are reported. The sample size is not explicitly stated. The number of confirmed cases is 23 and there are 2 suspected cases, therefore it is not clear whether the 2 suspected cases were included in the estimation, the sample size is assumed to be 23. #> 84 The distribution parameters were jointly inferred with the risk factors of mortality. #> 85 No additional notes #> 86 No additional notes #> 87 The estimate of the incubation period is from a non-parametric bootstrap approach that does not fit a parametric distribution. #> 88 This estimated mean incubation period is from a meta-analysis of 15 other incubation period estimates. Only the mean is reported and a distribution cannot be specified as the meta-mean is estimated from a random-effects model. #> 89 This estimated mean incubation period is from a meta-analysis of 14 other incubation period estimates. Only the mean is reported and a distribution cannot be specified as the meta-mean is estimated from a random-effects model. #> 90 This estimated mean serial interval is from a meta-analysis of 23 other serial interval estimates. Only the mean is reported and a distribution cannot be specified as the meta-mean is estimated from a random-effects model. #> 91 These estimates are from fitting to the entire dataset of contact pairs, including pairs that are uncertain. #> 92 These estimates are from fitting to a subset of the dataset of contact pairs, only including pairs that are the most certain. #> 93 No additional notes. #> 94 No additional notes. #> 95 This estimated mean incubation period is from a meta-analysis of 99 other incubation period estimates. Only the mean is reported and a distribution cannot be specified as the meta-mean is estimated from a random-effects model. #> 96 No additional notes #> 97 The incubation period parameters are estimated from a meta-analysis of other studies that estimated the incubation period using a lognormal distribution. This is the full set of data (N=9). #> 98 The incubation period parameters are estimated from a meta-analysis of other studies that estimated the incubation period using a lognormal distribution. This is the data set with Backer removed as they did not have a defined exposure window (N=8). #> 99 This dataset excludes Wuhan residents (to have a more precise exposure interval). This method does not apply right-truncation, but does compare the gamma, weibull and lognormal distributions. #> 100 This dataset includes Wuhan residents (which have a less precise exposure interval). This method does not apply right-truncation, but does compare the gamma, weibull and lognormal distributions. #> 101 This method does not apply right-truncation, but does compare the gamma, weibull and lognormal distributions. #> 102 This method does not apply right-truncation, but does compare the gamma, weibull and lognormal distributions. #> 103 This method does not apply right-truncation, but does compare the gamma, weibull and lognormal distributions. #> 104 This method does not apply right-truncation, but does compare the gamma, weibull and lognormal distributions. #> 105 This is excluding Wuhan residents from the dataset as this provides a more precise exposure interval. This method applies right-truncation but only fits a lognormal distribution. #> 106 This dataset includes only surviving patients. This method applies right-truncation but only fits a lognormal distribution. #> 107 This dataset includes only deceased patients. This method applies right-truncation but only fits a lognormal distribution. #> 108 This method applies right-truncation but only fits a lognormal distribution. #> 109 This method applies right-truncation but only fits a lognormal distribution. #> 110 This is the complete data set. #> 111 This is a subset of the data, including only those cases with a known onset of fever to be sure that the onset of symptoms is not from another pathogen. #> 112 This is a subset of the data, including only cases that are detected outside of mainland China. #> 113 This is a subset of the data, including only cases that are detected inside mainland China. #> 114 Data from Kraemer et al 10.1016/S1473-3099(22)00359-0 #> 115 Systematic review #> 116 Systematic review #> 117 Systematic review #> 118 Systematic review #> 119 Systematic review #> 120 SEIR model from 10.1016/j.mbs.2008.06.005 where the IP is assumed to follow a gamma distribution #> 121 SEIR model from 10.1016/j.mbs.2008.06.005 where the IP is assumed to follow a gamma distribution #> 122 SEIR model from 10.1016/j.mbs.2008.06.005 where the IP is assumed to follow a gamma distribution #> 123 Database entry per communication K. Charniga, Z. Cucunubá & Laura Gomez Bermeo. #> 124 No additional notes. #> 125 Case fatality risk is given in deaths per 1,000 cases. It was calculated as a cumulative case fatality ratio. CFR is a population-wide estimate. Odds of chikungunya-related death were not significantly different between males and females. Odds of chikungunya-related death were significantly higher for 55-74 years old, and >75 years old compared to <18."},{"path":"https://epiverse-trace.github.io/epiparameter/reference/as.function.epiparameter.html","id":null,"dir":"Reference","previous_headings":"","what":"as.function() method for <epiparameter> class — as.function.epiparameter","title":"as.function() method for <epiparameter> class — as.function.epiparameter","text":"Converts <epiparameter> object distribution function (see epiparameter_distribution_functions), either probability density/mass function, (density), cumulative distribution function (cdf), random number generator (generate), quantile (quantile).","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/as.function.epiparameter.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"as.function() method for <epiparameter> class — as.function.epiparameter","text":"","code":"# S3 method for class 'epiparameter' as.function(x, func_type = c(\"density\", \"cdf\", \"generate\", \"quantile\"), ...)"},{"path":"https://epiverse-trace.github.io/epiparameter/reference/as.function.epiparameter.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"as.function() method for <epiparameter> class — as.function.epiparameter","text":"x <epiparameter> object. func_type single character string specifying distribution convert <epiparameter> object . Default \"density\". options \"cdf\", \"generate\", \"quantile\". ... dots Extra arguments passed method.","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/as.function.epiparameter.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"as.function() method for <epiparameter> class — as.function.epiparameter","text":"function object.","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/as.function.epiparameter.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"as.function() method for <epiparameter> class — as.function.epiparameter","text":"function returned takes single required argument x.","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/as.function.epiparameter.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"as.function() method for <epiparameter> class — as.function.epiparameter","text":"","code":"ep <- epiparameter_db(single_epiparameter = TRUE) #> Using Linton N, Kobayashi T, Yang Y, Hayashi K, Akhmetzhanov A, Jung S, Yuan #> B, Kinoshita R, Nishiura H (2020). “Incubation Period and Other #> Epidemiological Characteristics of 2019 Novel Coronavirus Infections #> with Right Truncation: A Statistical Analysis of Publicly Available #> Case Data.” _Journal of Clinical Medicine_. doi:10.3390/jcm9020538 #> <https://doi.org/10.3390/jcm9020538>.. #> To retrieve the citation use the 'get_citation' function # by default it will convert to a density function f <- as.function(ep) # use function f(10) #> [1] 0.01732193 f <- as.function(ep, func_type = \"cdf\") f(10) #> [1] 0.7975232"},{"path":"https://epiverse-trace.github.io/epiparameter/reference/as_epiparameter.data.frame.html","id":null,"dir":"Reference","previous_headings":"","what":"Convert <data.frame> to an <epiparameter> object — as_epiparameter.data.frame","title":"Convert <data.frame> to an <epiparameter> object — as_epiparameter.data.frame","text":"Convert tabular information <data.frame> <epiparameter>. information <data.frame> converted <epiparameter> function error.","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/as_epiparameter.data.frame.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Convert <data.frame> to an <epiparameter> object — as_epiparameter.data.frame","text":"","code":"# S3 method for class 'data.frame' as_epiparameter(x, ...)"},{"path":"https://epiverse-trace.github.io/epiparameter/reference/as_epiparameter.data.frame.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Convert <data.frame> to an <epiparameter> object — as_epiparameter.data.frame","text":"x <data.frame>. ... dots used, extra arguments supplied cause warning.","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/as_epiparameter.data.frame.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Convert <data.frame> to an <epiparameter> object — as_epiparameter.data.frame","text":"<epiparameter> object list <epiparameter> objects.","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/as_epiparameter.data.frame.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Convert <data.frame> to an <epiparameter> object — as_epiparameter.data.frame","text":"","code":"ep <- epiparameter_db(single_epiparameter = TRUE) #> Using Linton N, Kobayashi T, Yang Y, Hayashi K, Akhmetzhanov A, Jung S, Yuan #> B, Kinoshita R, Nishiura H (2020). “Incubation Period and Other #> Epidemiological Characteristics of 2019 Novel Coronavirus Infections #> with Right Truncation: A Statistical Analysis of Publicly Available #> Case Data.” _Journal of Clinical Medicine_. doi:10.3390/jcm9020538 #> <https://doi.org/10.3390/jcm9020538>.. #> To retrieve the citation use the 'get_citation' function df <- as.data.frame(ep) ep <- as_epiparameter(df)"},{"path":"https://epiverse-trace.github.io/epiparameter/reference/as_epiparameter.html","id":null,"dir":"Reference","previous_headings":"","what":"Convert to an <epiparameter> object — as_epiparameter","title":"Convert to an <epiparameter> object — as_epiparameter","text":"Convert R object <epiparameter> object. conversion possible function error.","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/as_epiparameter.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Convert to an <epiparameter> object — as_epiparameter","text":"","code":"as_epiparameter(x, ...)"},{"path":"https://epiverse-trace.github.io/epiparameter/reference/as_epiparameter.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Convert to an <epiparameter> object — as_epiparameter","text":"x object used select method. ... dots Extra arguments passed method.","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/as_epiparameter.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Convert to an <epiparameter> object — as_epiparameter","text":"<epiparameter> object list <epiparameter> objects.","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/as_epiparameter.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Convert to an <epiparameter> object — as_epiparameter","text":"create full citation information article table epireview package corresponding entry need passed function via ... argument. argument called article, matched name $. specify probability distribution pass character string function via ... argument. argument called prob_distribution. example, specify gamma distribution: as_epiparameter(x, prob_distribution = \"gamma\"). Warning: distributions specified via prob_dist argument overwrite probability distribution specified x argument. example, probability distribution given epireview entry prob_dist argument specified function may error return unparameterised <epiparameter> parameterisation becomes incompatible. Valid probability distributions : \"gamma\", \"lnorm\", \"weibull\", \"nbinom\", \"geom\", \"pois\", \"norm\", \"exp\".","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/assert_epiparameter.html","id":null,"dir":"Reference","previous_headings":"","what":"Assert an object is a valid <epiparameter> object — assert_epiparameter","title":"Assert an object is a valid <epiparameter> object — assert_epiparameter","text":"Assert object valid <epiparameter> object","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/assert_epiparameter.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Assert an object is a valid <epiparameter> object — assert_epiparameter","text":"","code":"assert_epiparameter(x)"},{"path":"https://epiverse-trace.github.io/epiparameter/reference/assert_epiparameter.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Assert an object is a valid <epiparameter> object — assert_epiparameter","text":"x R object.","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/assert_epiparameter.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Assert an object is a valid <epiparameter> object — assert_epiparameter","text":"Invisibly returns <epiparameter>. Called side-effects (errors invalid <epiparameter> object provided).","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/assert_epiparameter.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Assert an object is a valid <epiparameter> object — assert_epiparameter","text":"","code":"ep <- epiparameter_db(single_epiparameter = TRUE) #> Using Linton N, Kobayashi T, Yang Y, Hayashi K, Akhmetzhanov A, Jung S, Yuan #> B, Kinoshita R, Nishiura H (2020). “Incubation Period and Other #> Epidemiological Characteristics of 2019 Novel Coronavirus Infections #> with Right Truncation: A Statistical Analysis of Publicly Available #> Case Data.” _Journal of Clinical Medicine_. doi:10.3390/jcm9020538 #> <https://doi.org/10.3390/jcm9020538>.. #> To retrieve the citation use the 'get_citation' function assert_epiparameter(ep) # example with invalid <epiparameter> ep$disease <- NULL try(assert_epiparameter(ep)) #> Error : <epiparameter> is invalid due to: #> - <epiparameter> must contain $disease. #> - <epiparameter> must contain one disease. #>"},{"path":"https://epiverse-trace.github.io/epiparameter/reference/c.epiparameter.html","id":null,"dir":"Reference","previous_headings":"","what":"c() method for <epiparameter> class — c.epiparameter","title":"c() method for <epiparameter> class — c.epiparameter","text":"c() method <epiparameter> class","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/c.epiparameter.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"c() method for <epiparameter> class — c.epiparameter","text":"","code":"# S3 method for class 'epiparameter' c(...) # S3 method for class 'multi_epiparameter' c(...)"},{"path":"https://epiverse-trace.github.io/epiparameter/reference/c.epiparameter.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"c() method for <epiparameter> class — c.epiparameter","text":"... dots Objects concatenated.","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/c.epiparameter.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"c() method for <epiparameter> class — c.epiparameter","text":"<epiparameter> list <epiparameter> objects.","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/c.epiparameter.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"c() method for <epiparameter> class — c.epiparameter","text":"","code":"db <- epiparameter_db() #> Returning 125 results that match the criteria (100 are parameterised). #> Use subset to filter by entry variables or single_epiparameter to return a single entry. #> To retrieve the citation for each use the 'get_citation' function # combine two <epiparameter> objects into a list c(db[[1]], db[[2]]) #> # List of 2 <epiparameter> objects #> Number of diseases: 2 #> ❯ Adenovirus ❯ Human Coronavirus #> Number of epi parameters: 1 #> ❯ incubation period #> [[1]] #> Disease: Adenovirus #> Pathogen: Adenovirus #> Epi Parameter: incubation period #> Study: Lessler J, Reich N, Brookmeyer R, Perl T, Nelson K, Cummings D (2009). #> “Incubation periods of acute respiratory viral infections: a systematic #> review.” _The Lancet Infectious Diseases_. #> doi:10.1016/S1473-3099(09)70069-6 #> <https://doi.org/10.1016/S1473-3099%2809%2970069-6>. #> Distribution: lnorm (days) #> Parameters: #> meanlog: 1.723 #> sdlog: 0.231 #> #> [[2]] #> Disease: Human Coronavirus #> Pathogen: Human_Cov #> Epi Parameter: incubation period #> Study: Lessler J, Reich N, Brookmeyer R, Perl T, Nelson K, Cummings D (2009). #> “Incubation periods of acute respiratory viral infections: a systematic #> review.” _The Lancet Infectious Diseases_. #> doi:10.1016/S1473-3099(09)70069-7 #> <https://doi.org/10.1016/S1473-3099%2809%2970069-7>. #> Distribution: lnorm (days) #> Parameters: #> meanlog: 1.163 #> sdlog: 0.140 #> #> # ℹ Use `parameter_tbl()` to see a summary table of the parameters. #> # ℹ Explore database online at: https://epiverse-trace.github.io/epiparameter/articles/database.html # combine a list of <epiparameter> objects and a single <epiparameter> object c(db, db[[1]]) #> # List of 126 <epiparameter> objects #> Number of diseases: 23 #> ❯ Adenovirus ❯ COVID-19 ❯ Chikungunya ❯ Dengue ❯ Ebola Virus Disease ❯ Hantavirus Pulmonary Syndrome ❯ Human Coronavirus ❯ Influenza ❯ Japanese Encephalitis ❯ MERS ❯ Marburg Virus Disease ❯ Measles ❯ Mpox ❯ Parainfluenza ❯ Pneumonic Plague ❯ RSV ❯ Rhinovirus ❯ Rift Valley Fever ❯ SARS ❯ Smallpox ❯ West Nile Fever ❯ Yellow Fever ❯ Zika Virus Disease #> Number of epi parameters: 13 #> ❯ case fatality risk ❯ generation time ❯ hospitalisation to death ❯ hospitalisation to discharge ❯ incubation period ❯ notification to death ❯ notification to discharge ❯ offspring distribution ❯ onset to death ❯ onset to discharge ❯ onset to hospitalisation ❯ onset to ventilation ❯ serial interval #> [[1]] #> Disease: Adenovirus #> Pathogen: Adenovirus #> Epi Parameter: incubation period #> Study: Lessler J, Reich N, Brookmeyer R, Perl T, Nelson K, Cummings D (2009). #> “Incubation periods of acute respiratory viral infections: a systematic #> review.” _The Lancet Infectious Diseases_. #> doi:10.1016/S1473-3099(09)70069-6 #> <https://doi.org/10.1016/S1473-3099%2809%2970069-6>. #> Distribution: lnorm (days) #> Parameters: #> meanlog: 1.723 #> sdlog: 0.231 #> #> [[2]] #> Disease: Human Coronavirus #> Pathogen: Human_Cov #> Epi Parameter: incubation period #> Study: Lessler J, Reich N, Brookmeyer R, Perl T, Nelson K, Cummings D (2009). #> “Incubation periods of acute respiratory viral infections: a systematic #> review.” _The Lancet Infectious Diseases_. #> doi:10.1016/S1473-3099(09)70069-7 #> <https://doi.org/10.1016/S1473-3099%2809%2970069-7>. #> Distribution: lnorm (days) #> Parameters: #> meanlog: 1.163 #> sdlog: 0.140 #> #> [[3]] #> Disease: SARS #> Pathogen: SARS-Cov-1 #> Epi Parameter: incubation period #> Study: Lessler J, Reich N, Brookmeyer R, Perl T, Nelson K, Cummings D (2009). #> “Incubation periods of acute respiratory viral infections: a systematic #> review.” _The Lancet Infectious Diseases_. #> doi:10.1016/S1473-3099(09)70069-8 #> <https://doi.org/10.1016/S1473-3099%2809%2970069-8>. #> Distribution: lnorm (days) #> Parameters: #> meanlog: 1.386 #> sdlog: 0.593 #> #> # ℹ 123 more elements #> # ℹ Use `print(n = ...)` to see more elements. #> # ℹ Use `parameter_tbl()` to see a summary table of the parameters. #> # ℹ Explore database online at: https://epiverse-trace.github.io/epiparameter/articles/database.html"},{"path":"https://epiverse-trace.github.io/epiparameter/reference/calc_disc_dist_quantile.html","id":null,"dir":"Reference","previous_headings":"","what":"Calculate the quantiles of a probability distribution based on the vector of probabilities and time data (e.g. time since infection) — calc_disc_dist_quantile","title":"Calculate the quantiles of a probability distribution based on the vector of probabilities and time data (e.g. time since infection) — calc_disc_dist_quantile","text":"function can used cases data fitted distribution openly available summary statistics distribution reported data scraped plot quantiles needed order use extract_param() function.","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/calc_disc_dist_quantile.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculate the quantiles of a probability distribution based on the vector of probabilities and time data (e.g. time since infection) — calc_disc_dist_quantile","text":"","code":"calc_disc_dist_quantile(prob, days, quantile)"},{"path":"https://epiverse-trace.github.io/epiparameter/reference/calc_disc_dist_quantile.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Calculate the quantiles of a probability distribution based on the vector of probabilities and time data (e.g. time since infection) — calc_disc_dist_quantile","text":"prob numeric vector probabilities. days numeric vector days. quantile single numeric vector numerics specifying quantiles extract distribution.","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/calc_disc_dist_quantile.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Calculate the quantiles of a probability distribution based on the vector of probabilities and time data (e.g. time since infection) — calc_disc_dist_quantile","text":"named vector quantiles.","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/calc_disc_dist_quantile.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Calculate the quantiles of a probability distribution based on the vector of probabilities and time data (e.g. time since infection) — calc_disc_dist_quantile","text":"","code":"prob <- dgamma(seq(0, 10, length.out = 21), shape = 2, scale = 2) days <- seq(0, 10, 0.5) quantiles <- c(0.025, 0.975) calc_disc_dist_quantile(prob = prob, days = days, quantile = quantiles) #> 0.025 0.975 #> 0 9"},{"path":"https://epiverse-trace.github.io/epiparameter/reference/convert_params_to_summary_stats.html","id":null,"dir":"Reference","previous_headings":"","what":"Convert the parameter(s) of a distribution to summary statistics — convert_params_to_summary_stats","title":"Convert the parameter(s) of a distribution to summary statistics — convert_params_to_summary_stats","text":"Convert parameters range distributions number summary statistics. summary statistics calculated analytically given parameters.","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/convert_params_to_summary_stats.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Convert the parameter(s) of a distribution to summary statistics — convert_params_to_summary_stats","text":"","code":"convert_params_to_summary_stats(x, ...) # S3 method for class 'character' convert_params_to_summary_stats( x = c(\"lnorm\", \"gamma\", \"weibull\", \"nbinom\", \"geom\"), ... ) # S3 method for class 'epiparameter' convert_params_to_summary_stats(x, ...)"},{"path":"https://epiverse-trace.github.io/epiparameter/reference/convert_params_to_summary_stats.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Convert the parameter(s) of a distribution to summary statistics — convert_params_to_summary_stats","text":"x R object. ... <dynamic-dots> Numeric named parameter(s) used convert summary statistics. example meanlog sdlog parameters lognormal (lnorm) distribution.","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/convert_params_to_summary_stats.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Convert the parameter(s) of a distribution to summary statistics — convert_params_to_summary_stats","text":"list eight elements including: mean, median, mode, variance (var), standard deviation (sd), coefficient variation (cv), skewness, excess kurtosis (ex_kurtosis).","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/convert_params_to_summary_stats.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Convert the parameter(s) of a distribution to summary statistics — convert_params_to_summary_stats","text":"distribution names parameter names follow style distributions R, example lognormal distribution lnorm, parameters meanlog sdlog.","code":""},{"path":[]},{"path":"https://epiverse-trace.github.io/epiparameter/reference/convert_params_to_summary_stats.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Convert the parameter(s) of a distribution to summary statistics — convert_params_to_summary_stats","text":"","code":"# example using characters convert_params_to_summary_stats(\"lnorm\", meanlog = 1, sdlog = 2) #> $mean #> [1] 20.08554 #> #> $median #> [1] 2.718282 #> #> $mode #> [1] 0.04978707 #> #> $var #> [1] 21623.04 #> #> $sd #> [1] 147.0477 #> #> $cv #> [1] 7.321076 #> #> $skewness #> [1] 414.3593 #> #> $ex_kurtosis #> [1] 9220557 #> convert_params_to_summary_stats(\"gamma\", shape = 1, scale = 1) #> $mean #> [1] 1 #> #> $median #> [1] 0.6931472 #> #> $mode #> [1] 0 #> #> $var #> [1] 1 #> #> $sd #> [1] 1 #> #> $cv #> [1] 1 #> #> $skewness #> [1] 2 #> #> $ex_kurtosis #> [1] 6 #> convert_params_to_summary_stats(\"nbinom\", prob = 0.5, dispersion = 2) #> $mean #> [1] 2 #> #> $median #> [1] 1 #> #> $mode #> [1] 1 #> #> $var #> [1] 4 #> #> $sd #> [1] 2 #> #> $cv #> [1] 1 #> #> $skewness #> [1] 1.5 #> #> $ex_kurtosis #> [1] 4 #> # example using <epiparameter> epiparameter <- epiparameter_db(single_epiparameter = TRUE) #> Using Linton N, Kobayashi T, Yang Y, Hayashi K, Akhmetzhanov A, Jung S, Yuan #> B, Kinoshita R, Nishiura H (2020). “Incubation Period and Other #> Epidemiological Characteristics of 2019 Novel Coronavirus Infections #> with Right Truncation: A Statistical Analysis of Publicly Available #> Case Data.” _Journal of Clinical Medicine_. doi:10.3390/jcm9020538 #> <https://doi.org/10.3390/jcm9020538>.. #> To retrieve the citation use the 'get_citation' function convert_params_to_summary_stats(epiparameter) #> $mean #> [1] 9.7 #> #> $median #> [1] 2.576957 #> #> $mode #> [1] 0.1818772 #> #> $var #> [1] 1239.04 #> #> $sd #> [1] 35.2 #> #> $cv #> [1] 3.628866 #> #> $skewness #> [1] 58.67393 #> #> $ex_kurtosis #> [1] 46586.04 #> # example using <epiparameter> and specifying parameters epiparameter <- epiparameter_db( disease = \"Influenza\", author = \"Virlogeux\", subset = prob_dist == \"weibull\" ) #> Returning 4 results that match the criteria (3 are parameterised). #> Use subset to filter by entry variables or single_epiparameter to return a single entry. #> To retrieve the citation for each use the 'get_citation' function convert_params_to_summary_stats(epiparameter[[2]], shape = 1, scale = 1) #> $mean #> [1] 1 #> #> $median #> [1] 0.6931472 #> #> $mode #> [1] 0 #> #> $var #> [1] 1 #> #> $sd #> [1] 1 #> #> $cv #> [1] 1 #> #> $skewness #> [1] 2 #> #> $ex_kurtosis #> [1] 6 #>"},{"path":"https://epiverse-trace.github.io/epiparameter/reference/convert_summary_stats_to_params.html","id":null,"dir":"Reference","previous_headings":"","what":"Convert the summary statistics of a distribution to parameters — convert_summary_stats_to_params","title":"Convert the summary statistics of a distribution to parameters — convert_summary_stats_to_params","text":"Convert summary statistics range distributions distribution's parameters. summary statistics calculated analytically given parameters. exception Weibull distribution uses root finding numerical method.","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/convert_summary_stats_to_params.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Convert the summary statistics of a distribution to parameters — convert_summary_stats_to_params","text":"","code":"convert_summary_stats_to_params(x, ...) # S3 method for class 'character' convert_summary_stats_to_params( x = c(\"lnorm\", \"gamma\", \"weibull\", \"nbinom\", \"geom\"), ... ) # S3 method for class 'epiparameter' convert_summary_stats_to_params(x, ...)"},{"path":"https://epiverse-trace.github.io/epiparameter/reference/convert_summary_stats_to_params.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Convert the summary statistics of a distribution to parameters — convert_summary_stats_to_params","text":"x R object. ... <dynamic-dots> Numeric named summary statistics used convert parameter(s). example mean sd summary statistics lognormal (lnorm) distribution.","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/convert_summary_stats_to_params.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Convert the summary statistics of a distribution to parameters — convert_summary_stats_to_params","text":"list either one two elements (depending many parameters distribution ).","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/convert_summary_stats_to_params.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Convert the summary statistics of a distribution to parameters — convert_summary_stats_to_params","text":"Summary statistics named accordingly (case-sensitive): mean: mean median: median mode: mode variance: var standard deviation: sd coefficient variation: cv skewness: skewness excess kurtosis: ex_kurtosis Note: combinations summary statistics can converted distribution parameters. case function error stating parameters calculated given input. distribution names parameter names follow style distributions R, example lognormal distribution lnorm, parameters meanlog sdlog.","code":""},{"path":[]},{"path":"https://epiverse-trace.github.io/epiparameter/reference/convert_summary_stats_to_params.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Convert the summary statistics of a distribution to parameters — convert_summary_stats_to_params","text":"","code":"# examples using characters convert_summary_stats_to_params(\"lnorm\", mean = 1, sd = 1) #> $meanlog #> [1] -0.3465736 #> #> $sdlog #> [1] 0.8325546 #> convert_summary_stats_to_params(\"weibull\", mean = 2, var = 2) #> Numerical approximation used, results may be unreliable. #> $shape #> [1] 1.435521 #> #> $scale #> [1] 2.202641 #> convert_summary_stats_to_params(\"geom\", mean = 2) #> $prob #> [1] 0.3333333 #> # examples using <epiparameter> epiparameter <- epiparameter_db(single_epiparameter = TRUE) #> Using Linton N, Kobayashi T, Yang Y, Hayashi K, Akhmetzhanov A, Jung S, Yuan #> B, Kinoshita R, Nishiura H (2020). “Incubation Period and Other #> Epidemiological Characteristics of 2019 Novel Coronavirus Infections #> with Right Truncation: A Statistical Analysis of Publicly Available #> Case Data.” _Journal of Clinical Medicine_. doi:10.3390/jcm9020538 #> <https://doi.org/10.3390/jcm9020538>.. #> To retrieve the citation use the 'get_citation' function convert_summary_stats_to_params(epiparameter) #> $meanlog #> [1] 0.9466094 #> #> $sdlog #> [1] 1.628199 #> # example using <epiparameter> and specifying summary stats epiparameter$summary_stats <- list() convert_summary_stats_to_params(epiparameter, mean = 10, sd = 2) #> $meanlog #> [1] 2.282975 #> #> $sdlog #> [1] 0.1980422 #>"},{"path":"https://epiverse-trace.github.io/epiparameter/reference/create_citation.html","id":null,"dir":"Reference","previous_headings":"","what":"Create a citation for an <epiparameter> object — create_citation","title":"Create a citation for an <epiparameter> object — create_citation","text":"helper function creating <epiparameter> object create citation list sensible defaults, type checking arguments help remember citation information accepted list.","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/create_citation.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Create a citation for an <epiparameter> object — create_citation","text":"","code":"create_citation( author = utils::person(), year = NA_integer_, title = NA_character_, journal = NA_character_, doi = NA_character_, pmid = NA_integer_ )"},{"path":"https://epiverse-trace.github.io/epiparameter/reference/create_citation.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Create a citation for an <epiparameter> object — create_citation","text":"author Either <person>, character string, vector list characters case multiple authors. Specify full name (\"<given name>\" \"<family name>\"). using characters make sure name can converted <person> (see .person()). Use white space separation names. Multiple names can stored within single <person> (see person()). year numeric year publication. title character string title article published epidemiological parameters. journal character string name journal published article published epidemiological parameters. can also pre-print server, e.g., medRxiv. doi character string Digital Object Identifier (DOI) assigned papers unique paper. pmid character string PubMed unique identifier number (PMID) assigned papers give unique identifier within PubMed.","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/create_citation.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Create a citation for an <epiparameter> object — create_citation","text":"<bibentry> object citation","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/create_citation.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Create a citation for an <epiparameter> object — create_citation","text":"function acts wrapper around bibentry() create citations sources reporting epidemiological parameters.","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/create_citation.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Create a citation for an <epiparameter> object — create_citation","text":"","code":"create_citation( author = person(given = \"John\", family = \"Smith\"), year = 2002, title = \"COVID-19 incubation period\", journal = \"Epi Journal\", doi = \"10.19832/j.1366-9516.2012.09147.x\" ) #> Using Smith J (2002). “COVID-19 incubation period.” _Epi Journal_. #> doi:10.19832/j.1366-9516.2012.09147.x #> <https://doi.org/10.19832/j.1366-9516.2012.09147.x>. #> To retrieve the citation use the 'get_citation' function #> Smith J (2002). “COVID-19 incubation period.” _Epi Journal_. #> doi:10.19832/j.1366-9516.2012.09147.x #> <https://doi.org/10.19832/j.1366-9516.2012.09147.x>."},{"path":"https://epiverse-trace.github.io/epiparameter/reference/create_metadata.html","id":null,"dir":"Reference","previous_headings":"","what":"Specify metadata associated with data set — create_metadata","title":"Specify metadata associated with data set — create_metadata","text":"helper function creating <epiparameter> object create metadata list sensible defaults, type checking arguments help remember metadata list structure (element names).","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/create_metadata.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Specify metadata associated with data set — create_metadata","text":"","code":"create_metadata( units = NA_character_, sample_size = NA_integer_, region = NA_character_, transmission_mode = NA_character_, vector = NA_character_, extrinsic = FALSE, inference_method = NA_character_ )"},{"path":"https://epiverse-trace.github.io/epiparameter/reference/create_metadata.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Specify metadata associated with data set — create_metadata","text":"units character units epidemiological parameter. sample_size sample data used fit delay distribution. usually number people data primary possibly secondary event interest. cases sample size stated NA can used. region geographical location data collected. can either given sub-national, national, continental. Multiple nested regions can given comma separated. region specified NA can given. transmission_mode character string specifying pathogen transmitted. information used determine whether epidemiological parameters vector-borne disease (.e. transmitted humans intermediate vector), specified transmission_mode = \"vector_borne\". vector name vector transmitting vector-borne disease. can common name, latin binomial name specific vector species. common name taxonomic name can given one given parentheses. disease vector-borne NA given. extrinsic boolean value defining whether data entry extrinsic delay distribution, extrinsic incubation period. field required intrinsic extrinsic delay distributions stored separate entries database can linked. disease vector-borne FALSE given. See Details explanation extrinsic distribution. inference_method type inference used fit delay distribution data. Abbreviations model fitting techniques can specified long non-ambiguous. field used determine whether uncertainty intervals possibly specified fields : confidence intervals (case maximum likelihood), credible intervals (case bayesian inference). Uncertainty bounds another types inference methods, inference method unstated assumed confidence intervals. inference method unknown disease probability distribution NA can given.","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/create_metadata.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Specify metadata associated with data set — create_metadata","text":"named list containing information sample size study, geography, whether disease vector-borne whether intrinsic extrinsic distribution well method distribution parameter estimation.","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/create_metadata.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Specify metadata associated with data set — create_metadata","text":"vector-borne diseases transmissibility disease dependent time taken host (.e. human) become infectious, also time takes vector become infectious. Therefore, extrinsic delay, vector infected yet infectious can role spread disease.","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/create_metadata.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Specify metadata associated with data set — create_metadata","text":"","code":"# it will automatically populate the fields with defaults if left empty create_metadata() #> $units #> [1] NA #> #> $sample_size #> [1] NA #> #> $region #> [1] NA #> #> $transmission_mode #> [1] NA #> #> $vector #> [1] NA #> #> $extrinsic #> [1] FALSE #> #> $inference_method #> [1] NA #> # supplying each field create_metadata( units = \"days\", sample_size = 10, region = \"UK\", transmission_mode = \"vector_borne\", vector = \"mosquito\", extrinsic = FALSE, inference_method = \"MLE\" ) #> $units #> [1] \"days\" #> #> $sample_size #> [1] 10 #> #> $region #> [1] \"UK\" #> #> $transmission_mode #> [1] \"vector_borne\" #> #> $vector #> [1] \"mosquito\" #> #> $extrinsic #> [1] FALSE #> #> $inference_method #> [1] \"MLE\" #>"},{"path":"https://epiverse-trace.github.io/epiparameter/reference/create_method_assess.html","id":null,"dir":"Reference","previous_headings":"","what":"Specify methodological aspects of distribution fitting — create_method_assess","title":"Specify methodological aspects of distribution fitting — create_method_assess","text":"helper function creating <epiparameter> object create method assessment list sensible defaults, type checking arguments help remember method assessments can accepted list.","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/create_method_assess.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Specify methodological aspects of distribution fitting — create_method_assess","text":"","code":"create_method_assess( censored = NA, right_truncated = NA, phase_bias_adjusted = NA )"},{"path":"https://epiverse-trace.github.io/epiparameter/reference/create_method_assess.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Specify methodological aspects of distribution fitting — create_method_assess","text":"censored boolean logical whether study used single double interval censoring methods infer delay distribution right_truncated boolean logical whether study used right- truncation methods infer delay distribution phase_bias_adjusted boolean logical whether study adjusted phase bias methods infer delay distribution","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/create_method_assess.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Specify methodological aspects of distribution fitting — create_method_assess","text":"named list three elements","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/create_method_assess.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Specify methodological aspects of distribution fitting — create_method_assess","text":"Currently, method assessment focuses common methodological aspects delay distributions (e.g. incubation period, serial interval, etc.), currently take account methodological aspects may important fitting offspring distributions data disease (super)spreading.","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/create_method_assess.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Specify methodological aspects of distribution fitting — create_method_assess","text":"","code":"create_method_assess( censored = FALSE, right_truncated = FALSE, phase_bias_adjusted = FALSE ) #> $censored #> [1] FALSE #> #> $right_truncated #> [1] FALSE #> #> $phase_bias_adjusted #> [1] FALSE #>"},{"path":"https://epiverse-trace.github.io/epiparameter/reference/create_prob_distribution.html","id":null,"dir":"Reference","previous_headings":"","what":"Create a distribution object — create_prob_distribution","title":"Create a distribution object — create_prob_distribution","text":"Creates S3 class holding distribution parameters probability distribution name, parameters distribution truncation discretisation. class holding distribution depends whether discretised distribution. continuous discrete distributions S3 classes distributional package used, discretised continuous distributions S3 class distcrete package used. details properties distribution classes respective package see documentation (either ?distributional ?distcrete)","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/create_prob_distribution.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Create a distribution object — create_prob_distribution","text":"","code":"create_prob_distribution( prob_distribution, prob_distribution_params, discretise = FALSE, truncation = NA, ... )"},{"path":"https://epiverse-trace.github.io/epiparameter/reference/create_prob_distribution.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Create a distribution object — create_prob_distribution","text":"prob_distribution character string specifying probability distribution. match R naming convention probability distributions (e.g. lognormal lnorm, negative binomial nbinom, geometric geom). prob_distribution_params named vector probability distribution parameters. discretise boolean logical whether distribution discretised. Default FALSE assumes continuous probability distribution. truncation numeric specifying truncation point inferred distribution truncated, NA unknown. ... dots Extra arguments passed distributional distcrete functions construct S3 distribution objects. see arguments can adjusted discretised distributions see distcrete::distcrete(), distributions see ?distributional documentation find specific distribution constructor function, e.g. Gamma distribution see distributional::dist_gamma().","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/create_prob_distribution.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Create a distribution object — create_prob_distribution","text":"S3 class containing probability distribution character string parameters probability distribution unknown.","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/create_prob_distribution.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Create a distribution object — create_prob_distribution","text":"Truncation enabled continuous distributions truncation implemented distcrete. default discretisation continuous distributions uses discretisation interval (interval) 1. unit distribution days, discretised day. endpoint weighting (w) discretisation 1. w can [0,1]. information please see distcrete::distcrete().","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/create_prob_distribution.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Create a distribution object — create_prob_distribution","text":"","code":"# example with continuous distribution without truncation create_prob_distribution( prob_distribution = \"gamma\", prob_distribution_params = c(shape = 1, scale = 1), discretise = FALSE, truncation = NA ) #> <distribution[1]> #> [1] Γ(1, 1) # example with continuous distribution with truncation create_prob_distribution( prob_distribution = \"gamma\", prob_distribution_params = c(shape = 1, scale = 1), discretise = FALSE, truncation = 10 ) #> <distribution[1]> #> [1] Γ(1, 1)[-Inf,10] # example with discrete distribution create_prob_distribution( prob_distribution = \"gamma\", prob_distribution_params = c(shape = 1, scale = 1), discretise = TRUE, truncation = NA ) #> A discrete distribution #> name: gamma #> parameters: #> shape: 1 #> scale: 1 # example passing extra arguments to distcrete create_prob_distribution( prob_distribution = \"gamma\", prob_distribution_params = c(shape = 1, scale = 1), discretise = TRUE, truncation = NA, w = 0.5 ) #> A discrete distribution #> name: gamma #> parameters: #> shape: 1 #> scale: 1"},{"path":"https://epiverse-trace.github.io/epiparameter/reference/create_region.html","id":null,"dir":"Reference","previous_headings":"","what":"Specify the geography of the data entry — create_region","title":"Specify the geography of the data entry — create_region","text":"geography data set can single geographical region either continent, country, region city level. specifying level geography fields may deduced.","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/create_region.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Specify the geography of the data entry — create_region","text":"","code":"create_region( continent = NA_character_, country = NA_character_, region = NA_character_, city = NA_character_ )"},{"path":"https://epiverse-trace.github.io/epiparameter/reference/create_region.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Specify the geography of the data entry — create_region","text":"continent character string specifying continent. country character string specifying country. region character string specifying region. city character string specifying city.","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/create_region.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Specify the geography of the data entry — create_region","text":"named list.","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/create_region.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Specify the geography of the data entry — create_region","text":"","code":"create_region(country = \"UK\") #> $continent #> [1] NA #> #> $country #> [1] \"UK\" #> #> $region #> [1] NA #> #> $city #> [1] NA #>"},{"path":"https://epiverse-trace.github.io/epiparameter/reference/create_summary_stats.html","id":null,"dir":"Reference","previous_headings":"","what":"Specify reported summary statistics — create_summary_stats","title":"Specify reported summary statistics — create_summary_stats","text":"helper function creating <epiparameter> object create summary statistics list sensible defaults, type checking arguments help remember summary statistics can accepted list.","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/create_summary_stats.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Specify reported summary statistics — create_summary_stats","text":"","code":"create_summary_stats( mean = NA_real_, mean_ci_limits = c(NA_real_, NA_real_), mean_ci = NA_real_, sd = NA_real_, sd_ci_limits = c(NA_real_, NA_real_), sd_ci = NA_real_, median = NA_real_, median_ci_limits = c(NA_real_, NA_real_), median_ci = NA_real_, dispersion = NA_real_, dispersion_ci_limits = c(NA_real_, NA_real_), dispersion_ci = NA_real_, lower_range = NA_real_, upper_range = NA_real_, quantiles = NA_real_ )"},{"path":"https://epiverse-trace.github.io/epiparameter/reference/create_summary_stats.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Specify reported summary statistics — create_summary_stats","text":"mean numeric mean (expectation) probability distribution. mean_ci_limits numeric vector length two confidence interval around mean. mean_ci numeric specifying confidence interval width, e.g. 95 95% CI sd numeric standard deviation probability distribution. sd_ci_limits numeric vector length 2 confidence interval around standard deviation. sd_ci numeric specifying confidence interval width, e.g. 95 95% confidence interval. median numeric median probability distribution. median_ci_limits numeric vector length two confidence interval around median. median_ci numeric specifying confidence interval width median. dispersion numeric dispersion probability distribution. Important dispersion probability distributions usually parameterised dispersion parameter, example lognormal distribution. probability distribution usually parameterised dispersion parameter, e.g. negative binomial distribution, considered parameter summary statistic go prob_distribution argument constructing <epiparameter> object epiparameter() (see create_prob_distribution()). dispersion_ci_limits numeric vector length 2 confidence interval around dispersion. dispersion_ci numeric specifying confidence interval width, e.g. 95 95% confidence interval. lower_range lower range data, used infer parameters distribution provided. upper_range upper range data, used infer parameters distribution provided. quantiles numeric vector quantiles distribution. quantiles provided default empty vector 2.5th, 5th, 25th, 75th, 95th, 97.5th quantiles supplied.","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/create_summary_stats.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Specify reported summary statistics — create_summary_stats","text":"list summary statistics. output list element names equal function arguments:","code":"$mean $mean_ci_limits $mean_ci $sd $sd_ci_limits $sd_ci $median $median_ci_limits $median_ci $dispersion $dispersion_ci_limits $dispersion_ci $lower_range $upper_range $quantiles"},{"path":"https://epiverse-trace.github.io/epiparameter/reference/create_summary_stats.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Specify reported summary statistics — create_summary_stats","text":"","code":"# mean and standard deviation create_summary_stats(mean = 5, sd = 2) #> $mean #> [1] 5 #> #> $mean_ci_limits #> [1] NA NA #> #> $mean_ci #> [1] NA #> #> $sd #> [1] 2 #> #> $sd_ci_limits #> [1] NA NA #> #> $sd_ci #> [1] NA #> #> $median #> [1] NA #> #> $median_ci_limits #> [1] NA NA #> #> $median_ci #> [1] NA #> #> $dispersion #> [1] NA #> #> $dispersion_ci_limits #> [1] NA NA #> #> $dispersion_ci #> [1] NA #> #> $quantiles #> [1] NA #> #> $range #> [1] NA NA #> # mean and standard deviation with uncertainty create_summary_stats( mean = 4, mean_ci_limits = c(2.1, 5.7), mean_ci = 95, sd = 0.7, sd_ci_limits = c(0.3, 1.1), sd_ci = 95 ) #> $mean #> [1] 4 #> #> $mean_ci_limits #> [1] 2.1 5.7 #> #> $mean_ci #> [1] 95 #> #> $sd #> [1] 0.7 #> #> $sd_ci_limits #> [1] 0.3 1.1 #> #> $sd_ci #> [1] 95 #> #> $median #> [1] NA #> #> $median_ci_limits #> [1] NA NA #> #> $median_ci #> [1] NA #> #> $dispersion #> [1] NA #> #> $dispersion_ci_limits #> [1] NA NA #> #> $dispersion_ci #> [1] NA #> #> $quantiles #> [1] NA #> #> $range #> [1] NA NA #> # median and range create_summary_stats( median = 5, lower_range = 1, upper_range = 13 ) #> $mean #> [1] NA #> #> $mean_ci_limits #> [1] NA NA #> #> $mean_ci #> [1] NA #> #> $sd #> [1] NA #> #> $sd_ci_limits #> [1] NA NA #> #> $sd_ci #> [1] NA #> #> $median #> [1] 5 #> #> $median_ci_limits #> [1] NA NA #> #> $median_ci #> [1] NA #> #> $dispersion #> [1] NA #> #> $dispersion_ci_limits #> [1] NA NA #> #> $dispersion_ci #> [1] NA #> #> $quantiles #> [1] NA #> #> $range #> [1] 1 13 #>"},{"path":"https://epiverse-trace.github.io/epiparameter/reference/create_uncertainty.html","id":null,"dir":"Reference","previous_headings":"","what":"Specify distribution parameter uncertainty — create_uncertainty","title":"Specify distribution parameter uncertainty — create_uncertainty","text":"helper function creating uncertainty parameters distribution <epiparameter> object.","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/create_uncertainty.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Specify distribution parameter uncertainty — create_uncertainty","text":"","code":"create_uncertainty(ci_limits = NA_real_, ci, ci_type)"},{"path":"https://epiverse-trace.github.io/epiparameter/reference/create_uncertainty.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Specify distribution parameter uncertainty — create_uncertainty","text":"ci_limits numeric vector length two lower upper bound confidence interval credible interval. ci numeric specifying interval ci, e.g. 95 95% ci. ci_type character string, either \"confidence interval\" \"credible interval\".","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/create_uncertainty.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Specify distribution parameter uncertainty — create_uncertainty","text":"List three elements: $ci_limits upper lower bounds CI (either confidence interval credible interval) (.e. two element numeric vector). $ci interval (e.g. 95 95% CI) given single numeric. $ci_type character string specifying type uncertainty (can either \"confidence interval\" \"credible interval\").","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/create_uncertainty.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Specify distribution parameter uncertainty — create_uncertainty","text":"","code":"# example with uncertainty for a single parameter create_uncertainty( ci_limits = c(1, 3), ci = 95, ci_type = \"confidence interval\" ) #> $ci_limits #> [1] 1 3 #> #> $ci #> [1] 95 #> #> $ci_type #> [1] \"confidence interval\" #> # example for multiple parameters # lengh of list should match number of parameters list( shape = create_uncertainty( ci_limits = c(1, 3), ci = 95, ci_type = \"confidence interval\" ), scale = create_uncertainty( ci_limits = c(2, 4), ci = 95, ci_type = \"confidence interval\" ) ) #> $shape #> $shape$ci_limits #> [1] 1 3 #> #> $shape$ci #> [1] 95 #> #> $shape$ci_type #> [1] \"confidence interval\" #> #> #> $scale #> $scale$ci_limits #> [1] 2 4 #> #> $scale$ci #> [1] 95 #> #> $scale$ci_type #> [1] \"confidence interval\" #> #> # example with unknown uncertainty # the function can be called without arguments create_uncertainty() #> $ci_limits #> [1] NA #> #> $ci #> [1] NA NA #> #> $ci_type #> [1] NA #> # or give NA as the first argument create_uncertainty(NA) #> $ci_limits #> [1] NA #> #> $ci #> [1] NA NA #> #> $ci_type #> [1] NA #>"},{"path":"https://epiverse-trace.github.io/epiparameter/reference/discretise.html","id":null,"dir":"Reference","previous_headings":"","what":"Discretises a continuous distribution in an <epiparameter> object — discretise","title":"Discretises a continuous distribution in an <epiparameter> object — discretise","text":"Discretises continuous distribution <epiparameter> object","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/discretise.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Discretises a continuous distribution in an <epiparameter> object — discretise","text":"","code":"discretise(x, ...) discretize(x, ...) # S3 method for class 'epiparameter' discretise(x, ...) # Default S3 method discretise(x, ...)"},{"path":"https://epiverse-trace.github.io/epiparameter/reference/discretise.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Discretises a continuous distribution in an <epiparameter> object — discretise","text":"x <epiparameter> object. ... dots Extra arguments passed method.","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/discretise.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Discretises a continuous distribution in an <epiparameter> object — discretise","text":"<epiparameter> object.","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/discretise.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Discretises a continuous distribution in an <epiparameter> object — discretise","text":"Converts S3 distribution object <epiparameter> continuous (using object {distributional} package) discretised distribution (using object {distcrete} package).","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/discretise.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Discretises a continuous distribution in an <epiparameter> object — discretise","text":"","code":"ebola_incubation <- epiparameter( disease = \"ebola\", epi_name = \"incubation_period\", prob_distribution = create_prob_distribution( prob_distribution = \"gamma\", prob_distribution_params = c(shape = 1, scale = 1) ) ) #> Citation cannot be created as author, year, journal or title is missing discretise(ebola_incubation) #> Disease: ebola #> Pathogen: NA #> Epi Parameter: incubation period #> Study: (????). “No citation.” #> Distribution: discrete gamma (NA) #> Parameters: #> shape: 1.000 #> scale: 1.000"},{"path":"https://epiverse-trace.github.io/epiparameter/reference/dot-calc_dist_params.html","id":null,"dir":"Reference","previous_headings":"","what":"Calculate the parameters of a probability distribution from a list of summary statistics — .calc_dist_params","title":"Calculate the parameters of a probability distribution from a list of summary statistics — .calc_dist_params","text":"parameters probability distribution provided (e.g. describing distribution literature) instead summary statistics distribution provided, parameters can usually calculated summary statistics. function can provide convenient wrapper around convert_summary_stats_to_params() extract_param() known summary statistics can used calculate parameters distribution.","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/dot-calc_dist_params.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculate the parameters of a probability distribution from a list of summary statistics — .calc_dist_params","text":"","code":".calc_dist_params(prob_distribution, summary_stats, sample_size)"},{"path":"https://epiverse-trace.github.io/epiparameter/reference/dot-calc_dist_params.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Calculate the parameters of a probability distribution from a list of summary statistics — .calc_dist_params","text":"prob_distribution S3 class containing probability distribution character string parameters probability distribution unknown name distribution known, NA distribution name parameters unknown. Use create_prob_distribution() create prob_distribution. summary_stats list summary statistics, use create_summary_stats() create list. list can include summary statistics inferred distribution mean standard deviation, quantiles distribution, information data used fit distribution lower upper range. summary statistics can also include uncertainty around metrics confidence interval around mean standard deviation. sample_size sample size data. needed falling back using median-range extraction calculation.","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/dot-calc_dist_params.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Calculate the parameters of a probability distribution from a list of summary statistics — .calc_dist_params","text":"named numeric vector parameters.","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/dot-calc_dist_params.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Calculate the parameters of a probability distribution from a list of summary statistics — .calc_dist_params","text":"hierarchy methods : Conversion prioritised mean standard deviation available mostly analytical conversions (except one Weibull conversions). Next method possible extraction percentiles. method requires lower percentile ((0-50]) upper percentile ((50-100)). multiple percentiles ranges provided lowest value used calculation. last method extraction using median range data.","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/dot-check_optim_conv.html","id":null,"dir":"Reference","previous_headings":"","what":"Check whether the optimisation of distribution parameters has converged to stable value for the parameters and function output for multiple iterations — .check_optim_conv","title":"Check whether the optimisation of distribution parameters has converged to stable value for the parameters and function output for multiple iterations — .check_optim_conv","text":"function try prevent optimisation local optimum thus checks whether multiple optimisation routines consistently finding parameter values within set tolerance.","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/dot-check_optim_conv.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Check whether the optimisation of distribution parameters has converged to stable value for the parameters and function output for multiple iterations — .check_optim_conv","text":"","code":".check_optim_conv(optim_params_list, optim_params, tolerance)"},{"path":"https://epiverse-trace.github.io/epiparameter/reference/dot-check_optim_conv.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Check whether the optimisation of distribution parameters has converged to stable value for the parameters and function output for multiple iterations — .check_optim_conv","text":"optim_params_list list, element output stats::optim(). See ?optim details. optim_params list given output stats::optim(). tolerance numeric specifying within disparity convergence parameter estimates function minimisation accepted.","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/dot-check_optim_conv.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Check whether the optimisation of distribution parameters has converged to stable value for the parameters and function output for multiple iterations — .check_optim_conv","text":"Boolean","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/dot-citet.html","id":null,"dir":"Reference","previous_headings":"","what":"Format short citation from <bibentry> object — .citet","title":"Format short citation from <bibentry> object — .citet","text":"Output equivalent \\citet{} function natbib LaTeX package.","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/dot-citet.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Format short citation from <bibentry> object — .citet","text":"","code":".citet(x)"},{"path":"https://epiverse-trace.github.io/epiparameter/reference/dot-citet.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Format short citation from <bibentry> object — .citet","text":"x <bibentry> object, see bibentry().","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/dot-citet.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Format short citation from <bibentry> object — .citet","text":"character string short citation.","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/dot-clean_params.html","id":null,"dir":"Reference","previous_headings":"","what":"Standardise distribution parameters — .clean_params","title":"Standardise distribution parameters — .clean_params","text":".clean_params() dispatches distribution specific parameter cleaning function depending prob_dist. example prob_dist = \"gamma\" call .clean_params_gamma().","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/dot-clean_params.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Standardise distribution parameters — .clean_params","text":"","code":".clean_params(prob_distribution, prob_distribution_params) .clean_params_gamma(prob_dist_params) .clean_params_lnorm(prob_dist_params) .clean_params_nbinom(prob_dist_params) .clean_params_geom(prob_dist_params) .clean_params_pois(prob_dist_params) .clean_params_norm(prob_dist_params) .clean_params_exp(prob_dist_params)"},{"path":"https://epiverse-trace.github.io/epiparameter/reference/dot-clean_params.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Standardise distribution parameters — .clean_params","text":"prob_distribution character string specifying probability distribution. match R naming convention probability distributions (e.g. lognormal lnorm, negative binomial nbinom, geometric geom). prob_distribution_params named vector probability distribution parameters.","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/dot-clean_params.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Standardise distribution parameters — .clean_params","text":"Named numeric vector parameters.","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/dot-clean_params.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Standardise distribution parameters — .clean_params","text":"Calling is_epiparameter_params() start .clean_params() ensures parameterisation incorrect error early dispatch distribution specific cleaning functions (e.g. .clean_params_gamma()). means distribution specific parameter cleaning functions need check error incorrect parameterisation.","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/dot-clean_string.html","id":null,"dir":"Reference","previous_headings":"","what":"Standardise the variables input by users — .clean_string","title":"Standardise the variables input by users — .clean_string","text":"Checks user supplied character string converts epiparameter standards: lower-case whitespace instead dashes underscores. Examples strings needing cleaned : disease pathogen names, epidemiological distributions.","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/dot-clean_string.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Standardise the variables input by users — .clean_string","text":"","code":".clean_string(x)"},{"path":"https://epiverse-trace.github.io/epiparameter/reference/dot-clean_string.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Standardise the variables input by users — .clean_string","text":"x character string.","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/dot-clean_string.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Standardise the variables input by users — .clean_string","text":"character vector equal length input.","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/dot-clean_uncertainty.html","id":null,"dir":"Reference","previous_headings":"","what":"Standardise distribution parameter uncertainty — .clean_uncertainty","title":"Standardise distribution parameter uncertainty — .clean_uncertainty","text":"Standardise distribution parameter uncertainty","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/dot-clean_uncertainty.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Standardise distribution parameter uncertainty — .clean_uncertainty","text":"","code":".clean_uncertainty(x, prob_distribution, uncertainty_missing)"},{"path":"https://epiverse-trace.github.io/epiparameter/reference/dot-clean_uncertainty.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Standardise distribution parameter uncertainty — .clean_uncertainty","text":"x <epiparameter> object. prob_distribution S3 class containing probability distribution character string parameters probability distribution unknown name distribution known, NA distribution name parameters unknown. Use create_prob_distribution() create prob_distribution. uncertainty_missing boolean logical whether uncertainty missing (see missing()) parent function.","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/dot-clean_uncertainty.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Standardise distribution parameter uncertainty — .clean_uncertainty","text":"uncertainty list <epiparameter> object.","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/dot-convert_params_gamma.html","id":null,"dir":"Reference","previous_headings":"","what":"Convert parameters of the gamma distribution to summary statistics — .convert_params_gamma","title":"Convert parameters of the gamma distribution to summary statistics — .convert_params_gamma","text":"Convert shape scale parameters gamma distribution number summary statistics can calculated analytically given gamma parameters. One exception median calculated using qgamma() analytical form available.","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/dot-convert_params_gamma.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Convert parameters of the gamma distribution to summary statistics — .convert_params_gamma","text":"","code":".convert_params_gamma(...)"},{"path":"https://epiverse-trace.github.io/epiparameter/reference/dot-convert_params_gamma.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Convert parameters of the gamma distribution to summary statistics — .convert_params_gamma","text":"... <dynamic-dots> Numeric named parameter(s) used convert summary statistics. example meanlog sdlog parameters lognormal (lnorm) distribution.","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/dot-convert_params_gamma.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Convert parameters of the gamma distribution to summary statistics — .convert_params_gamma","text":"list eight elements including: mean, median, mode, variance (var), standard deviation (sd), coefficient variation (cv), skewness, excess kurtosis (ex_kurtosis).","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/dot-convert_params_geom.html","id":null,"dir":"Reference","previous_headings":"","what":"Convert parameter of the geometric distribution to summary statistics — .convert_params_geom","title":"Convert parameter of the geometric distribution to summary statistics — .convert_params_geom","text":"Convert probability (prob) geometric distribution number summary statistics can calculated analytically given geometric parameter. One exception median calculated using stats::qgeom() analytical form always unique.","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/dot-convert_params_geom.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Convert parameter of the geometric distribution to summary statistics — .convert_params_geom","text":"","code":".convert_params_geom(...)"},{"path":"https://epiverse-trace.github.io/epiparameter/reference/dot-convert_params_geom.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Convert parameter of the geometric distribution to summary statistics — .convert_params_geom","text":"... <dynamic-dots> Numeric named parameter(s) used convert summary statistics. example meanlog sdlog parameters lognormal (lnorm) distribution.","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/dot-convert_params_geom.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Convert parameter of the geometric distribution to summary statistics — .convert_params_geom","text":"list eight elements including: mean, median, mode, variance (var), standard deviation (sd), coefficient variation (cv), skewness, excess kurtosis (ex_kurtosis).","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/dot-convert_params_geom.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Convert parameter of the geometric distribution to summary statistics — .convert_params_geom","text":"conversion function assumes distribution represents number failures first success (supported zero). form used base R distributional::dist_geometric().","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/dot-convert_params_lnorm.html","id":null,"dir":"Reference","previous_headings":"","what":"Converts the parameters of the lognormal distribution to summary statistics — .convert_params_lnorm","title":"Converts the parameters of the lognormal distribution to summary statistics — .convert_params_lnorm","text":"Converts meanlog sdlog parameters lognormal distribution number summary statistics can calculated analytically given lognormal parameters.","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/dot-convert_params_lnorm.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Converts the parameters of the lognormal distribution to summary statistics — .convert_params_lnorm","text":"","code":".convert_params_lnorm(...)"},{"path":"https://epiverse-trace.github.io/epiparameter/reference/dot-convert_params_lnorm.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Converts the parameters of the lognormal distribution to summary statistics — .convert_params_lnorm","text":"... <dynamic-dots> Numeric named parameter(s) used convert summary statistics. example meanlog sdlog parameters lognormal (lnorm) distribution.","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/dot-convert_params_lnorm.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Converts the parameters of the lognormal distribution to summary statistics — .convert_params_lnorm","text":"list eight elements including: mean, median, mode, variance (var), standard deviation (sd), coefficient variation (cv), skewness, excess kurtosis (ex_kurtosis).","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/dot-convert_params_nbinom.html","id":null,"dir":"Reference","previous_headings":"","what":"Convert parameters of the negative binomial distribution to summary statistics — .convert_params_nbinom","title":"Convert parameters of the negative binomial distribution to summary statistics — .convert_params_nbinom","text":"Convert probability (prob) dispersion parameters negative binomial distribution number summary statistics can calculated analytically given negative binomial parameters. One exception median calculated using qnbinom() analytical form available. parameters prob dispersion (also commonly represented r).","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/dot-convert_params_nbinom.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Convert parameters of the negative binomial distribution to summary statistics — .convert_params_nbinom","text":"","code":".convert_params_nbinom(...)"},{"path":"https://epiverse-trace.github.io/epiparameter/reference/dot-convert_params_nbinom.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Convert parameters of the negative binomial distribution to summary statistics — .convert_params_nbinom","text":"... <dynamic-dots> Numeric named parameter(s) used convert summary statistics. example meanlog sdlog parameters lognormal (lnorm) distribution.","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/dot-convert_params_nbinom.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Convert parameters of the negative binomial distribution to summary statistics — .convert_params_nbinom","text":"list eight elements including: mean, median, mode, variance (var), standard deviation (sd), coefficient variation (cv), skewness, ex_kurtosis.","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/dot-convert_params_weibull.html","id":null,"dir":"Reference","previous_headings":"","what":"Convert parameters of the Weibull distribution to summary statistics — .convert_params_weibull","title":"Convert parameters of the Weibull distribution to summary statistics — .convert_params_weibull","text":"Convert shape scale parameters Weibull distribution number summary statistics can calculated analytically given Weibull parameters. Note conversion uses gamma() function.","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/dot-convert_params_weibull.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Convert parameters of the Weibull distribution to summary statistics — .convert_params_weibull","text":"","code":".convert_params_weibull(...)"},{"path":"https://epiverse-trace.github.io/epiparameter/reference/dot-convert_params_weibull.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Convert parameters of the Weibull distribution to summary statistics — .convert_params_weibull","text":"... <dynamic-dots> Numeric named parameter(s) used convert summary statistics. example meanlog sdlog parameters lognormal (lnorm) distribution.","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/dot-convert_params_weibull.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Convert parameters of the Weibull distribution to summary statistics — .convert_params_weibull","text":"list eight elements including: mean, median, mode, variance (var), standard deviation (sd), coefficient variation (cv), skewness, excess kurtosis (ex_kurtosis).","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/dot-convert_summary_stats_gamma.html","id":null,"dir":"Reference","previous_headings":"","what":"Convert summary statistics to parameters of the gamma distribution — .convert_summary_stats_gamma","title":"Convert summary statistics to parameters of the gamma distribution — .convert_summary_stats_gamma","text":"Convert summary statistics input shape scale parameters gamma distribution.","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/dot-convert_summary_stats_gamma.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Convert summary statistics to parameters of the gamma distribution — .convert_summary_stats_gamma","text":"","code":".convert_summary_stats_gamma(...)"},{"path":"https://epiverse-trace.github.io/epiparameter/reference/dot-convert_summary_stats_gamma.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Convert summary statistics to parameters of the gamma distribution — .convert_summary_stats_gamma","text":"... <dynamic-dots> Numeric named summary statistics used convert parameter(s). example mean sd summary statistics lognormal (lnorm) distribution.","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/dot-convert_summary_stats_gamma.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Convert summary statistics to parameters of the gamma distribution — .convert_summary_stats_gamma","text":"list two elements, shape scale","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/dot-convert_summary_stats_geom.html","id":null,"dir":"Reference","previous_headings":"","what":"Convert summary statistics to parameters of the geometric distribution — .convert_summary_stats_geom","title":"Convert summary statistics to parameters of the geometric distribution — .convert_summary_stats_geom","text":"Convert summary statistics geometric distribution parameter (prob) geometric distribution.","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/dot-convert_summary_stats_geom.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Convert summary statistics to parameters of the geometric distribution — .convert_summary_stats_geom","text":"","code":".convert_summary_stats_geom(...)"},{"path":"https://epiverse-trace.github.io/epiparameter/reference/dot-convert_summary_stats_geom.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Convert summary statistics to parameters of the geometric distribution — .convert_summary_stats_geom","text":"... <dynamic-dots> Numeric named summary statistics used convert parameter(s). example mean sd summary statistics lognormal (lnorm) distribution.","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/dot-convert_summary_stats_geom.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Convert summary statistics to parameters of the geometric distribution — .convert_summary_stats_geom","text":"list one element, probability parameter.","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/dot-convert_summary_stats_geom.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Convert summary statistics to parameters of the geometric distribution — .convert_summary_stats_geom","text":"conversion function assumes distribution represents number failures first success (supported zero). form used base R distributional::dist_geometric().","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/dot-convert_summary_stats_lnorm.html","id":null,"dir":"Reference","previous_headings":"","what":"Convert summary statistics to parameters of the lognormal distribution — .convert_summary_stats_lnorm","title":"Convert summary statistics to parameters of the lognormal distribution — .convert_summary_stats_lnorm","text":"Convert summary statistics input meanlog sdlog parameters lognormal distribution.","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/dot-convert_summary_stats_lnorm.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Convert summary statistics to parameters of the lognormal distribution — .convert_summary_stats_lnorm","text":"","code":".convert_summary_stats_lnorm(...)"},{"path":"https://epiverse-trace.github.io/epiparameter/reference/dot-convert_summary_stats_lnorm.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Convert summary statistics to parameters of the lognormal distribution — .convert_summary_stats_lnorm","text":"... <dynamic-dots> Numeric named summary statistics used convert parameter(s). example mean sd summary statistics lognormal (lnorm) distribution.","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/dot-convert_summary_stats_lnorm.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Convert summary statistics to parameters of the lognormal distribution — .convert_summary_stats_lnorm","text":"list two elements: meanlog sdlog","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/dot-convert_summary_stats_nbinom.html","id":null,"dir":"Reference","previous_headings":"","what":"Convert summary statistics to parameters of the negative binomial distribution — .convert_summary_stats_nbinom","title":"Convert summary statistics to parameters of the negative binomial distribution — .convert_summary_stats_nbinom","text":"Convert summary statistics negative binomial distribution parameters (prob) (dispersion) negative binomial distribution.","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/dot-convert_summary_stats_nbinom.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Convert summary statistics to parameters of the negative binomial distribution — .convert_summary_stats_nbinom","text":"","code":".convert_summary_stats_nbinom(...)"},{"path":"https://epiverse-trace.github.io/epiparameter/reference/dot-convert_summary_stats_nbinom.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Convert summary statistics to parameters of the negative binomial distribution — .convert_summary_stats_nbinom","text":"... <dynamic-dots> Numeric named summary statistics used convert parameter(s). example mean sd summary statistics lognormal (lnorm) distribution.","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/dot-convert_summary_stats_nbinom.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Convert summary statistics to parameters of the negative binomial distribution — .convert_summary_stats_nbinom","text":"list two elements, probability dispersion parameters.","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/dot-convert_summary_stats_weibull.html","id":null,"dir":"Reference","previous_headings":"","what":"Convert summary statistics to parameters of the Weibull distribution — .convert_summary_stats_weibull","title":"Convert summary statistics to parameters of the Weibull distribution — .convert_summary_stats_weibull","text":"Convert summary statistics input shape scale parameters Weibull distribution.","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/dot-convert_summary_stats_weibull.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Convert summary statistics to parameters of the Weibull distribution — .convert_summary_stats_weibull","text":"","code":".convert_summary_stats_weibull(...)"},{"path":"https://epiverse-trace.github.io/epiparameter/reference/dot-convert_summary_stats_weibull.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Convert summary statistics to parameters of the Weibull distribution — .convert_summary_stats_weibull","text":"... <dynamic-dots> Numeric named summary statistics used convert parameter(s). example mean sd summary statistics lognormal (lnorm) distribution.","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/dot-convert_summary_stats_weibull.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Convert summary statistics to parameters of the Weibull distribution — .convert_summary_stats_weibull","text":"list two elements, shape scale.","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/dot-distributional_family.html","id":null,"dir":"Reference","previous_headings":"","what":"Get the underlying distributions names from a <distribution> object from the distributional package in R distribution naming convention. — .distributional_family","title":"Get the underlying distributions names from a <distribution> object from the distributional package in R distribution naming convention. — .distributional_family","text":"Get underlying distributions names <distribution> object distributional package R distribution naming convention.","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/dot-distributional_family.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Get the underlying distributions names from a <distribution> object from the distributional package in R distribution naming convention. — .distributional_family","text":"","code":".distributional_family(x, base_dist = TRUE)"},{"path":"https://epiverse-trace.github.io/epiparameter/reference/dot-distributional_family.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Get the underlying distributions names from a <distribution> object from the distributional package in R distribution naming convention. — .distributional_family","text":"x <distribution> object. base_dist boolean logical whether return name transformed distribution (e.g. \"mixture\" \"truncated\") underlying distribution type (e.g. \"gamma\" \"lnorm\"). Default TRUE.","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/dot-distributional_family.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Get the underlying distributions names from a <distribution> object from the distributional package in R distribution naming convention. — .distributional_family","text":"character vector.","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/dot-distributional_family.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Get the underlying distributions names from a <distribution> object from the distributional package in R distribution naming convention. — .distributional_family","text":"Get standardise distribution name. untransformed distributions (e.g. distributional::dist_gamma()) return distribution name. transformed distributions (e.g. distributional::dist_mixture()) get name underlying distribution(s) default (base_dist = TRUE). Distribution names returned R naming style (e.g. lognormal \"lnorm\"). base_dist = FALSE transformed distributions return name transformation (e.g. \"mixture\").","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/dot-epiparameter_df_to_epiparameter.html","id":null,"dir":"Reference","previous_headings":"","what":"Convert <data.frame> from as.data.frame.epiparameter() to <epiparameter> — .epiparameter_df_to_epiparameter","title":"Convert <data.frame> from as.data.frame.epiparameter() to <epiparameter> — .epiparameter_df_to_epiparameter","text":"Convert <data.frame> .data.frame.epiparameter() <epiparameter>","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/dot-epiparameter_df_to_epiparameter.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Convert <data.frame> from as.data.frame.epiparameter() to <epiparameter> — .epiparameter_df_to_epiparameter","text":"","code":".epiparameter_df_to_epiparameter(x, ...)"},{"path":"https://epiverse-trace.github.io/epiparameter/reference/dot-epiparameter_df_to_epiparameter.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Convert <data.frame> from as.data.frame.epiparameter() to <epiparameter> — .epiparameter_df_to_epiparameter","text":"x <data.frame>. ... dots Extra arguments pass epiparameter().","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/dot-epiparameter_df_to_epiparameter.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Convert <data.frame> from as.data.frame.epiparameter() to <epiparameter> — .epiparameter_df_to_epiparameter","text":"<epiparameter> object.","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/dot-extract_param.html","id":null,"dir":"Reference","previous_headings":"","what":"Optimises the parameters for a specified probability distribution given the percentiles of a distribution and the values at those percentiles, or the median and range of a sample and the number of samples. — .extract_param","title":"Optimises the parameters for a specified probability distribution given the percentiles of a distribution and the values at those percentiles, or the median and range of a sample and the number of samples. — .extract_param","text":"Optimises parameters specified probability distribution given percentiles distribution values percentiles, median range sample number samples.","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/dot-extract_param.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Optimises the parameters for a specified probability distribution given the percentiles of a distribution and the values at those percentiles, or the median and range of a sample and the number of samples. — .extract_param","text":"","code":".extract_param(values, distribution, percentiles, samples)"},{"path":"https://epiverse-trace.github.io/epiparameter/reference/dot-extract_param.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Optimises the parameters for a specified probability distribution given the percentiles of a distribution and the values at those percentiles, or the median and range of a sample and the number of samples. — .extract_param","text":"values vector. type = percentiles: c(percentile_1, percentile_2); type = range: c(median, min, max). distribution character specifying distribution use. Default lnorm; also takes gamma, weibull norm. percentiles vector two elements specifying percentiles defined values using type = \"percentiles\". Percentiles specified 0 1. example 2.5th 97.5th percentile given c(0.025, 0.975). samples numeric specifying sample size using type = \"range\".","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/dot-extract_param.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Optimises the parameters for a specified probability distribution given the percentiles of a distribution and the values at those percentiles, or the median and range of a sample and the number of samples. — .extract_param","text":"list output stats::optim().","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/dot-get_percentiles.html","id":null,"dir":"Reference","previous_headings":"","what":"Convert a vector of named percentiles into correct format and selects two values for parameter extraction — .get_percentiles","title":"Convert a vector of named percentiles into correct format and selects two values for parameter extraction — .get_percentiles","text":"Parameters probability distribution can extracted using values given percentiles distribution percentiles using extract_param(). .get_percentiles() takes named vector percentiles (names) values percentiles (elements vector) selects two values lower upper percentiles used extraction. lower upper percentile available NA returned. also formats vector names can correctly converted numeric using .numeric().","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/dot-get_percentiles.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Convert a vector of named percentiles into correct format and selects two values for parameter extraction — .get_percentiles","text":"","code":".get_percentiles(percentiles)"},{"path":"https://epiverse-trace.github.io/epiparameter/reference/dot-get_percentiles.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Convert a vector of named percentiles into correct format and selects two values for parameter extraction — .get_percentiles","text":"percentiles named vector values percentiles names percentiles. See Details accepted vector name format.","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/dot-get_percentiles.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Convert a vector of named percentiles into correct format and selects two values for parameter extraction — .get_percentiles","text":"named numeric vector percentiles.","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/dot-get_percentiles.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Convert a vector of named percentiles into correct format and selects two values for parameter extraction — .get_percentiles","text":"name format character value percentile. Numbers decimal places decimal point name. example 5th 95th percentile can given 2.5th 97.5th percentile can given ","code":".get_percentiles(c(\"5\" = 1, \"95\" = 10)) .get_percentiles(c(\"2.5\" = 1, \"97.5\" = 10))"},{"path":"https://epiverse-trace.github.io/epiparameter/reference/dot-get_sym_percentiles.html","id":null,"dir":"Reference","previous_headings":"","what":"Get the lower and upper percentiles with a preference for symmetrical percentiles — .get_sym_percentiles","title":"Get the lower and upper percentiles with a preference for symmetrical percentiles — .get_sym_percentiles","text":"Get lower upper percentiles preference symmetrical percentiles","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/dot-get_sym_percentiles.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Get the lower and upper percentiles with a preference for symmetrical percentiles — .get_sym_percentiles","text":"","code":".get_sym_percentiles(percentiles)"},{"path":"https://epiverse-trace.github.io/epiparameter/reference/dot-get_sym_percentiles.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Get the lower and upper percentiles with a preference for symmetrical percentiles — .get_sym_percentiles","text":"percentiles named vector percentiles. names correct format converted numeric value using .numeric().","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/dot-get_sym_percentiles.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Get the lower and upper percentiles with a preference for symmetrical percentiles — .get_sym_percentiles","text":"named numeric vector two elements lower (first element) upper (second element) percentiles.","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/dot-is_epireview.html","id":null,"dir":"Reference","previous_headings":"","what":"Check if <data.frame> input is from epireview — .is_epireview","title":"Check if <data.frame> input is from epireview — .is_epireview","text":"Check <data.frame> input epireview","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/dot-is_epireview.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Check if <data.frame> input is from epireview — .is_epireview","text":"","code":".is_epireview(x)"},{"path":"https://epiverse-trace.github.io/epiparameter/reference/dot-is_epireview.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Check if <data.frame> input is from epireview — .is_epireview","text":"x <data.frame>.","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/dot-is_epireview.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Check if <data.frame> input is from epireview — .is_epireview","text":"single logical boolean.","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/epidist_db.html","id":null,"dir":"Reference","previous_headings":"","what":"Create <epiparameter> object(s) directly from the epiparameter library (database) — epidist_db","title":"Create <epiparameter> object(s) directly from the epiparameter library (database) — epidist_db","text":"epidist_db() renamed epiparameter_db(). Please use epiparameter_db() instead epidist_db() alias removed package future.","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/epidist_db.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Create <epiparameter> object(s) directly from the epiparameter library (database) — epidist_db","text":"","code":"epidist_db( disease = \"all\", pathogen = \"all\", epi_name = \"all\", author = NULL, subset = NULL, single_epiparameter = FALSE )"},{"path":"https://epiverse-trace.github.io/epiparameter/reference/epidist_db.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Create <epiparameter> object(s) directly from the epiparameter library (database) — epidist_db","text":"disease character string specifying disease. pathogen character string specifying pathogen. epi_name character string specifying epidemiological parameter. See details full list epidemiological distributions. author character string specifying author study reporting distribution. first author matched. recommended use family name first names may may initialised. subset Either NULL valid R expressions evaluates logicals subset list <epiparameter>, function can applied list <epiparameter> objects. Subsetting (using subset) can combined subsetting done disease epi_name arguments (author specified). left NULL (default) subsetting carried . subset argument similar subsetting <data.frame>, difference fixed comparisons vectorised comparisons needed. example sample_size > 10 valid subset expression, sample_size == max(sample_size), valid subset expression <data.frame> work. vectorised expression often error, likely return unexpected results. sample_size == max(sample_size) example always return TRUE (except NAs) single numeric equal max value. expression specified without using data object name (e.g. df$var) instead just var supplied. words, argument uses non-standard evaluation, just subset argument subset(), similar <data-masking> used dplyr package. single_epiparameter boolean logical determining whether single <epiparameter> multiple entries library can returned matched arguments (disease, epi_name, author). argument used prevent multiple sets parameters returned one wanted. Note: multiple entries match arguments supplied single_epiparameter = TRUE <epiparameter> parameterised (accounts truncation available) largest sample size returned (see is_parameterised()). multiple entries equal sorting first entry returned.","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/epidist_db.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Create <epiparameter> object(s) directly from the epiparameter library (database) — epidist_db","text":"<epiparameter> object list <epiparameter> objects.","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/epiparameter-package.html","id":null,"dir":"Reference","previous_headings":"","what":"epiparameter: Library of Epidemiological Parameters with Helper Functions and Classes — epiparameter-package","title":"epiparameter: Library of Epidemiological Parameters with Helper Functions and Classes — epiparameter-package","text":"Library epidemiological parameters infectious diseases extracted literature, set classes helper functions working parameters.","code":""},{"path":[]},{"path":"https://epiverse-trace.github.io/epiparameter/reference/epiparameter-package.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"epiparameter: Library of Epidemiological Parameters with Helper Functions and Classes — epiparameter-package","text":"Maintainer: Joshua W. Lambert joshua.lambert@lshtm.ac.uk (ORCID) [copyright holder] Authors: Adam Kucharski adam.kucharski@lshtm.ac.uk (ORCID) [copyright holder] Carmen Tamayo carmen.tamayo-cuartero@lshtm.ac.uk (ORCID) contributors: Hugo Gruson hugo.gruson@data.org (ORCID) [contributor, reviewer] Sebastian Funk sebastian.funk@lshtm.ac.uk (ORCID) [contributor] Pratik Gupte pratik.gupte@lshtm.ac.uk (ORCID) [reviewer] James M. Azam james.azam@lshtm.ac.uk (ORCID) [reviewer] Chris Hartgerink chris@data.org (ORCID) [reviewer] Tim Taylor tim.taylor@hiddenelephants.co.uk (ORCID) [reviewer]","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/epiparameter.html","id":null,"dir":"Reference","previous_headings":"","what":"Create an <epiparameter> object — epiparameter","title":"Create an <epiparameter> object — epiparameter","text":"<epiparameter> class used store epidemiological parameters single disease. epidemiological parameters cover variety aspects including delay distributions (e.g. incubation periods serial intervals, among others) offspring distributions. <epiparameter> object functional unit provided {epiparameter} plug epidemiological pipelines. Obtaining <epiparameter> object can achieved two main ways: epidemiological distribution stored {epiparameter} library can accessed epiparameter_db(). alternative method information (e.g. disease distribution parameter estimates) like input <epiparameter> object order work existing analysis pipelines. epiparameter() function can used fill field information known.","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/epiparameter.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Create an <epiparameter> object — epiparameter","text":"","code":"epiparameter( disease, pathogen = NA_character_, epi_name, prob_distribution = create_prob_distribution(prob_distribution = NA_character_), uncertainty = create_uncertainty(), summary_stats = create_summary_stats(), citation = create_citation(), metadata = create_metadata(), method_assess = create_method_assess(), notes = NULL, auto_calc_params = TRUE, ... )"},{"path":"https://epiverse-trace.github.io/epiparameter/reference/epiparameter.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Create an <epiparameter> object — epiparameter","text":"disease character string name infectious disease. pathogen character string name causative agent disease, NA known. epi_name character string name epidemiological parameter type. prob_distribution S3 class containing probability distribution character string parameters probability distribution unknown name distribution known, NA distribution name parameters unknown. Use create_prob_distribution() create prob_distribution. uncertainty list named vectors uncertainty around probability distribution parameters. uncertainty around parameter estimates unknown use create_uncertainty() (argument default) create list correct names missing values. summary_stats list summary statistics, use create_summary_stats() create list. list can include summary statistics inferred distribution mean standard deviation, quantiles distribution, information data used fit distribution lower upper range. summary statistics can also include uncertainty around metrics confidence interval around mean standard deviation. citation <bibentry> citation source data paper inferred distribution parameters, use create_citation() create citation. metadata list metadata, can include: units, sample size, transmission mode disease (e.g. vector-borne directly transmitted), etc. assumed disease vector-borne distribution intrinsic (e.g. extrinsic delay distribution extrinsic incubation period) unless transmission_mode = \"vector_borne\" contained metadata. Use create_metadata() create metadata. method_assess list methodological aspects used fitting distribution, use create_method_assess() create method assessment. notes character string additional information data, inference method disease. auto_calc_params boolean logical determining whether try calculate probability distribution parameters summary statistics distribution parameters provided. Default TRUE. case sufficient summary statistics provided parameter(s) distribution , .calc_dist_params() function called calculate parameters add epiparameter object created. ... dots Extra arguments passed internal functions. commonly used pass arguments distcrete::distcrete() construct discretised distribution S3 object. see arguments can adjusted discretised distributions see distcrete::distcrete().","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/epiparameter.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Create an <epiparameter> object — epiparameter","text":"<epiparameter> object.","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/epiparameter.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Create an <epiparameter> object — epiparameter","text":"Accepted <epiparameter> distribution parameterisations : Gamma must either 'shape' 'scale' 'shape' 'rate' Weibull must 'shape' 'scale' Lognormal must 'meanlog' 'sdlog' 'mu' 'sigma' Negative Binomial must either 'mean' 'dispersion' 'n' 'p' Geometric must either 'mean' 'prob' Poisson must 'mean'","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/epiparameter.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Create an <epiparameter> object — epiparameter","text":"","code":"# minimal input required for `epiparameter` ebola_incubation <- epiparameter( disease = \"ebola\", epi_name = \"incubation_period\", prob_distribution = create_prob_distribution( prob_distribution = \"gamma\", prob_distribution_params = c(shape = 1, scale = 1) ) ) #> Citation cannot be created as author, year, journal or title is missing # minimal input required for discrete `epiparameter` ebola_incubation <- epiparameter( disease = \"ebola\", epi_name = \"incubation_period\", prob_distribution = create_prob_distribution( prob_distribution = \"gamma\", prob_distribution_params = c(shape = 1, scale = 1), discretise = TRUE ) ) #> Citation cannot be created as author, year, journal or title is missing # example with more fields filled in ebola_incubation <- epiparameter( disease = \"ebola\", pathogen = \"ebola_virus\", epi_name = \"incubation\", prob_distribution = create_prob_distribution( prob_distribution = \"gamma\", prob_distribution_params = c(shape = 1, scale = 1), discretise = FALSE, truncation = NA ), uncertainty = list( shape = create_uncertainty(), scale = create_uncertainty() ), summary_stats = create_summary_stats(mean = 2, sd = 1), citation = create_citation( author = person(given = \"John\", family = \"Smith\"), year = 2002, title = \"COVID-19 incubation period\", journal = \"Epi Journal\", doi = \"10.19832/j.1366-9516.2012.09147.x\" ), metadata = create_metadata( units = \"days\", sample_size = 10, region = \"UK\", transmission_mode = \"natural_human_to_human\", inference_method = \"MLE\" ), method_assess = create_method_assess( censored = TRUE ), notes = \"No notes\" ) #> Using Smith J (2002). “COVID-19 incubation period.” _Epi Journal_. #> doi:10.19832/j.1366-9516.2012.09147.x #> <https://doi.org/10.19832/j.1366-9516.2012.09147.x>. #> To retrieve the citation use the 'get_citation' function"},{"path":"https://epiverse-trace.github.io/epiparameter/reference/epiparameter_db.html","id":null,"dir":"Reference","previous_headings":"","what":"Create <epiparameter> object(s) directly from the epiparameter library (database) — epiparameter_db","title":"Create <epiparameter> object(s) directly from the epiparameter library (database) — epiparameter_db","text":"Extract <epiparameter> object(s) directly library epidemiological parameters. epiparameter library epidemiological parameters compiled primary literature sources. list output epiparameter_db() can subset data contains, example : disease, pathogen, epidemiological distribution, sample size, region, etc. distribution specific study required, author argument can specified. Multiple entries (<epiparameter> objects) can returned, use arguments subset entries use single_epiparameter = TRUE force single <epiparameter> returned.","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/epiparameter_db.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Create <epiparameter> object(s) directly from the epiparameter library (database) — epiparameter_db","text":"","code":"epiparameter_db( disease = \"all\", pathogen = \"all\", epi_name = \"all\", author = NULL, subset = NULL, single_epiparameter = FALSE )"},{"path":"https://epiverse-trace.github.io/epiparameter/reference/epiparameter_db.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Create <epiparameter> object(s) directly from the epiparameter library (database) — epiparameter_db","text":"disease character string specifying disease. pathogen character string specifying pathogen. epi_name character string specifying epidemiological parameter. See details full list epidemiological distributions. author character string specifying author study reporting distribution. first author matched. recommended use family name first names may may initialised. subset Either NULL valid R expressions evaluates logicals subset list <epiparameter>, function can applied list <epiparameter> objects. Subsetting (using subset) can combined subsetting done disease epi_name arguments (author specified). left NULL (default) subsetting carried . subset argument similar subsetting <data.frame>, difference fixed comparisons vectorised comparisons needed. example sample_size > 10 valid subset expression, sample_size == max(sample_size), valid subset expression <data.frame> work. vectorised expression often error, likely return unexpected results. sample_size == max(sample_size) example always return TRUE (except NAs) single numeric equal max value. expression specified without using data object name (e.g. df$var) instead just var supplied. words, argument uses non-standard evaluation, just subset argument subset(), similar <data-masking> used dplyr package. single_epiparameter boolean logical determining whether single <epiparameter> multiple entries library can returned matched arguments (disease, epi_name, author). argument used prevent multiple sets parameters returned one wanted. Note: multiple entries match arguments supplied single_epiparameter = TRUE <epiparameter> parameterised (accounts truncation available) largest sample size returned (see is_parameterised()). multiple entries equal sorting first entry returned.","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/epiparameter_db.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Create <epiparameter> object(s) directly from the epiparameter library (database) — epiparameter_db","text":"<epiparameter> object list <epiparameter> objects.","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/epiparameter_db.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Create <epiparameter> object(s) directly from the epiparameter library (database) — epiparameter_db","text":"disease, epi_name author given individual arguments common variables subset parameter library . subset argument facilitates subsetting rows select <epiparameter> object(s) desired. subset based multiple variables separate expression &. List epidemiological parameters: \"\" (default, returns entries library) \"incubation period\" \"onset hospitalisation\" \"onset death\" \"serial interval\" \"generation time\" \"offspring distribution\" \"hospitalisation death\" \"hospitalisation discharge\" \"notification death\" \"notification discharge\" \"onset discharge\" \"onset ventilation\"","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/epiparameter_db.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Create <epiparameter> object(s) directly from the epiparameter library (database) — epiparameter_db","text":"","code":"epiparameter_db(disease = \"influenza\", epi_name = \"serial_interval\") #> Returning 1 results that match the criteria (1 are parameterised). #> Use subset to filter by entry variables or single_epiparameter to return a single entry. #> To retrieve the citation for each use the 'get_citation' function #> Disease: Influenza #> Pathogen: Influenza-A-H1N1Pdm #> Epi Parameter: serial interval #> Study: Ghani A, Baguelin M, Griffin J, Flasche S, van Hoek A, Cauchemez S, #> Donnelly C, Robertson C, White M, Truscott J, Fraser C, Garske T, White #> P, Leach S, Hall I, Jenkins H, Ferguson N, Cooper B (2009). “The Early #> Transmission Dynamics of H1N1pdm Influenza in the United Kingdom.” #> _PLoS Currents_. doi:10.1371/currents.RRN1130 #> <https://doi.org/10.1371/currents.RRN1130>. #> Distribution: gamma (days) #> Parameters: #> shape: 2.622 #> scale: 0.957 # example using custom subsetting eparam <- epiparameter_db( disease = \"SARS\", epi_name = \"offspring_distribution\", subset = sample_size > 40 ) #> Returning 1 results that match the criteria (1 are parameterised). #> Use subset to filter by entry variables or single_epiparameter to return a single entry. #> To retrieve the citation for each use the 'get_citation' function # example using functional subsetting eparam <- epiparameter_db( disease = \"COVID-19\", epi_name = \"incubation_period\", subset = is_parameterised ) #> Returning 11 results that match the criteria (11 are parameterised). #> Use subset to filter by entry variables or single_epiparameter to return a single entry. #> To retrieve the citation for each use the 'get_citation' function # example forcing a single <epiparameter> to be returned eparam <- epiparameter_db( disease = \"SARS\", epi_name = \"offspring_distribution\", single_epiparameter = TRUE ) #> Using Lloyd-Smith J, Schreiber S, Kopp P, Getz W (2005). “Superspreading and #> the effect of individual variation on disease emergence.” _Nature_. #> doi:10.1038/nature04153 <https://doi.org/10.1038/nature04153>.. #> To retrieve the citation use the 'get_citation' function"},{"path":"https://epiverse-trace.github.io/epiparameter/reference/epiparameter_distribution_functions.html","id":null,"dir":"Reference","previous_headings":"","what":"PDF, CDF, PMF, quantiles and random number generation for <epiparameter> objects — epiparameter_distribution_functions","title":"PDF, CDF, PMF, quantiles and random number generation for <epiparameter> objects — epiparameter_distribution_functions","text":"<epiparameter> object holds probability distribution can either continuous discrete distribution. density, cumulative distribution, quantile random number generation functions. operate distribution can included <epiparameter> object.","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/epiparameter_distribution_functions.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"PDF, CDF, PMF, quantiles and random number generation for <epiparameter> objects — epiparameter_distribution_functions","text":"","code":"# S3 method for class 'epiparameter' density(x, at, ...) # S3 method for class 'epiparameter' cdf(x, q, ..., log = FALSE) # S3 method for class 'epiparameter' quantile(x, p, ...) # S3 method for class 'epiparameter' generate(x, times, ...)"},{"path":"https://epiverse-trace.github.io/epiparameter/reference/epiparameter_distribution_functions.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"PDF, CDF, PMF, quantiles and random number generation for <epiparameter> objects — epiparameter_distribution_functions","text":"x <epiparameter> object. quantiles evaluate . ... dots Extra arguments passed method. q quantiles evaluate . log TRUE, probabilities given log probabilities. p probabilities evaluate . times number random samples.","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/epiparameter_distribution_functions.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"PDF, CDF, PMF, quantiles and random number generation for <epiparameter> objects — epiparameter_distribution_functions","text":"numeric vector.","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/epiparameter_distribution_functions.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"PDF, CDF, PMF, quantiles and random number generation for <epiparameter> objects — epiparameter_distribution_functions","text":"","code":"ep <- epiparameter( disease = \"ebola\", epi_name = \"incubation_period\", prob_distribution = create_prob_distribution( prob_distribution = \"gamma\", prob_distribution_params = c(shape = 1, scale = 1) ) ) #> Citation cannot be created as author, year, journal or title is missing # example of each distribution method for an `epiparameter` object stats::density(ep, at = 1) #> [1] 0.3678794 distributional::cdf(ep, q = 1) #> [1] 0.6321206 stats::quantile(ep, p = 0.2) #> [1] 0.2231436 distributional::generate(ep, times = 10) #> [1] 3.56720380 1.16790186 0.05745463 0.34040705 3.47156091 1.04366207 #> [7] 3.02627506 0.36756952 0.09970044 0.78957870"},{"path":"https://epiverse-trace.github.io/epiparameter/reference/epiparameter_options.html","id":null,"dir":"Reference","previous_headings":"","what":"Package options — epiparameter_options","title":"Package options — epiparameter_options","text":"Options modify printing epiparameter objects. Currently options used modify printing <multi_epiparameter> class.","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/epiparameter_options.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Package options — epiparameter_options","text":"","code":"epiparameter_options"},{"path":"https://epiverse-trace.github.io/epiparameter/reference/epiparameter_options.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Package options — epiparameter_options","text":"object class list length 2.","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/epiparameter_options.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Package options — epiparameter_options","text":"Options set options() retrieved getOption(). options changed epiparameter package need reloaded new options taken account. Options can set .Rprofile persist across R sessions.","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/epireview_core_cols.html","id":null,"dir":"Reference","previous_headings":"","what":"A vector of character strings with the core column names of the epidemiological parameter data exported by the epireview R package. — epireview_core_cols","title":"A vector of character strings with the core column names of the epidemiological parameter data exported by the epireview R package. — epireview_core_cols","text":"vector character strings core column names epidemiological parameter data exported epireview R package.","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/epireview_core_cols.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"A vector of character strings with the core column names of the epidemiological parameter data exported by the epireview R package. — epireview_core_cols","text":"","code":"epireview_core_cols"},{"path":[]},{"path":"https://epiverse-trace.github.io/epiparameter/reference/epireview_core_cols.html","id":"epireview-core-cols","dir":"Reference","previous_headings":"","what":"epireview_core_cols","title":"A vector of character strings with the core column names of the epidemiological parameter data exported by the epireview R package. — epireview_core_cols","text":"character vector 58 elements. data taken intersection column names disease parameter tables epireview R package.","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/epireview_core_cols.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"A vector of character strings with the core column names of the epidemiological parameter data exported by the epireview R package. — epireview_core_cols","text":"https://github.com/mrc-ide/epireview","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/extract_param.html","id":null,"dir":"Reference","previous_headings":"","what":"Calculate the parameters of a parametric probability distribution from reported values of percentiles, or median and range — extract_param","title":"Calculate the parameters of a parametric probability distribution from reported values of percentiles, or median and range — extract_param","text":"Summary data distributions, provided reports meta-analyses, can used extract parameters chosen distribution. Currently available distributions : lognormal, gamma, Weibull normal. Extracting lognormal returns meanlog sdlog parameters, extracting gamma Weibull returns shape scale parameters, extracting normal returns mean sd parameters.","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/extract_param.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculate the parameters of a parametric probability distribution from reported values of percentiles, or median and range — extract_param","text":"","code":"extract_param( type = c(\"percentiles\", \"range\"), values, distribution = c(\"lnorm\", \"gamma\", \"weibull\", \"norm\"), percentiles, samples, control = list(max_iter = 1000, tolerance = 1e-05) )"},{"path":"https://epiverse-trace.github.io/epiparameter/reference/extract_param.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Calculate the parameters of a parametric probability distribution from reported values of percentiles, or median and range — extract_param","text":"type character defining whether summary statistics based around percentiles (default) range. values vector. type = percentiles: c(percentile_1, percentile_2); type = range: c(median, min, max). distribution character specifying distribution use. Default lnorm; also takes gamma, weibull norm. percentiles vector two elements specifying percentiles defined values using type = \"percentiles\". Percentiles specified 0 1. example 2.5th 97.5th percentile given c(0.025, 0.975). samples numeric specifying sample size using type = \"range\". control named list containing options optimisation. List element $max_iter numeric specifying maximum number times parameter extraction run optimisation returning result early. prevents overly long optimisation loops optimisation unstable converge multiple iterations. Default 1000 iterations. List element $tolerance passed .check_optim_conv() tolerance parameter convergence iterations optimisation. Elements control list passed optim().","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/extract_param.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Calculate the parameters of a parametric probability distribution from reported values of percentiles, or median and range — extract_param","text":"named numeric vector parameter values distribution. distribution = lnorm parameters returned meanlog sdlog; distribution = gamma distribution = weibull parameters returned shape scale; distribution = norm parameters returned mean sd.","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/extract_param.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Calculate the parameters of a parametric probability distribution from reported values of percentiles, or median and range — extract_param","text":"gamma, lnorm weibull, extract_param() works strictly positive values percentiles distribution median range data (numerics supplied values argument). means negative values lower percentile lower range work function although may present epidemiological data (e.g. negative serial interval). norm distribution negative values allowed.","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/extract_param.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Calculate the parameters of a parametric probability distribution from reported values of percentiles, or median and range — extract_param","text":"Adam Kucharski, Joshua W. Lambert","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/extract_param.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Calculate the parameters of a parametric probability distribution from reported values of percentiles, or median and range — extract_param","text":"","code":"# set seed to control for stochasticity set.seed(1) # extract parameters of a lognormal distribution from the 75 percentiles extract_param( type = \"percentiles\", values = c(6, 13), distribution = \"lnorm\", percentiles = c(0.125, 0.875) ) #> Stochastic numerical optimisation used. #> Rerun function multiple times to check global optimum is found #> meanlog sdlog #> 2.1783557 0.3360688 # extract parameters of a gamma distribution from median and range extract_param( type = \"range\", values = c(10, 3, 18), distribution = \"gamma\", samples = 20 ) #> Stochastic numerical optimisation used. #> Rerun function multiple times to check global optimum is found #> shape scale #> 5.342206 1.994304"},{"path":"https://epiverse-trace.github.io/epiparameter/reference/extraction_functions.html","id":null,"dir":"Reference","previous_headings":"","what":"Function for extracting distribution parameters — extraction_functions","title":"Function for extracting distribution parameters — extraction_functions","text":"Set functions can used estimate parameters distribution (lognormal, gamma, Weibull, normal) via optimisation either percentiles median ranges.","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/extraction_functions.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Function for extracting distribution parameters — extraction_functions","text":"","code":".fit_range(param, val, dist = c(\"lnorm\", \"gamma\", \"weibull\", \"norm\")) .fit_percentiles(param, val, dist = c(\"lnorm\", \"gamma\", \"weibull\", \"norm\"))"},{"path":"https://epiverse-trace.github.io/epiparameter/reference/extraction_functions.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Function for extracting distribution parameters — extraction_functions","text":"param Named numeric vector distribution parameters optimised. val Numeric vector, case using percentiles contains values percentiles percentiles, case median range contains median, lower range, upper range number sample points evaluate function . dist character string name distribution fitting. Naming follows base R distribution names (e.g. lnorm lognormal).","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/extraction_functions.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Function for extracting distribution parameters — extraction_functions","text":"Adam Kucharski, Joshua W. Lambert","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/family.epiparameter.html","id":null,"dir":"Reference","previous_headings":"","what":"Family method for the <epiparameter> class — family.epiparameter","title":"Family method for the <epiparameter> class — family.epiparameter","text":"family() function used extract distribution names objects {distributional} {distcrete}. method provides interface <epiparameter> objects give consistent output irrespective internal distribution class.","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/family.epiparameter.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Family method for the <epiparameter> class — family.epiparameter","text":"","code":"# S3 method for class 'epiparameter' family(object, ..., base_dist = FALSE)"},{"path":"https://epiverse-trace.github.io/epiparameter/reference/family.epiparameter.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Family method for the <epiparameter> class — family.epiparameter","text":"object <epiparameter> object. ... arguments passed methods. base_dist boolean logical whether return name transformed distribution (e.g. \"mixture\" \"truncated\") underlying distribution type (e.g. \"gamma\" \"lnorm\"). Default FALSE.","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/family.epiparameter.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Family method for the <epiparameter> class — family.epiparameter","text":"character string name distribution, NA <epiparameter> object unparameterised.","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/family.epiparameter.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Family method for the <epiparameter> class — family.epiparameter","text":"","code":"# example with continuous distribution ep <- epiparameter( disease = \"ebola\", epi_name = \"incubation_period\", prob_distribution = create_prob_distribution( prob_distribution = \"gamma\", prob_distribution_params = c(shape = 1, scale = 1) ) ) #> Citation cannot be created as author, year, journal or title is missing family(ep) #> [1] \"gamma\" # example with discretised distribution ep <- epiparameter( disease = \"ebola\", epi_name = \"incubation_period\", prob_distribution = create_prob_distribution( prob_distribution = \"lnorm\", prob_distribution_params = c(meanlog = 1, sdlog = 1), discretise = TRUE ) ) #> Citation cannot be created as author, year, journal or title is missing family(ep) #> [1] \"lnorm\""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/format.epiparameter.html","id":null,"dir":"Reference","previous_headings":"","what":"Format method for <epiparameter> class — format.epiparameter","title":"Format method for <epiparameter> class — format.epiparameter","text":"Format method <epiparameter> class","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/format.epiparameter.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Format method for <epiparameter> class — format.epiparameter","text":"","code":"# S3 method for class 'epiparameter' format(x, ...)"},{"path":"https://epiverse-trace.github.io/epiparameter/reference/format.epiparameter.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Format method for <epiparameter> class — format.epiparameter","text":"x <epiparameter> object. ... dots Extra arguments passed method.","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/format.epiparameter.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Format method for <epiparameter> class — format.epiparameter","text":"Invisibly returns <epiparameter>. Called printing side-effects.","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/format.epiparameter.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Format method for <epiparameter> class — format.epiparameter","text":"","code":"epiparameter <- epiparameter( disease = \"ebola\", epi_name = \"incubation_period\", prob_distribution = create_prob_distribution( prob_distribution = \"gamma\", prob_distribution_params = c(shape = 1, scale = 1) ) ) #> Citation cannot be created as author, year, journal or title is missing format(epiparameter) #> Disease: ebola #> Pathogen: NA #> Epi Parameter: incubation period #> Study: (????). “No citation.” #> Distribution: gamma (NA) #> Parameters: #> shape: 1.000 #> scale: 1.000"},{"path":"https://epiverse-trace.github.io/epiparameter/reference/get_citation.epiparameter.html","id":null,"dir":"Reference","previous_headings":"","what":"Get citation from an <epiparameter> object — get_citation.epiparameter","title":"Get citation from an <epiparameter> object — get_citation.epiparameter","text":"Extract citation stored <epiparameter> object.","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/get_citation.epiparameter.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Get citation from an <epiparameter> object — get_citation.epiparameter","text":"","code":"# S3 method for class 'epiparameter' get_citation(x, ...)"},{"path":"https://epiverse-trace.github.io/epiparameter/reference/get_citation.epiparameter.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Get citation from an <epiparameter> object — get_citation.epiparameter","text":"x <epiparameter> object. ... dots used, extra arguments supplied cause warning.","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/get_citation.epiparameter.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Get citation from an <epiparameter> object — get_citation.epiparameter","text":"<bibentry> object.","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/get_citation.epiparameter.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Get citation from an <epiparameter> object — get_citation.epiparameter","text":"","code":"# example with <epiparameter> ep <- epiparameter_db(single_epiparameter = TRUE) #> Using Linton N, Kobayashi T, Yang Y, Hayashi K, Akhmetzhanov A, Jung S, Yuan #> B, Kinoshita R, Nishiura H (2020). “Incubation Period and Other #> Epidemiological Characteristics of 2019 Novel Coronavirus Infections #> with Right Truncation: A Statistical Analysis of Publicly Available #> Case Data.” _Journal of Clinical Medicine_. doi:10.3390/jcm9020538 #> <https://doi.org/10.3390/jcm9020538>.. #> To retrieve the citation use the 'get_citation' function get_citation(ep) #> Linton N, Kobayashi T, Yang Y, Hayashi K, Akhmetzhanov A, Jung S, Yuan #> B, Kinoshita R, Nishiura H (2020). “Incubation Period and Other #> Epidemiological Characteristics of 2019 Novel Coronavirus Infections #> with Right Truncation: A Statistical Analysis of Publicly Available #> Case Data.” _Journal of Clinical Medicine_. doi:10.3390/jcm9020538 #> <https://doi.org/10.3390/jcm9020538>. # example returning bibtex format ep <- epiparameter_db(disease = \"COVID-19\", single_epiparameter = TRUE) #> Using Linton N, Kobayashi T, Yang Y, Hayashi K, Akhmetzhanov A, Jung S, Yuan #> B, Kinoshita R, Nishiura H (2020). “Incubation Period and Other #> Epidemiological Characteristics of 2019 Novel Coronavirus Infections #> with Right Truncation: A Statistical Analysis of Publicly Available #> Case Data.” _Journal of Clinical Medicine_. doi:10.3390/jcm9020538 #> <https://doi.org/10.3390/jcm9020538>.. #> To retrieve the citation use the 'get_citation' function cit <- get_citation(ep) format(cit, style = \"bibtex\") #> [1] \"@Article{,\\n author = {Natalie M. Linton and Tetsuro Kobayashi and Yichi Yang and Katsuma Hayashi and Andrei R. Akhmetzhanov and Sung-mok Jung and Baoyin Yuan and Ryo Kinoshita and Hiroshi Nishiura},\\n year = {2020},\\n title = {Incubation Period and Other Epidemiological Characteristics of 2019 Novel Coronavirus Infections with Right Truncation: A Statistical Analysis of Publicly Available Case Data},\\n journal = {Journal of Clinical Medicine},\\n doi = {10.3390/jcm9020538},\\n pmid = {32079150},\\n}\""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/get_citation.html","id":null,"dir":"Reference","previous_headings":"","what":"Get citation from an object — get_citation","title":"Get citation from an object — get_citation","text":"Extract citation stored R object.","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/get_citation.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Get citation from an object — get_citation","text":"","code":"get_citation(x, ...)"},{"path":"https://epiverse-trace.github.io/epiparameter/reference/get_citation.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Get citation from an object — get_citation","text":"x object used select method. ... dots Extra arguments passed method.","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/get_citation.multi_epiparameter.html","id":null,"dir":"Reference","previous_headings":"","what":"Get citation from a list of <epiparameter> objects — get_citation.multi_epiparameter","title":"Get citation from a list of <epiparameter> objects — get_citation.multi_epiparameter","text":"Extract citation stored list <epiparameter> objects.","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/get_citation.multi_epiparameter.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Get citation from a list of <epiparameter> objects — get_citation.multi_epiparameter","text":"","code":"# S3 method for class 'multi_epiparameter' get_citation(x, ...)"},{"path":"https://epiverse-trace.github.io/epiparameter/reference/get_citation.multi_epiparameter.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Get citation from a list of <epiparameter> objects — get_citation.multi_epiparameter","text":"x <epiparameter> object. ... dots used, extra arguments supplied cause warning.","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/get_citation.multi_epiparameter.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Get citation from a list of <epiparameter> objects — get_citation.multi_epiparameter","text":"<bibentry> object containing multiple references. length output <bibentry> equal length list <epiparameter> objects supplied.","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/get_citation.multi_epiparameter.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Get citation from a list of <epiparameter> objects — get_citation.multi_epiparameter","text":"","code":"# example with list of <epiparameter> db <- epiparameter_db() #> Returning 125 results that match the criteria (100 are parameterised). #> Use subset to filter by entry variables or single_epiparameter to return a single entry. #> To retrieve the citation for each use the 'get_citation' function get_citation(db) #> Lessler J, Reich N, Brookmeyer R, Perl T, Nelson K, Cummings D (2009). #> “Incubation periods of acute respiratory viral infections: a systematic #> review.” _The Lancet Infectious Diseases_. #> doi:10.1016/S1473-3099(09)70069-6 #> <https://doi.org/10.1016/S1473-3099%2809%2970069-6>. #> #> Lessler J, Reich N, Brookmeyer R, Perl T, Nelson K, Cummings D (2009). #> “Incubation periods of acute respiratory viral infections: a systematic #> review.” _The Lancet Infectious Diseases_. #> doi:10.1016/S1473-3099(09)70069-7 #> <https://doi.org/10.1016/S1473-3099%2809%2970069-7>. #> #> Lessler J, Reich N, Brookmeyer R, Perl T, Nelson K, Cummings D (2009). #> “Incubation periods of acute respiratory viral infections: a systematic #> review.” _The Lancet Infectious Diseases_. #> doi:10.1016/S1473-3099(09)70069-8 #> <https://doi.org/10.1016/S1473-3099%2809%2970069-8>. #> #> Lessler J, Reich N, Brookmeyer R, Perl T, Nelson K, Cummings D (2009). #> “Incubation periods of acute respiratory viral infections: a systematic #> review.” _The Lancet Infectious Diseases_. #> doi:10.1016/S1473-3099(09)70069-9 #> <https://doi.org/10.1016/S1473-3099%2809%2970069-9>. #> #> Lessler J, Reich N, Brookmeyer R, Perl T, Nelson K, Cummings D (2009). #> “Incubation periods of acute respiratory viral infections: a systematic #> review.” _The Lancet Infectious Diseases_. #> doi:10.1016/S1473-3099(09)70069-10 #> <https://doi.org/10.1016/S1473-3099%2809%2970069-10>. #> #> Lessler J, Reich N, Brookmeyer R, Perl T, Nelson K, Cummings D (2009). #> “Incubation periods of acute respiratory viral infections: a systematic #> review.” _The Lancet Infectious Diseases_. #> doi:10.1016/S1473-3099(09)70069-11 #> <https://doi.org/10.1016/S1473-3099%2809%2970069-11>. #> #> Lessler J, Reich N, Brookmeyer R, Perl T, Nelson K, Cummings D (2009). #> “Incubation periods of acute respiratory viral infections: a systematic #> review.” _The Lancet Infectious Diseases_. #> doi:10.1016/S1473-3099(09)70069-12 #> <https://doi.org/10.1016/S1473-3099%2809%2970069-12>. #> #> Lessler J, Reich N, Brookmeyer R, Perl T, Nelson K, Cummings D (2009). #> “Incubation periods of acute respiratory viral infections: a systematic #> review.” _The Lancet Infectious Diseases_. #> doi:10.1016/S1473-3099(09)70069-13 #> <https://doi.org/10.1016/S1473-3099%2809%2970069-13>. #> #> Lessler J, Reich N, Brookmeyer R, Perl T, Nelson K, Cummings D (2009). #> “Incubation periods of acute respiratory viral infections: a systematic #> review.” _The Lancet Infectious Diseases_. #> doi:10.1016/S1473-3099(09)70069-14 #> <https://doi.org/10.1016/S1473-3099%2809%2970069-14>. #> #> Lessler J, Reich N, Brookmeyer R, Perl T, Nelson K, Cummings D (2009). #> “Incubation periods of acute respiratory viral infections: a systematic #> review.” _The Lancet Infectious Diseases_. #> doi:10.1016/S1473-3099(09)70069-15 #> <https://doi.org/10.1016/S1473-3099%2809%2970069-15>. #> #> Reich N, Lessler J, Cummings D, Brookmeyer R (2009). “Estimating #> incubation period distributions with coarse data.” _Statistics in #> Medicine_. doi:10.1002/sim.3659 <https://doi.org/10.1002/sim.3659>. #> #> Reich N, Lessler J, Cummings D, Brookmeyer R (2009). “Estimating #> incubation period distributions with coarse data.” _Statistics in #> Medicine_. doi:10.1002/sim.3659 <https://doi.org/10.1002/sim.3659>. #> #> Reich N, Lessler J, Cummings D, Brookmeyer R (2009). “Estimating #> incubation period distributions with coarse data.” _Statistics in #> Medicine_. doi:10.1002/sim.3659 <https://doi.org/10.1002/sim.3659>. #> #> Reich N, Lessler J, Cummings D, Brookmeyer R (2009). “Estimating #> incubation period distributions with coarse data.” _Statistics in #> Medicine_. doi:10.1002/sim.3659 <https://doi.org/10.1002/sim.3659>. #> #> Nishiura H, Inaba H (2011). “Estimation of the incubation period of #> influenza A (H1N1-2009) among imported cases: addressing censoring #> using outbreak data at the origin of importation.” _Journal of #> Theoretical Biology_. doi:10.1016/j.jtbi.2010.12.017 #> <https://doi.org/10.1016/j.jtbi.2010.12.017>. #> #> Nishiura H, Inaba H (2011). “Estimation of the incubation period of #> influenza A (H1N1-2009) among imported cases: addressing censoring #> using outbreak data at the origin of importation.” _Journal of #> Theoretical Biology_. doi:10.1016/j.jtbi.2010.12.017 #> <https://doi.org/10.1016/j.jtbi.2010.12.017>. #> #> Virlogeux V, Li M, Tsang T, Feng L, Fang V, Jiang H, Wu P, Zheng J, Lau #> E, Cao Y, Qin Y, Liao Q, Yu H, Cowling B (2015). “Estimating the #> Distribution of the Incubation Periods of Human Avian Influenza A(H7N9) #> Virus Infections.” _American Journal of Epidemiology_. #> doi:10.1093/aje/kwv115 <https://doi.org/10.1093/aje/kwv115>. #> #> Virlogeux V, Li M, Tsang T, Feng L, Fang V, Jiang H, Wu P, Zheng J, Lau #> E, Cao Y, Qin Y, Liao Q, Yu H, Cowling B (2015). “Estimating the #> Distribution of the Incubation Periods of Human Avian Influenza A(H7N9) #> Virus Infections.” _American Journal of Epidemiology_. #> doi:10.1093/aje/kwv115 <https://doi.org/10.1093/aje/kwv115>. #> #> Virlogeux V, Yang J, Fang V, Feng L, Tsang T, Jiang H, Wu P, Zheng J, #> Lau E, Qin Y, Peng Z, Peiris J, Yu H, Cowling B (2016). “Association #> between the Severity of Influenza A(H7N9) Virus Infections and Length #> of the Incubation Period.” _PLoS One_. doi:10.1371/journal.pone.0148506 #> <https://doi.org/10.1371/journal.pone.0148506>. #> #> Virlogeux V, Yang J, Fang V, Feng L, Tsang T, Jiang H, Wu P, Zheng J, #> Lau E, Qin Y, Peng Z, Peiris J, Yu H, Cowling B (2016). “Association #> between the Severity of Influenza A(H7N9) Virus Infections and Length #> of the Incubation Period.” _PLoS One_. doi:10.1371/journal.pone.0148506 #> <https://doi.org/10.1371/journal.pone.0148506>. #> #> Virlogeux V, Yang J, Fang V, Feng L, Tsang T, Jiang H, Wu P, Zheng J, #> Lau E, Qin Y, Peng Z, Peiris J, Yu H, Cowling B (2016). “Association #> between the Severity of Influenza A(H7N9) Virus Infections and Length #> of the Incubation Period.” _PLoS One_. doi:10.1371/journal.pone.0148506 #> <https://doi.org/10.1371/journal.pone.0148506>. #> #> Tuite A, Greer A, Whelan M, Winter A, Lee B, Yan P, Wu J, Moghadas S, #> Buckeridge D, Pourbohloul B, Fisman D (2010). “Estimated epidemiologic #> parameters and morbidity associated with pandemic H1N1 influenza.” #> _Canadian Medical Association Journal_. doi:10.1503/cmaj.091807 #> <https://doi.org/10.1503/cmaj.091807>. #> #> Ghani A, Baguelin M, Griffin J, Flasche S, van Hoek A, Cauchemez S, #> Donnelly C, Robertson C, White M, Truscott J, Fraser C, Garske T, White #> P, Leach S, Hall I, Jenkins H, Ferguson N, Cooper B (2009). “The Early #> Transmission Dynamics of H1N1pdm Influenza in the United Kingdom.” #> _PLoS Currents_. doi:10.1371/currents.RRN1130 #> <https://doi.org/10.1371/currents.RRN1130>. #> #> Ghani A, Baguelin M, Griffin J, Flasche S, van Hoek A, Cauchemez S, #> Donnelly C, Robertson C, White M, Truscott J, Fraser C, Garske T, White #> P, Leach S, Hall I, Jenkins H, Ferguson N, Cooper B (2009). “The Early #> Transmission Dynamics of H1N1pdm Influenza in the United Kingdom.” #> _PLoS Currents_. doi:10.1371/currents.RRN1130 #> <https://doi.org/10.1371/currents.RRN1130>. #> #> Lessler J, Reich N, Cummings D, New York City Department of Health and #> Mental Hygiene Swine Influenza Investigation Team (2009). “Outbreak of #> 2009 Pandemic Influenza A (H1N1) at a New York City School.” _The New #> England Journal of Medicine_. doi:10.1056/NEJMoa0906089 #> <https://doi.org/10.1056/NEJMoa0906089>. #> #> Lessler J, Reich N, Cummings D, New York City Department of Health and #> Mental Hygiene Swine Influenza Investigation Team (2009). “Outbreak of #> 2009 Pandemic Influenza A (H1N1) at a New York City School.” _The New #> England Journal of Medicine_. doi:10.1056/NEJMoa0906089 #> <https://doi.org/10.1056/NEJMoa0906089>. #> #> Pavlin B (2014). “Calculation of incubation period and serial interval #> from multiple outbreaks of Marburg virus disease.” _BMC Research #> Notes_. doi:10.1186/1756-0500-7-906 #> <https://doi.org/10.1186/1756-0500-7-906>. #> #> Pavlin B (2014). “Calculation of incubation period and serial interval #> from multiple outbreaks of Marburg virus disease.” _BMC Research #> Notes_. doi:10.1186/1756-0500-7-906 #> <https://doi.org/10.1186/1756-0500-7-906>. #> #> Pavlin B (2014). “Calculation of incubation period and serial interval #> from multiple outbreaks of Marburg virus disease.” _BMC Research #> Notes_. doi:10.1186/1756-0500-7-906 #> <https://doi.org/10.1186/1756-0500-7-906>. #> #> Colebunders R, Tshomba A, Van Kerkhove M, Bausch D, Campbell P, Libande #> M, Pirard P, Tshioko F, Mardel S, Mulangu S, Sleurs H, Rollin P, #> Muyembe-Tamfum J, Jeffs B, Borchert M, International Scientific and #> Technical Committee 'DRC Watsa/Durba 1999 Marburg Outbreak #> Investigation Group' (2007). “Marburg hemorrhagic fever in Durba and #> Watsa, Democratic Republic of the Congo: clinical documentation, #> features of illness, and treatment.” _The Journal of Infectious #> Diseases_. doi:10.1086/520543 <https://doi.org/10.1086/520543>. #> #> Ajelli M, Merler S (2012). “Transmission Potential and Design of #> Adequate Control Measures for Marburg Hemorrhagic Fever.” _PLoS One_. #> doi:10.1371/journal.pone.0050948 #> <https://doi.org/10.1371/journal.pone.0050948>. #> #> Lloyd-Smith J, Schreiber S, Kopp P, Getz W (2005). “Superspreading and #> the effect of individual variation on disease emergence.” _Nature_. #> doi:10.1038/nature04153 <https://doi.org/10.1038/nature04153>. #> #> Lloyd-Smith J, Schreiber S, Kopp P, Getz W (2005). “Superspreading and #> the effect of individual variation on disease emergence.” _Nature_. #> doi:10.1038/nature04153 <https://doi.org/10.1038/nature04153>. #> #> Lloyd-Smith J, Schreiber S, Kopp P, Getz W (2005). “Superspreading and #> the effect of individual variation on disease emergence.” _Nature_. #> doi:10.1038/nature04153 <https://doi.org/10.1038/nature04153>. #> #> Lloyd-Smith J, Schreiber S, Kopp P, Getz W (2005). “Superspreading and #> the effect of individual variation on disease emergence.” _Nature_. #> doi:10.1038/nature04153 <https://doi.org/10.1038/nature04153>. #> #> Lloyd-Smith J, Schreiber S, Kopp P, Getz W (2005). “Superspreading and #> the effect of individual variation on disease emergence.” _Nature_. #> doi:10.1038/nature04153 <https://doi.org/10.1038/nature04153>. #> #> Lloyd-Smith J, Schreiber S, Kopp P, Getz W (2005). “Superspreading and #> the effect of individual variation on disease emergence.” _Nature_. #> doi:10.1038/nature04153 <https://doi.org/10.1038/nature04153>. #> #> Lloyd-Smith J, Schreiber S, Kopp P, Getz W (2005). “Superspreading and #> the effect of individual variation on disease emergence.” _Nature_. #> doi:10.1038/nature04153 <https://doi.org/10.1038/nature04153>. #> #> Lloyd-Smith J, Schreiber S, Kopp P, Getz W (2005). “Superspreading and #> the effect of individual variation on disease emergence.” _Nature_. #> doi:10.1038/nature04153 <https://doi.org/10.1038/nature04153>. #> #> Lloyd-Smith J, Schreiber S, Kopp P, Getz W (2005). “Superspreading and #> the effect of individual variation on disease emergence.” _Nature_. #> doi:10.1038/nature04153 <https://doi.org/10.1038/nature04153>. #> #> Lloyd-Smith J, Schreiber S, Kopp P, Getz W (2005). “Superspreading and #> the effect of individual variation on disease emergence.” _Nature_. #> doi:10.1038/nature04153 <https://doi.org/10.1038/nature04153>. #> #> Chan M, Johansson M (2012). “The Incubation Periods of Dengue Viruses.” #> _PLoS One_. doi:10.1371/journal.pone.0050972 #> <https://doi.org/10.1371/journal.pone.0050972>. #> #> Chan M, Johansson M (2012). “The Incubation Periods of Dengue Viruses.” #> _PLoS One_. doi:10.1371/journal.pone.0050972 #> <https://doi.org/10.1371/journal.pone.0050972>. #> #> Chan M, Johansson M (2012). “The Incubation Periods of Dengue Viruses.” #> _PLoS One_. doi:10.1371/journal.pone.0050972 #> <https://doi.org/10.1371/journal.pone.0050972>. #> #> Lessler J, Ott C, Carcelen A, Konikoff J, Williamson J, Bi Q, Kucirka #> L, Cummings D, Reich N, Chaisson L (2016). “Times to key events in Zika #> virus infection and implications for blood donation: a systematic #> review.” _Bulletin of the World Health Organization_. #> doi:10.2471/BLT.16.174540 <https://doi.org/10.2471/BLT.16.174540>. #> #> Rudolph K, Lessler J, Moloney R, Kmush B, Cummings D (2014). #> “Incubation periods of mosquito-borne viral infections: a systematic #> review.” _The American Journal of Tropical Medicine and Hygiene_. #> doi:10.4269/ajtmh.13-0403 <https://doi.org/10.4269/ajtmh.13-0403>. #> #> Rudolph K, Lessler J, Moloney R, Kmush B, Cummings D (2014). #> “Incubation periods of mosquito-borne viral infections: a systematic #> review.” _The American Journal of Tropical Medicine and Hygiene_. #> doi:10.4269/ajtmh.13-0403 <https://doi.org/10.4269/ajtmh.13-0403>. #> #> Rudolph K, Lessler J, Moloney R, Kmush B, Cummings D (2014). #> “Incubation periods of mosquito-borne viral infections: a systematic #> review.” _The American Journal of Tropical Medicine and Hygiene_. #> doi:10.4269/ajtmh.13-0403 <https://doi.org/10.4269/ajtmh.13-0403>. #> #> Rudolph K, Lessler J, Moloney R, Kmush B, Cummings D (2014). #> “Incubation periods of mosquito-borne viral infections: a systematic #> review.” _The American Journal of Tropical Medicine and Hygiene_. #> doi:10.4269/ajtmh.13-0403 <https://doi.org/10.4269/ajtmh.13-0403>. #> #> Rudolph K, Lessler J, Moloney R, Kmush B, Cummings D (2014). #> “Incubation periods of mosquito-borne viral infections: a systematic #> review.” _The American Journal of Tropical Medicine and Hygiene_. #> doi:10.4269/ajtmh.13-0403 <https://doi.org/10.4269/ajtmh.13-0403>. #> #> Rudolph K, Lessler J, Moloney R, Kmush B, Cummings D (2014). #> “Incubation periods of mosquito-borne viral infections: a systematic #> review.” _The American Journal of Tropical Medicine and Hygiene_. #> doi:10.4269/ajtmh.13-0403 <https://doi.org/10.4269/ajtmh.13-0403>. #> #> Rudolph K, Lessler J, Moloney R, Kmush B, Cummings D (2014). #> “Incubation periods of mosquito-borne viral infections: a systematic #> review.” _The American Journal of Tropical Medicine and Hygiene_. #> doi:10.4269/ajtmh.13-0403 <https://doi.org/10.4269/ajtmh.13-0403>. #> #> Rudolph K, Lessler J, Moloney R, Kmush B, Cummings D (2014). #> “Incubation periods of mosquito-borne viral infections: a systematic #> review.” _The American Journal of Tropical Medicine and Hygiene_. #> doi:10.4269/ajtmh.13-0403 <https://doi.org/10.4269/ajtmh.13-0403>. #> #> Rudolph K, Lessler J, Moloney R, Kmush B, Cummings D (2014). #> “Incubation periods of mosquito-borne viral infections: a systematic #> review.” _The American Journal of Tropical Medicine and Hygiene_. #> doi:10.4269/ajtmh.13-0403 <https://doi.org/10.4269/ajtmh.13-0403>. #> #> Rudolph K, Lessler J, Moloney R, Kmush B, Cummings D (2014). #> “Incubation periods of mosquito-borne viral infections: a systematic #> review.” _The American Journal of Tropical Medicine and Hygiene_. #> doi:10.4269/ajtmh.13-0403 <https://doi.org/10.4269/ajtmh.13-0403>. #> #> Miura F, van Ewijk C, Backer J, Xiridou M, Franz E, Op de Coul E, #> Brandwagt D, van Cleef B, van Rijckevorsel G, Swaan C, van den Hof S, #> Wallinga J (2022). “Estimated incubation period for monkeypox cases #> confirmed in the Netherlands, May 2022.” _Eurosurveillance_. #> doi:10.2807/1560-7917.ES.2022.27.24.2200448 #> <https://doi.org/10.2807/1560-7917.ES.2022.27.24.2200448>. #> #> Charniga K, Masters N, Slayton R, Gosdin L, Minhaj F, Philpott D, Smith #> D, Gearhart S, Alvarado-Ramy F, Brown C, Waltenburg M, Hughes C, #> Nakazawa Y (2022). “Estimating the incubation period of monkeypox virus #> during the 2022 multi-national outbreak.” _medRxiv_. #> doi:10.1101/2022.06.22.22276713 #> <https://doi.org/10.1101/2022.06.22.22276713>. #> #> Guzzetta G, Mammone A, Ferraro F, Caraglia A, Rapiti A, Marziano V, #> Poletti P, Cereda D, Vairo F, Mattei G, Maraglino F, Rezza G, Merler S #> (2022). “Early Estimates of Monkeypox Incubation Period, Generation #> Time, and Reproduction Number, Italy, May-June 2022.” _Emerging #> Infectious Diseases_. doi:10.3201/eid2810.221126 #> <https://doi.org/10.3201/eid2810.221126>. #> #> Madewell Z, Charniga K, Masters N, Asher J, Fahrenwald L, Still W, Chen #> J, Kipperman N, Bui D, Shea M, Saathoff-Huber L, Johnson S, Harbi K, #> Berns A, Perez T, Gateley E, Spicknall I, Nakazawa Y, Gift T, 2022 #> Monkeypox Outbreak Response Team (2022). “Serial interval and #> incubation period estimates of monkeypox virus infection in 12 U.S. #> jurisdictions, May – August 2022.” _medRxiv_. #> doi:10.1101/2022.10.26.22281516 #> <https://doi.org/10.1101/2022.10.26.22281516>. #> #> Madewell Z, Charniga K, Masters N, Asher J, Fahrenwald L, Still W, Chen #> J, Kipperman N, Bui D, Shea M, Saathoff-Huber L, Johnson S, Harbi K, #> Berns A, Perez T, Gateley E, Spicknall I, Nakazawa Y, Gift T, 2022 #> Monkeypox Outbreak Response Team (2022). “Serial interval and #> incubation period estimates of monkeypox virus infection in 12 U.S. #> jurisdictions, May – August 2023.” _medRxiv_. #> doi:10.1101/2022.10.26.22281516 #> <https://doi.org/10.1101/2022.10.26.22281516>. #> #> Madewell Z, Charniga K, Masters N, Asher J, Fahrenwald L, Still W, Chen #> J, Kipperman N, Bui D, Shea M, Saathoff-Huber L, Johnson S, Harbi K, #> Berns A, Perez T, Gateley E, Spicknall I, Nakazawa Y, Gift T, 2022 #> Monkeypox Outbreak Response Team (2022). “Serial interval and #> incubation period estimates of monkeypox virus infection in 12 U.S. #> jurisdictions, May – August 2024.” _medRxiv_. #> doi:10.1101/2022.10.26.22281516 #> <https://doi.org/10.1101/2022.10.26.22281516>. #> #> Madewell Z, Charniga K, Masters N, Asher J, Fahrenwald L, Still W, Chen #> J, Kipperman N, Bui D, Shea M, Saathoff-Huber L, Johnson S, Harbi K, #> Berns A, Perez T, Gateley E, Spicknall I, Nakazawa Y, Gift T, 2022 #> Monkeypox Outbreak Response Team (2022). “Serial interval and #> incubation period estimates of monkeypox virus infection in 12 U.S. #> jurisdictions, May – August 2025.” _medRxiv_. #> doi:10.1101/2022.10.26.22281516 #> <https://doi.org/10.1101/2022.10.26.22281516>. #> #> Eichner M, Dowell S, Firese N (2011). “Incubation period of ebola #> hemorrhagic virus subtype zaire.” _Osong Public Health and Research #> Perspectives_. doi:10.1016/j.phrp.2011.04.001 #> <https://doi.org/10.1016/j.phrp.2011.04.001>. #> #> The Ebola Outbreak Epidemiology Team, Barry A, Ahuka-Mundeke S, Ali #> Ahmed Y, Allarangar Y, Anoko J, Archer B, Abedi A, Bagaria J, Belizaire #> M, Bhatia S, Bokenge T, Bruni E, Cori A, Dabire E, Diallo A, Diallo B, #> Donnelly C, Dorigatti I, Dorji T, Waeber A, Fall I, Ferguson N, #> FitzJohn R, Tengomo G, Formenty P, Forna A, Fortin A, Garske T, #> Gaythorpe K, Gurry C, Hamblion E, Djingarey M, Haskew C, Hugonnet S, #> Imai N, Impouma B, Kabongo G, Kalenga O, Kibangou E, Lee T, Lukoya C, #> Ly O, Makiala-Mandanda S, Mamba A, Mbala-Kingebeni P, Mboussou F, #> Mlanda T, Makuma V, Morgan O, Mulumba A, Kakoni P, Mukadi-Bamuleka D, #> Muyembe J, Bathé N, Ndumbi Ngamala P, Ngom R, Ngoy G, Nouvellet P, Nsio #> J, Ousman K, Peron E, Polonsky J, Ryan M, Touré A, Towner R, Tshapenda #> G, Van De Weerdt R, Van Kerkhove M, Wendland A, Yao N, Yoti Z, Yuma E, #> Kalambayi Kabamba G, Mwati J, Mbuy G, Lubula L, Mutombo A, Mavila O, #> Lay Y, Kitenge E (2018). “Outbreak of Ebola virus disease in the #> Democratic Republic of the Congo, April–May, 2018: an epidemiological #> study.” _The Lancet_. doi:10.1016/S0140-6736(18)31387-4 #> <https://doi.org/10.1016/S0140-6736%2818%2931387-4>. #> #> WHO Ebola Response Team, Agua-Agum J, Ariyarajah A, Aylward B, Blake I, #> Brennan R, Cori A, Donnelly C, Dorigatti I, Dye C, Eckmanns T, Ferguson #> N, Formenty P, Fraser C, Garcia E, Garske T, Hinsley W, Holmes D, #> Hugonnet S, Iyengar S, Jombart T, Krishnan R, Meijers S, Mills H, #> Mohamed Y, Nedjati-Gilani G, Newton E, Nouvellet P, Pelletier L, #> Perkins D, Riley S, Sagrado M, Schnitzler J, Schumacher D, Shah A, Van #> Kerkhove M, Varsaneux O, Kannangarage N (2015). “West African Ebola #> Epidemic after One Year — Slowing but Not Yet under Control.” _The New #> England Journal of Medicine_. doi:10.1056/NEJMc1414992 #> <https://doi.org/10.1056/NEJMc1414992>. #> #> WHO Ebola Response Team, Agua-Agum J, Ariyarajah A, Aylward B, Blake I, #> Brennan R, Cori A, Donnelly C, Dorigatti I, Dye C, Eckmanns T, Ferguson #> N, Formenty P, Fraser C, Garcia E, Garske T, Hinsley W, Holmes D, #> Hugonnet S, Iyengar S, Jombart T, Krishnan R, Meijers S, Mills H, #> Mohamed Y, Nedjati-Gilani G, Newton E, Nouvellet P, Pelletier L, #> Perkins D, Riley S, Sagrado M, Schnitzler J, Schumacher D, Shah A, Van #> Kerkhove M, Varsaneux O, Kannangarage N (2015). “West African Ebola #> Epidemic after One Year — Slowing but Not Yet under Control.” _The New #> England Journal of Medicine_. doi:10.1056/NEJMc1414992 #> <https://doi.org/10.1056/NEJMc1414992>. #> #> WHO Ebola Response Team, Agua-Agum J, Ariyarajah A, Aylward B, Blake I, #> Brennan R, Cori A, Donnelly C, Dorigatti I, Dye C, Eckmanns T, Ferguson #> N, Formenty P, Fraser C, Garcia E, Garske T, Hinsley W, Holmes D, #> Hugonnet S, Iyengar S, Jombart T, Krishnan R, Meijers S, Mills H, #> Mohamed Y, Nedjati-Gilani G, Newton E, Nouvellet P, Pelletier L, #> Perkins D, Riley S, Sagrado M, Schnitzler J, Schumacher D, Shah A, Van #> Kerkhove M, Varsaneux O, Kannangarage N (2015). “West African Ebola #> Epidemic after One Year — Slowing but Not Yet under Control.” _The New #> England Journal of Medicine_. doi:10.1056/NEJMc1414992 #> <https://doi.org/10.1056/NEJMc1414992>. #> #> WHO Ebola Response Team, Agua-Agum J, Ariyarajah A, Aylward B, Blake I, #> Brennan R, Cori A, Donnelly C, Dorigatti I, Dye C, Eckmanns T, Ferguson #> N, Formenty P, Fraser C, Garcia E, Garske T, Hinsley W, Holmes D, #> Hugonnet S, Iyengar S, Jombart T, Krishnan R, Meijers S, Mills H, #> Mohamed Y, Nedjati-Gilani G, Newton E, Nouvellet P, Pelletier L, #> Perkins D, Riley S, Sagrado M, Schnitzler J, Schumacher D, Shah A, Van #> Kerkhove M, Varsaneux O, Kannangarage N (2015). “West African Ebola #> Epidemic after One Year — Slowing but Not Yet under Control.” _The New #> England Journal of Medicine_. doi:10.1056/NEJMc1414992 #> <https://doi.org/10.1056/NEJMc1414992>. #> #> WHO Ebola Response Team, Agua-Agum J, Ariyarajah A, Aylward B, Blake I, #> Brennan R, Cori A, Donnelly C, Dorigatti I, Dye C, Eckmanns T, Ferguson #> N, Formenty P, Fraser C, Garcia E, Garske T, Hinsley W, Holmes D, #> Hugonnet S, Iyengar S, Jombart T, Krishnan R, Meijers S, Mills H, #> Mohamed Y, Nedjati-Gilani G, Newton E, Nouvellet P, Pelletier L, #> Perkins D, Riley S, Sagrado M, Schnitzler J, Schumacher D, Shah A, Van #> Kerkhove M, Varsaneux O, Kannangarage N (2015). “West African Ebola #> Epidemic after One Year — Slowing but Not Yet under Control.” _The New #> England Journal of Medicine_. doi:10.1056/NEJMc1414992 #> <https://doi.org/10.1056/NEJMc1414992>. #> #> WHO Ebola Response Team, Agua-Agum J, Ariyarajah A, Aylward B, Blake I, #> Brennan R, Cori A, Donnelly C, Dorigatti I, Dye C, Eckmanns T, Ferguson #> N, Formenty P, Fraser C, Garcia E, Garske T, Hinsley W, Holmes D, #> Hugonnet S, Iyengar S, Jombart T, Krishnan R, Meijers S, Mills H, #> Mohamed Y, Nedjati-Gilani G, Newton E, Nouvellet P, Pelletier L, #> Perkins D, Riley S, Sagrado M, Schnitzler J, Schumacher D, Shah A, Van #> Kerkhove M, Varsaneux O, Kannangarage N (2015). “West African Ebola #> Epidemic after One Year — Slowing but Not Yet under Control.” _The New #> England Journal of Medicine_. doi:10.1056/NEJMc1414992 #> <https://doi.org/10.1056/NEJMc1414992>. #> #> WHO Ebola Response Team, Agua-Agum J, Ariyarajah A, Aylward B, Blake I, #> Brennan R, Cori A, Donnelly C, Dorigatti I, Dye C, Eckmanns T, Ferguson #> N, Formenty P, Fraser C, Garcia E, Garske T, Hinsley W, Holmes D, #> Hugonnet S, Iyengar S, Jombart T, Krishnan R, Meijers S, Mills H, #> Mohamed Y, Nedjati-Gilani G, Newton E, Nouvellet P, Pelletier L, #> Perkins D, Riley S, Sagrado M, Schnitzler J, Schumacher D, Shah A, Van #> Kerkhove M, Varsaneux O, Kannangarage N (2015). “West African Ebola #> Epidemic after One Year — Slowing but Not Yet under Control.” _The New #> England Journal of Medicine_. doi:10.1056/NEJMc1414992 #> <https://doi.org/10.1056/NEJMc1414992>. #> #> WHO Ebola Response Team, Agua-Agum J, Ariyarajah A, Aylward B, Blake I, #> Brennan R, Cori A, Donnelly C, Dorigatti I, Dye C, Eckmanns T, Ferguson #> N, Formenty P, Fraser C, Garcia E, Garske T, Hinsley W, Holmes D, #> Hugonnet S, Iyengar S, Jombart T, Krishnan R, Meijers S, Mills H, #> Mohamed Y, Nedjati-Gilani G, Newton E, Nouvellet P, Pelletier L, #> Perkins D, Riley S, Sagrado M, Schnitzler J, Schumacher D, Shah A, Van #> Kerkhove M, Varsaneux O, Kannangarage N (2015). “West African Ebola #> Epidemic after One Year — Slowing but Not Yet under Control.” _The New #> England Journal of Medicine_. doi:10.1056/NEJMc1414992 #> <https://doi.org/10.1056/NEJMc1414992>. #> #> WHO Ebola Response Team, Agua-Agum J, Ariyarajah A, Aylward B, Blake I, #> Brennan R, Cori A, Donnelly C, Dorigatti I, Dye C, Eckmanns T, Ferguson #> N, Formenty P, Fraser C, Garcia E, Garske T, Hinsley W, Holmes D, #> Hugonnet S, Iyengar S, Jombart T, Krishnan R, Meijers S, Mills H, #> Mohamed Y, Nedjati-Gilani G, Newton E, Nouvellet P, Pelletier L, #> Perkins D, Riley S, Sagrado M, Schnitzler J, Schumacher D, Shah A, Van #> Kerkhove M, Varsaneux O, Kannangarage N (2015). “West African Ebola #> Epidemic after One Year — Slowing but Not Yet under Control.” _The New #> England Journal of Medicine_. doi:10.1056/NEJMc1414992 #> <https://doi.org/10.1056/NEJMc1414992>. #> #> WHO Ebola Response Team, Agua-Agum J, Ariyarajah A, Aylward B, Blake I, #> Brennan R, Cori A, Donnelly C, Dorigatti I, Dye C, Eckmanns T, Ferguson #> N, Formenty P, Fraser C, Garcia E, Garske T, Hinsley W, Holmes D, #> Hugonnet S, Iyengar S, Jombart T, Krishnan R, Meijers S, Mills H, #> Mohamed Y, Nedjati-Gilani G, Newton E, Nouvellet P, Pelletier L, #> Perkins D, Riley S, Sagrado M, Schnitzler J, Schumacher D, Shah A, Van #> Kerkhove M, Varsaneux O, Kannangarage N (2015). “West African Ebola #> Epidemic after One Year — Slowing but Not Yet under Control.” _The New #> England Journal of Medicine_. doi:10.1056/NEJMc1414992 #> <https://doi.org/10.1056/NEJMc1414992>. #> #> WHO Ebola Response Team, Agua-Agum J, Ariyarajah A, Aylward B, Blake I, #> Brennan R, Cori A, Donnelly C, Dorigatti I, Dye C, Eckmanns T, Ferguson #> N, Formenty P, Fraser C, Garcia E, Garske T, Hinsley W, Holmes D, #> Hugonnet S, Iyengar S, Jombart T, Krishnan R, Meijers S, Mills H, #> Mohamed Y, Nedjati-Gilani G, Newton E, Nouvellet P, Pelletier L, #> Perkins D, Riley S, Sagrado M, Schnitzler J, Schumacher D, Shah A, Van #> Kerkhove M, Varsaneux O, Kannangarage N (2015). “West African Ebola #> Epidemic after One Year — Slowing but Not Yet under Control.” _The New #> England Journal of Medicine_. doi:10.1056/NEJMc1414992 #> <https://doi.org/10.1056/NEJMc1414992>. #> #> WHO Ebola Response Team, Agua-Agum J, Ariyarajah A, Aylward B, Blake I, #> Brennan R, Cori A, Donnelly C, Dorigatti I, Dye C, Eckmanns T, Ferguson #> N, Formenty P, Fraser C, Garcia E, Garske T, Hinsley W, Holmes D, #> Hugonnet S, Iyengar S, Jombart T, Krishnan R, Meijers S, Mills H, #> Mohamed Y, Nedjati-Gilani G, Newton E, Nouvellet P, Pelletier L, #> Perkins D, Riley S, Sagrado M, Schnitzler J, Schumacher D, Shah A, Van #> Kerkhove M, Varsaneux O, Kannangarage N (2015). “West African Ebola #> Epidemic after One Year — Slowing but Not Yet under Control.” _The New #> England Journal of Medicine_. doi:10.1056/NEJMc1414992 #> <https://doi.org/10.1056/NEJMc1414992>. #> #> WHO Ebola Response Team, Agua-Agum J, Ariyarajah A, Aylward B, Blake I, #> Brennan R, Cori A, Donnelly C, Dorigatti I, Dye C, Eckmanns T, Ferguson #> N, Formenty P, Fraser C, Garcia E, Garske T, Hinsley W, Holmes D, #> Hugonnet S, Iyengar S, Jombart T, Krishnan R, Meijers S, Mills H, #> Mohamed Y, Nedjati-Gilani G, Newton E, Nouvellet P, Pelletier L, #> Perkins D, Riley S, Sagrado M, Schnitzler J, Schumacher D, Shah A, Van #> Kerkhove M, Varsaneux O, Kannangarage N (2015). “West African Ebola #> Epidemic after One Year — Slowing but Not Yet under Control.” _The New #> England Journal of Medicine_. doi:10.1056/NEJMc1414992 #> <https://doi.org/10.1056/NEJMc1414992>. #> #> WHO Ebola Response Team, Agua-Agum J, Ariyarajah A, Aylward B, Blake I, #> Brennan R, Cori A, Donnelly C, Dorigatti I, Dye C, Eckmanns T, Ferguson #> N, Formenty P, Fraser C, Garcia E, Garske T, Hinsley W, Holmes D, #> Hugonnet S, Iyengar S, Jombart T, Krishnan R, Meijers S, Mills H, #> Mohamed Y, Nedjati-Gilani G, Newton E, Nouvellet P, Pelletier L, #> Perkins D, Riley S, Sagrado M, Schnitzler J, Schumacher D, Shah A, Van #> Kerkhove M, Varsaneux O, Kannangarage N (2015). “West African Ebola #> Epidemic after One Year — Slowing but Not Yet under Control.” _The New #> England Journal of Medicine_. doi:10.1056/NEJMc1414992 #> <https://doi.org/10.1056/NEJMc1414992>. #> #> Assiri A, McGeer A, Perl T, Price C, Al Rabeeah A, Cummings D, #> Alabdullatif Z, Assad M, Almulhim A, Makhdoom H, Madani H, Alhakeem R, #> Al-Tawfiq J, Cotten M, Watson S, Kellam P, Zumla A, Memish Z, KSA #> MERSCOV Investigation Team (2013). “Hospital Outbreak of Middle East #> Respiratory Syndrome Coronavirus.” _The New England Journal of #> Medicine_. doi:10.1056/NEJMoa1306742 #> <https://doi.org/10.1056/NEJMoa1306742>. #> #> Assiri A, McGeer A, Perl T, Price C, Al Rabeeah A, Cummings D, #> Alabdullatif Z, Assad M, Almulhim A, Makhdoom H, Madani H, Alhakeem R, #> Al-Tawfiq J, Cotten M, Watson S, Kellam P, Zumla A, Memish Z, KSA #> MERSCOV Investigation Team (2013). “Hospital Outbreak of Middle East #> Respiratory Syndrome Coronavirus.” _The New England Journal of #> Medicine_. doi:10.1056/NEJMoa1306742 #> <https://doi.org/10.1056/NEJMoa1306742>. #> #> Assiri A, McGeer A, Perl T, Price C, Al Rabeeah A, Cummings D, #> Alabdullatif Z, Assad M, Almulhim A, Makhdoom H, Madani H, Alhakeem R, #> Al-Tawfiq J, Cotten M, Watson S, Kellam P, Zumla A, Memish Z, KSA #> MERSCOV Investigation Team (2013). “Hospital Outbreak of Middle East #> Respiratory Syndrome Coronavirus.” _The New England Journal of #> Medicine_. doi:10.1056/NEJMoa1306742 #> <https://doi.org/10.1056/NEJMoa1306742>. #> #> Assiri A, McGeer A, Perl T, Price C, Al Rabeeah A, Cummings D, #> Alabdullatif Z, Assad M, Almulhim A, Makhdoom H, Madani H, Alhakeem R, #> Al-Tawfiq J, Cotten M, Watson S, Kellam P, Zumla A, Memish Z, KSA #> MERSCOV Investigation Team (2013). “Hospital Outbreak of Middle East #> Respiratory Syndrome Coronavirus.” _The New England Journal of #> Medicine_. doi:10.1056/NEJMoa1306742 #> <https://doi.org/10.1056/NEJMoa1306742>. #> #> Assiri A, McGeer A, Perl T, Price C, Al Rabeeah A, Cummings D, #> Alabdullatif Z, Assad M, Almulhim A, Makhdoom H, Madani H, Alhakeem R, #> Al-Tawfiq J, Cotten M, Watson S, Kellam P, Zumla A, Memish Z, KSA #> MERSCOV Investigation Team (2013). “Hospital Outbreak of Middle East #> Respiratory Syndrome Coronavirus.” _The New England Journal of #> Medicine_. doi:10.1056/NEJMoa1306742 #> <https://doi.org/10.1056/NEJMoa1306742>. #> #> Mizumoto K, Endo A, Chowell G, Miyamatsu Y, Saitoh M, Nishiura H #> (2015). “Real-time characterization of risks of death associated with #> the Middle East respiratory syndrome (MERS) in the Republic of Korea, #> 2015.” _BMC Medicine_. doi:10.1186/s12916-015-0468-3 #> <https://doi.org/10.1186/s12916-015-0468-3>. #> #> Cowling B, Park M, Fang V, Wu P, Leung G, Wu J (2015). “Preliminary #> epidemiological assessment of MERS-CoV outbreak in South Korea, May to #> June 2015.” _Eurosurveillance_. #> doi:10.2807/1560-7917.es2015.20.25.21163 #> <https://doi.org/10.2807/1560-7917.es2015.20.25.21163>. #> #> Cowling B, Park M, Fang V, Wu P, Leung G, Wu J (2015). “Preliminary #> epidemiological assessment of MERS-CoV outbreak in South Korea, May to #> June 2016.” _Eurosurveillance_. #> doi:10.2807/1560-7917.es2015.20.25.21163 #> <https://doi.org/10.2807/1560-7917.es2015.20.25.21163>. #> #> Men K, Wang X, Yihao L, Zhang G, Hu J, Gao Y, Han H (2020). “Estimate #> the incubation period of coronavirus 2019 (COVID-19).” _medRxiv_. #> doi:10.1101/2020.02.24.20027474 #> <https://doi.org/10.1101/2020.02.24.20027474>. #> #> Rai B, Shukla A, Dwivedi L (2022). “Incubation period for COVID-19: a #> systematic review and meta-analysis.” _Zeitschrift fur #> Gesundheitswissenschaften_. doi:10.1007/s10389-021-01478-1 #> <https://doi.org/10.1007/s10389-021-01478-1>. #> #> Alene M, Yismaw L, Assemie M, Ketema D, Gietaneh W, Birhan T (2021). #> “Serial interval and incubation period of COVID-19: a systematic review #> and meta-analysis.” _BMC Infectious Diseases_. #> doi:10.1186/s12879-021-05950-x #> <https://doi.org/10.1186/s12879-021-05950-x>. #> #> Alene M, Yismaw L, Assemie M, Ketema D, Gietaneh W, Birhan T (2021). #> “Serial interval and incubation period of COVID-19: a systematic review #> and meta-analysis.” _BMC Infectious Diseases_. #> doi:10.1186/s12879-021-05950-x #> <https://doi.org/10.1186/s12879-021-05950-x>. #> #> Nishiura H, Linton N, Akhmetzhanov A (2020). “Serial interval of novel #> coronavirus (COVID-19) infections.” _International Journal of #> Infectious Diseases_. doi:10.1016/j.ijid.2020.02.060 #> <https://doi.org/10.1016/j.ijid.2020.02.060>. #> #> Nishiura H, Linton N, Akhmetzhanov A (2020). “Serial interval of novel #> coronavirus (COVID-19) infections.” _International Journal of #> Infectious Diseases_. doi:10.1016/j.ijid.2020.02.060 #> <https://doi.org/10.1016/j.ijid.2020.02.060>. #> #> Yang L, Dai J, Zhao J, Wang Y, Deng P, Wang J (2020). “Estimation of #> incubation period and serial interval of COVID-19: analysis of 178 #> cases and 131 transmission chains in Hubei province, China.” #> _Epidemiology and Infection_. doi:10.1017/S0950268820001338 #> <https://doi.org/10.1017/S0950268820001338>. #> #> Yang L, Dai J, Zhao J, Wang Y, Deng P, Wang J (2020). “Estimation of #> incubation period and serial interval of COVID-19: analysis of 178 #> cases and 131 transmission chains in Hubei province, China.” #> _Epidemiology and Infection_. doi:10.1017/S0950268820001338 #> <https://doi.org/10.1017/S0950268820001338>. #> #> Elias C, Sekri A, Leblanc P, Cucherat M, Vanhems P (2021). “The #> incubation period of COVID-19: A meta-analysis.” _International Journal #> of Infectious Diseases_. doi:10.1016/j.ijid.2021.01.069 #> <https://doi.org/10.1016/j.ijid.2021.01.069>. #> #> Bui L, Nguyen H, Levine H, Nguyen H, Nguyen T, Nguyen T, Nguyen T, Do #> T, Pham N, Bui M (2020). “Estimation of the incubation period of #> COVID-19 in Vietnam.” _PLoS One_. doi:10.1371/journal.pone.0243889 #> <https://doi.org/10.1371/journal.pone.0243889>. #> #> McAloon C, Collins Á, Hunt K, Barber A, Byrne A, Butler F, Casey M, #> Griffin J, Lane E, McEvoy D, Wall P, Green M, O'Grady L, More S (2020). #> “Incubation period of COVID-19: a rapid systematic review and #> meta-analysis of observational research.” _BMJ Open_. #> doi:10.1136/bmjopen-2020-039652 #> <https://doi.org/10.1136/bmjopen-2020-039652>. #> #> McAloon C, Collins Á, Hunt K, Barber A, Byrne A, Butler F, Casey M, #> Griffin J, Lane E, McEvoy D, Wall P, Green M, O'Grady L, More S (2020). #> “Incubation period of COVID-19: a rapid systematic review and #> meta-analysis of observational research.” _BMJ Open_. #> doi:10.1136/bmjopen-2020-039652 #> <https://doi.org/10.1136/bmjopen-2020-039652>. #> #> Linton N, Kobayashi T, Yang Y, Hayashi K, Akhmetzhanov A, Jung S, Yuan #> B, Kinoshita R, Nishiura H (2020). “Incubation Period and Other #> Epidemiological Characteristics of 2019 Novel Coronavirus Infections #> with Right Truncation: A Statistical Analysis of Publicly Available #> Case Data.” _Journal of Clinical Medicine_. doi:10.3390/jcm9020538 #> <https://doi.org/10.3390/jcm9020538>. #> #> Linton N, Kobayashi T, Yang Y, Hayashi K, Akhmetzhanov A, Jung S, Yuan #> B, Kinoshita R, Nishiura H (2020). “Incubation Period and Other #> Epidemiological Characteristics of 2019 Novel Coronavirus Infections #> with Right Truncation: A Statistical Analysis of Publicly Available #> Case Data.” _Journal of Clinical Medicine_. doi:10.3390/jcm9020538 #> <https://doi.org/10.3390/jcm9020538>. #> #> Linton N, Kobayashi T, Yang Y, Hayashi K, Akhmetzhanov A, Jung S, Yuan #> B, Kinoshita R, Nishiura H (2020). “Incubation Period and Other #> Epidemiological Characteristics of 2019 Novel Coronavirus Infections #> with Right Truncation: A Statistical Analysis of Publicly Available #> Case Data.” _Journal of Clinical Medicine_. doi:10.3390/jcm9020538 #> <https://doi.org/10.3390/jcm9020538>. #> #> Linton N, Kobayashi T, Yang Y, Hayashi K, Akhmetzhanov A, Jung S, Yuan #> B, Kinoshita R, Nishiura H (2020). “Incubation Period and Other #> Epidemiological Characteristics of 2019 Novel Coronavirus Infections #> with Right Truncation: A Statistical Analysis of Publicly Available #> Case Data.” _Journal of Clinical Medicine_. doi:10.3390/jcm9020538 #> <https://doi.org/10.3390/jcm9020538>. #> #> Linton N, Kobayashi T, Yang Y, Hayashi K, Akhmetzhanov A, Jung S, Yuan #> B, Kinoshita R, Nishiura H (2020). “Incubation Period and Other #> Epidemiological Characteristics of 2019 Novel Coronavirus Infections #> with Right Truncation: A Statistical Analysis of Publicly Available #> Case Data.” _Journal of Clinical Medicine_. doi:10.3390/jcm9020538 #> <https://doi.org/10.3390/jcm9020538>. #> #> Linton N, Kobayashi T, Yang Y, Hayashi K, Akhmetzhanov A, Jung S, Yuan #> B, Kinoshita R, Nishiura H (2020). “Incubation Period and Other #> Epidemiological Characteristics of 2019 Novel Coronavirus Infections #> with Right Truncation: A Statistical Analysis of Publicly Available #> Case Data.” _Journal of Clinical Medicine_. doi:10.3390/jcm9020538 #> <https://doi.org/10.3390/jcm9020538>. #> #> Linton N, Kobayashi T, Yang Y, Hayashi K, Akhmetzhanov A, Jung S, Yuan #> B, Kinoshita R, Nishiura H (2020). “Incubation Period and Other #> Epidemiological Characteristics of 2019 Novel Coronavirus Infections #> with Right Truncation: A Statistical Analysis of Publicly Available #> Case Data.” _Journal of Clinical Medicine_. doi:10.3390/jcm9020538 #> <https://doi.org/10.3390/jcm9020538>. #> #> Linton N, Kobayashi T, Yang Y, Hayashi K, Akhmetzhanov A, Jung S, Yuan #> B, Kinoshita R, Nishiura H (2020). “Incubation Period and Other #> Epidemiological Characteristics of 2019 Novel Coronavirus Infections #> with Right Truncation: A Statistical Analysis of Publicly Available #> Case Data.” _Journal of Clinical Medicine_. doi:10.3390/jcm9020538 #> <https://doi.org/10.3390/jcm9020538>. #> #> Linton N, Kobayashi T, Yang Y, Hayashi K, Akhmetzhanov A, Jung S, Yuan #> B, Kinoshita R, Nishiura H (2020). “Incubation Period and Other #> Epidemiological Characteristics of 2019 Novel Coronavirus Infections #> with Right Truncation: A Statistical Analysis of Publicly Available #> Case Data.” _Journal of Clinical Medicine_. doi:10.3390/jcm9020538 #> <https://doi.org/10.3390/jcm9020538>. #> #> Linton N, Kobayashi T, Yang Y, Hayashi K, Akhmetzhanov A, Jung S, Yuan #> B, Kinoshita R, Nishiura H (2020). “Incubation Period and Other #> Epidemiological Characteristics of 2019 Novel Coronavirus Infections #> with Right Truncation: A Statistical Analysis of Publicly Available #> Case Data.” _Journal of Clinical Medicine_. doi:10.3390/jcm9020538 #> <https://doi.org/10.3390/jcm9020538>. #> #> Linton N, Kobayashi T, Yang Y, Hayashi K, Akhmetzhanov A, Jung S, Yuan #> B, Kinoshita R, Nishiura H (2020). “Incubation Period and Other #> Epidemiological Characteristics of 2019 Novel Coronavirus Infections #> with Right Truncation: A Statistical Analysis of Publicly Available #> Case Data.” _Journal of Clinical Medicine_. doi:10.3390/jcm9020538 #> <https://doi.org/10.3390/jcm9020538>. #> #> Lauer S, Grantz K, Bi Q, Jones F, Zheng Q, Meredith H, Azman A, Reich #> N, Lessler J (2020). “The Incubation Period of Coronavirus Disease 2019 #> (COVID-19) From Publicly Reported Confirmed Cases: Estimation and #> Application.” _Annals of Internal Medicine_. doi:10.7326/M20-0504 #> <https://doi.org/10.7326/M20-0504>. #> #> Lauer S, Grantz K, Bi Q, Jones F, Zheng Q, Meredith H, Azman A, Reich #> N, Lessler J (2020). “The Incubation Period of Coronavirus Disease 2019 #> (COVID-19) From Publicly Reported Confirmed Cases: Estimation and #> Application.” _Annals of Internal Medicine_. doi:10.7326/M20-0504 #> <https://doi.org/10.7326/M20-0504>. #> #> Lauer S, Grantz K, Bi Q, Jones F, Zheng Q, Meredith H, Azman A, Reich #> N, Lessler J (2020). “The Incubation Period of Coronavirus Disease 2019 #> (COVID-19) From Publicly Reported Confirmed Cases: Estimation and #> Application.” _Annals of Internal Medicine_. doi:10.7326/M20-0504 #> <https://doi.org/10.7326/M20-0504>. #> #> Lauer S, Grantz K, Bi Q, Jones F, Zheng Q, Meredith H, Azman A, Reich #> N, Lessler J (2020). “The Incubation Period of Coronavirus Disease 2019 #> (COVID-19) From Publicly Reported Confirmed Cases: Estimation and #> Application.” _Annals of Internal Medicine_. doi:10.7326/M20-0504 #> <https://doi.org/10.7326/M20-0504>. #> #> Guo Z, Zhao S, Sun S, He D, Chong K, Yeoh E (2022). “Estimation of the #> serial interval of monkeypox during the early outbreak in 2022.” #> _Journal of Medical Virology_. doi:10.1002/jmv.28248 #> <https://doi.org/10.1002/jmv.28248>. #> #> Wang S, Zhang F, Yuan Z, Xu M, Wang Z, Gao C, Guo R, Du Z (2022). #> “Serial intervals and incubation periods of the monkeypox virus #> clades.” _Journal of Travel Medicine_. doi:10.1093/jtm/taac105 #> <https://doi.org/10.1093/jtm/taac105>. #> #> Wang S, Zhang F, Yuan Z, Xu M, Wang Z, Gao C, Guo R, Du Z (2022). #> “Serial intervals and incubation periods of the monkeypox virus #> clades.” _Journal of Travel Medicine_. doi:10.1093/jtm/taac105 #> <https://doi.org/10.1093/jtm/taac105>. #> #> Wang S, Zhang F, Yuan Z, Xu M, Wang Z, Gao C, Guo R, Du Z (2022). #> “Serial intervals and incubation periods of the monkeypox virus #> clades.” _Journal of Travel Medicine_. doi:10.1093/jtm/taac105 #> <https://doi.org/10.1093/jtm/taac105>. #> #> Wang S, Zhang F, Yuan Z, Xu M, Wang Z, Gao C, Guo R, Du Z (2022). #> “Serial intervals and incubation periods of the monkeypox virus #> clades.” _Journal of Travel Medicine_. doi:10.1093/jtm/taac105 #> <https://doi.org/10.1093/jtm/taac105>. #> #> Wang S, Zhang F, Yuan Z, Xu M, Wang Z, Gao C, Guo R, Du Z (2022). #> “Serial intervals and incubation periods of the monkeypox virus #> clades.” _Journal of Travel Medicine_. doi:10.1093/jtm/taac105 #> <https://doi.org/10.1093/jtm/taac105>. #> #> Wei F, Peng Z, Jin Z, Wang J, Xu X, Zhang X, Xu J, Ren Z, Bai Y, Wang #> X, Lu B, Wang Z, Xu J, Huang S (2022). “Study and prediction of the #> 2022 global monkeypox epidemic.” _Journal of Biosafety and #> Biosecurity_. doi:10.1016/j.jobb.2022.12.001 #> <https://doi.org/10.1016/j.jobb.2022.12.001>. #> #> Wei F, Peng Z, Jin Z, Wang J, Xu X, Zhang X, Xu J, Ren Z, Bai Y, Wang #> X, Lu B, Wang Z, Xu J, Huang S (2022). “Study and prediction of the #> 2022 global monkeypox epidemic.” _Journal of Biosafety and #> Biosecurity_. doi:10.1016/j.jobb.2022.12.001 #> <https://doi.org/10.1016/j.jobb.2022.12.001>. #> #> Wei F, Peng Z, Jin Z, Wang J, Xu X, Zhang X, Xu J, Ren Z, Bai Y, Wang #> X, Lu B, Wang Z, Xu J, Huang S (2022). “Study and prediction of the #> 2022 global monkeypox epidemic.” _Journal of Biosafety and #> Biosecurity_. doi:10.1016/j.jobb.2022.12.001 #> <https://doi.org/10.1016/j.jobb.2022.12.001>. #> #> Salje H, Cauchemez S, Alera M, Rodriguez-Barraquer I, Thaisomboonsuk B, #> Srikiatkhachorn A, Lago C, Villa D, Klungthong C, Tac-An I, Fernandez #> S, Velasco J, Roque Jr V, Nisalak A, Macareo L, Levy J, Cummings D, #> Yoon I (2015). “Reconstruction of 60 Years of Chikungunya Epidemiology #> in the Philippines Demonstrates Episodic and Focal Transmission.” _The #> Journal of Infectious Diseases_. doi:10.1093/infdis/jiv470 #> <https://doi.org/10.1093/infdis/jiv470>. #> #> Guzzetta G, Vairo F, Mammone A, Lanini S, Poletti P, Manica M, Rosa R, #> Caputo B, Solimini A, della Torre A, Scognamiglio P, Zumla A, Ippolito #> G, Merler S (2020). “Spatial modes for transmission of chikungunya #> virus during a large chikungunya outbreak in Italy: a modeling #> analysis.” _BMC Medicine_. doi:10.1186/s12916-020-01674-y #> <https://doi.org/10.1186/s12916-020-01674-y>. #> #> de Souza W, de Lima S, Mello L, Candido D, Buss L, Whittaker C, Claro #> I, Chandradeva N, Granja F, de Jesus R, Lemos P, Toledo-Teixeira D, #> Barbosa P, Firmino A, Amorim M, Duarte L, Pessoa Jr I, Forato J, #> Vasconcelos I, Maximo A, Araújo E, Mello L, Sabino E, Proença-Módena J, #> Faria N, Weaver S (2023). “Spatiotemporal dynamics and recurrence of #> chikungunya virus in Brazil: an epidemiological study.” _The Lancet #> Microbe_. doi:10.1016/S2666-5247(23)00033-2 #> <https://doi.org/10.1016/S2666-5247%2823%2900033-2>."},{"path":"https://epiverse-trace.github.io/epiparameter/reference/get_parameters.epiparameter.html","id":null,"dir":"Reference","previous_headings":"","what":"Get parameters from an <epiparameter> object — get_parameters.epiparameter","title":"Get parameters from an <epiparameter> object — get_parameters.epiparameter","text":"Extract parameters distribution stored <epiparameter> object.","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/get_parameters.epiparameter.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Get parameters from an <epiparameter> object — get_parameters.epiparameter","text":"","code":"# S3 method for class 'epiparameter' get_parameters(x, ...) # S3 method for class 'multi_epiparameter' get_parameters(x, ...)"},{"path":"https://epiverse-trace.github.io/epiparameter/reference/get_parameters.epiparameter.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Get parameters from an <epiparameter> object — get_parameters.epiparameter","text":"x <epiparameter> object. ... dots used, extra arguments supplied cause warning.","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/get_parameters.epiparameter.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Get parameters from an <epiparameter> object — get_parameters.epiparameter","text":"named vector parameters NA <epiparameter> object unparameterised.","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/get_parameters.epiparameter.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Get parameters from an <epiparameter> object — get_parameters.epiparameter","text":"<epiparameter> object can unparameterised lacks probability distribution parameters probability distribution, case get_parameters.epiparameter() method return NA.","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/get_parameters.epiparameter.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Get parameters from an <epiparameter> object — get_parameters.epiparameter","text":"","code":"ep <- epiparameter_db( disease = \"COVID-19\", epi_name = \"serial interval\", single_epiparameter = TRUE ) #> Using Nishiura H, Linton N, Akhmetzhanov A (2020). “Serial interval of novel #> coronavirus (COVID-19) infections.” _International Journal of #> Infectious Diseases_. doi:10.1016/j.ijid.2020.02.060 #> <https://doi.org/10.1016/j.ijid.2020.02.060>.. #> To retrieve the citation use the 'get_citation' function get_parameters(ep) #> meanlog sdlog #> 1.3862617 0.5679803"},{"path":"https://epiverse-trace.github.io/epiparameter/reference/get_parameters.html","id":null,"dir":"Reference","previous_headings":"","what":"Get parameters from an object — get_parameters","title":"Get parameters from an object — get_parameters","text":"Extract parameters stored R object.","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/get_parameters.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Get parameters from an object — get_parameters","text":"","code":"get_parameters(x, ...)"},{"path":"https://epiverse-trace.github.io/epiparameter/reference/get_parameters.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Get parameters from an object — get_parameters","text":"x object used select method. ... dots Extra arguments passed method.","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/is_continuous.html","id":null,"dir":"Reference","previous_headings":"","what":"Check if distribution in <epiparameter> is continuous — is_continuous","title":"Check if distribution in <epiparameter> is continuous — is_continuous","text":"Check distribution <epiparameter> continuous","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/is_continuous.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Check if distribution in <epiparameter> is continuous — is_continuous","text":"","code":"is_continuous(x)"},{"path":"https://epiverse-trace.github.io/epiparameter/reference/is_continuous.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Check if distribution in <epiparameter> is continuous — is_continuous","text":"x <epiparameter> object.","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/is_continuous.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Check if distribution in <epiparameter> is continuous — is_continuous","text":"boolean logical.","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/is_continuous.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Check if distribution in <epiparameter> is continuous — is_continuous","text":"<epiparameter> class can hold <distribution> <distcrete> probability distribution objects distributional package distcrete package, respectively. <distribution> objects can continuous discrete distributions (e.g. gamma negative binomial), <distcrete> objects discrete.","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/is_continuous.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Check if distribution in <epiparameter> is continuous — is_continuous","text":"","code":"ep <- epiparameter( disease = \"ebola\", epi_name = \"incubation_period\", prob_distribution = create_prob_distribution( prob_distribution = \"lnorm\", prob_distribution_params = c(meanlog = 1, sdlog = 1) ) ) #> Citation cannot be created as author, year, journal or title is missing is_continuous(ep) #> [1] TRUE is_continuous(discretise(ep)) #> [1] FALSE ep <- epiparameter( disease = \"ebola\", epi_name = \"offspring distribution\", prob_distribution = create_prob_distribution( prob_distribution = \"nbinom\", prob_distribution_params = c(mean = 2, dispersion = 0.5) ) ) #> Citation cannot be created as author, year, journal or title is missing is_continuous(ep) #> [1] FALSE"},{"path":"https://epiverse-trace.github.io/epiparameter/reference/is_epiparameter.html","id":null,"dir":"Reference","previous_headings":"","what":"Check object is an <epiparameter> — is_epiparameter","title":"Check object is an <epiparameter> — is_epiparameter","text":"Check object <epiparameter>","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/is_epiparameter.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Check object is an <epiparameter> — is_epiparameter","text":"","code":"is_epiparameter(x)"},{"path":"https://epiverse-trace.github.io/epiparameter/reference/is_epiparameter.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Check object is an <epiparameter> — is_epiparameter","text":"x R object.","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/is_epiparameter.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Check object is an <epiparameter> — is_epiparameter","text":"boolean logical, TRUE object <epiparameter> FALSE .","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/is_epiparameter.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Check object is an <epiparameter> — is_epiparameter","text":"","code":"ep <- epiparameter( disease = \"ebola\", epi_name = \"serial_interval\", prob_distribution = create_prob_distribution( prob_distribution = \"gamma\", prob_distribution_params = c(shape = 1, scale = 1) ) ) #> Citation cannot be created as author, year, journal or title is missing is_epiparameter(ep) #> [1] TRUE false_ep <- list( disease = \"ebola\", epi_name = \"serial_interval\", prob_distribution = \"gamma\", prob_distribution_params = c(shape = 1, scale = 1) ) is_epiparameter(false_ep) #> [1] FALSE"},{"path":"https://epiverse-trace.github.io/epiparameter/reference/is_epiparameter_df.html","id":null,"dir":"Reference","previous_headings":"","what":"Check if <data.frame> input is from as.data.frame.epiparameter() — is_epiparameter_df","title":"Check if <data.frame> input is from as.data.frame.epiparameter() — is_epiparameter_df","text":"Check <data.frame> input .data.frame.epiparameter()","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/is_epiparameter_df.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Check if <data.frame> input is from as.data.frame.epiparameter() — is_epiparameter_df","text":"","code":"is_epiparameter_df(x)"},{"path":"https://epiverse-trace.github.io/epiparameter/reference/is_epiparameter_df.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Check if <data.frame> input is from as.data.frame.epiparameter() — is_epiparameter_df","text":"x <data.frame>.","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/is_epiparameter_df.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Check if <data.frame> input is from as.data.frame.epiparameter() — is_epiparameter_df","text":"single logical boolean.","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/is_epiparameter_params.html","id":null,"dir":"Reference","previous_headings":"","what":"Check whether the vector of parameters for the probability distribution are in the set of possible parameters used in the epiparameter package — is_epiparameter_params","title":"Check whether the vector of parameters for the probability distribution are in the set of possible parameters used in the epiparameter package — is_epiparameter_params","text":"Check whether vector parameters probability distribution set possible parameters used epiparameter package","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/is_epiparameter_params.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Check whether the vector of parameters for the probability distribution are in the set of possible parameters used in the epiparameter package — is_epiparameter_params","text":"","code":"is_epiparameter_params(prob_distribution, prob_distribution_params)"},{"path":"https://epiverse-trace.github.io/epiparameter/reference/is_epiparameter_params.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Check whether the vector of parameters for the probability distribution are in the set of possible parameters used in the epiparameter package — is_epiparameter_params","text":"prob_distribution character string specifying probability distribution. match R naming convention probability distributions (e.g. lognormal lnorm, negative binomial nbinom, geometric geom). prob_distribution_params named vector probability distribution parameters.","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/is_epiparameter_params.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Check whether the vector of parameters for the probability distribution are in the set of possible parameters used in the epiparameter package — is_epiparameter_params","text":"boolean logical.","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/is_epiparameter_params.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Check whether the vector of parameters for the probability distribution are in the set of possible parameters used in the epiparameter package — is_epiparameter_params","text":"check valid parameters independent whether distribution truncated discretised.","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/is_epiparameter_params.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Check whether the vector of parameters for the probability distribution are in the set of possible parameters used in the epiparameter package — is_epiparameter_params","text":"","code":"is_epiparameter_params( prob_distribution = \"gamma\", prob_distribution_params = c(shape = 2, scale = 1) ) #> [1] TRUE"},{"path":"https://epiverse-trace.github.io/epiparameter/reference/is_parameterised.html","id":null,"dir":"Reference","previous_headings":"","what":"Check if <epiparameter> or list of <epiparameter> objects contains a distribution and distribution parameters — is_parameterised","title":"Check if <epiparameter> or list of <epiparameter> objects contains a distribution and distribution parameters — is_parameterised","text":"Check <epiparameter> list <epiparameter> objects contains distribution distribution parameters","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/is_parameterised.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Check if <epiparameter> or list of <epiparameter> objects contains a distribution and distribution parameters — is_parameterised","text":"","code":"is_parameterised(x, ...) is_parameterized(x, ...) # S3 method for class 'epiparameter' is_parameterised(x, ...) # S3 method for class 'multi_epiparameter' is_parameterised(x, ...)"},{"path":"https://epiverse-trace.github.io/epiparameter/reference/is_parameterised.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Check if <epiparameter> or list of <epiparameter> objects contains a distribution and distribution parameters — is_parameterised","text":"x <epiparameter> list <epiparameter> objects. ... dots Extra arguments passed method.","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/is_parameterised.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Check if <epiparameter> or list of <epiparameter> objects contains a distribution and distribution parameters — is_parameterised","text":"single boolean logical <epiparameter> vector logicals equal length list <epiparameter> objects input. <epiparameter> object missing either probability distribution parameters probability distribution returns FALSE, otherwise returns TRUE.","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/is_parameterised.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Check if <epiparameter> or list of <epiparameter> objects contains a distribution and distribution parameters — is_parameterised","text":"","code":"# parameterised <epiparameter> ep <- epiparameter( disease = \"ebola\", epi_name = \"incubation\", prob_distribution = create_prob_distribution( prob_distribution = \"gamma\", prob_distribution_params = c(shape = 1, scale = 1) ) ) #> Citation cannot be created as author, year, journal or title is missing is_parameterised(ep) #> [1] TRUE # unparameterised <epiparameter> ep <- epiparameter( disease = \"ebola\", epi_name = \"incubation\" ) #> Citation cannot be created as author, year, journal or title is missing #> No adequate summary statistics available to calculate the parameters of the NA distribution #> Unparameterised <epiparameter> object is_parameterised(ep) #> [1] FALSE # list of <epiparameter> db <- epiparameter_db() #> Returning 125 results that match the criteria (100 are parameterised). #> Use subset to filter by entry variables or single_epiparameter to return a single entry. #> To retrieve the citation for each use the 'get_citation' function is_parameterised(db) #> [1] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE #> [13] TRUE TRUE TRUE TRUE TRUE TRUE FALSE TRUE TRUE FALSE TRUE TRUE #> [25] TRUE TRUE FALSE FALSE FALSE FALSE TRUE TRUE TRUE TRUE TRUE TRUE #> [37] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE #> [49] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE #> [61] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE #> [73] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE FALSE FALSE FALSE TRUE #> [85] FALSE TRUE FALSE FALSE FALSE FALSE TRUE TRUE TRUE TRUE FALSE TRUE #> [97] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE #> [109] TRUE TRUE TRUE TRUE TRUE TRUE FALSE FALSE FALSE FALSE FALSE FALSE #> [121] FALSE FALSE FALSE TRUE FALSE"},{"path":"https://epiverse-trace.github.io/epiparameter/reference/is_truncated.html","id":null,"dir":"Reference","previous_headings":"","what":"Check if distribution in <epiparameter> is truncated — is_truncated","title":"Check if distribution in <epiparameter> is truncated — is_truncated","text":"Check distribution <epiparameter> truncated","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/is_truncated.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Check if distribution in <epiparameter> is truncated — is_truncated","text":"","code":"is_truncated(x)"},{"path":"https://epiverse-trace.github.io/epiparameter/reference/is_truncated.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Check if distribution in <epiparameter> is truncated — is_truncated","text":"x <epiparameter> object.","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/is_truncated.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Check if distribution in <epiparameter> is truncated — is_truncated","text":"boolean logical.","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/is_truncated.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Check if distribution in <epiparameter> is truncated — is_truncated","text":"<epiparameter> class can hold probability distribution objects {distributional} package {distcrete} package, however, distribution objects {distributional} can truncated. <epiparameter> object <distcrete> object is_truncated return FALSE default.","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/is_truncated.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Check if distribution in <epiparameter> is truncated — is_truncated","text":"","code":"ep <- epiparameter( disease = \"ebola\", epi_name = \"incubation_period\", prob_distribution = create_prob_distribution( prob_distribution = \"lnorm\", prob_distribution_params = c(meanlog = 1, sdlog = 1) ) ) #> Citation cannot be created as author, year, journal or title is missing is_truncated(ep) #> [1] FALSE ep <- epiparameter( disease = \"ebola\", epi_name = \"incubation_period\", prob_distribution = create_prob_distribution( prob_distribution = \"lnorm\", prob_distribution_params = c(meanlog = 1, sdlog = 1), truncation = 10 ) ) #> Citation cannot be created as author, year, journal or title is missing is_truncated(ep) #> [1] TRUE"},{"path":"https://epiverse-trace.github.io/epiparameter/reference/lines.epiparameter.html","id":null,"dir":"Reference","previous_headings":"","what":"lines() method for <epiparameter> class — lines.epiparameter","title":"lines() method for <epiparameter> class — lines.epiparameter","text":"lines() method <epiparameter> class","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/lines.epiparameter.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"lines() method for <epiparameter> class — lines.epiparameter","text":"","code":"# S3 method for class 'epiparameter' lines(x, cumulative = FALSE, ...)"},{"path":"https://epiverse-trace.github.io/epiparameter/reference/lines.epiparameter.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"lines() method for <epiparameter> class — lines.epiparameter","text":"x <epiparameter> object. cumulative boolean logical, default FALSE. cumulative = TRUE plots cumulative distribution function (CDF). ... arguments passed methods.","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/lines.epiparameter.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"lines() method for <epiparameter> class — lines.epiparameter","text":"","code":"ebola_si <- epiparameter_db(disease = \"Ebola\", epi_name = \"serial\") #> Returning 4 results that match the criteria (4 are parameterised). #> Use subset to filter by entry variables or single_epiparameter to return a single entry. #> To retrieve the citation for each use the 'get_citation' function plot(ebola_si[[1]]) lines(ebola_si[[2]])"},{"path":"https://epiverse-trace.github.io/epiparameter/reference/mean.epiparameter.html","id":null,"dir":"Reference","previous_headings":"","what":"Mean method for <epiparameter> class — mean.epiparameter","title":"Mean method for <epiparameter> class — mean.epiparameter","text":"Mean method <epiparameter> class","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/mean.epiparameter.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Mean method for <epiparameter> class — mean.epiparameter","text":"","code":"# S3 method for class 'epiparameter' mean(x, ...)"},{"path":"https://epiverse-trace.github.io/epiparameter/reference/mean.epiparameter.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Mean method for <epiparameter> class — mean.epiparameter","text":"x <epiparameter> object. ... dots used, extra arguments supplied cause warning.","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/mean.epiparameter.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Mean method for <epiparameter> class — mean.epiparameter","text":"numeric mean distribution NA.","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/mean.epiparameter.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Mean method for <epiparameter> class — mean.epiparameter","text":"","code":"ep <- epiparameter_db( disease = \"COVID-19\", epi_name = \"incubation period\", single_epiparameter = TRUE ) #> Using Linton N, Kobayashi T, Yang Y, Hayashi K, Akhmetzhanov A, Jung S, Yuan #> B, Kinoshita R, Nishiura H (2020). “Incubation Period and Other #> Epidemiological Characteristics of 2019 Novel Coronavirus Infections #> with Right Truncation: A Statistical Analysis of Publicly Available #> Case Data.” _Journal of Clinical Medicine_. doi:10.3390/jcm9020538 #> <https://doi.org/10.3390/jcm9020538>.. #> To retrieve the citation use the 'get_citation' function mean(ep) #> [1] 5.6"},{"path":"https://epiverse-trace.github.io/epiparameter/reference/new_epiparameter.html","id":null,"dir":"Reference","previous_headings":"","what":"Constructor for <epiparameter> class — new_epiparameter","title":"Constructor for <epiparameter> class — new_epiparameter","text":"Create <epiparameter> object. constructor search whether parameters probability distribution supplied look see whether can inferred/extracted/ converted summary statistics provided. also convert probability distribution (prob_dist) parameters (prob_dist_params) S3 class, either distribution object {distributional} discretise = FALSE, distcrete object {distcrete} discretise = TRUE.","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/new_epiparameter.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Constructor for <epiparameter> class — new_epiparameter","text":"","code":"new_epiparameter( disease = character(), pathogen = character(), epi_name = character(), prob_distribution = list(), uncertainty = list(), summary_stats = list(), citation = character(), metadata = list(), method_assess = list(), notes = character(), auto_calc_params = logical(), ... )"},{"path":"https://epiverse-trace.github.io/epiparameter/reference/new_epiparameter.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Constructor for <epiparameter> class — new_epiparameter","text":"disease character string name infectious disease. pathogen character string name causative agent disease, NA known. epi_name character string name epidemiological parameter type. prob_distribution S3 class containing probability distribution character string parameters probability distribution unknown name distribution known, NA distribution name parameters unknown. Use create_prob_distribution() create prob_distribution. uncertainty list named vectors uncertainty around probability distribution parameters. uncertainty around parameter estimates unknown use create_uncertainty() (argument default) create list correct names missing values. summary_stats list summary statistics, use create_summary_stats() create list. list can include summary statistics inferred distribution mean standard deviation, quantiles distribution, information data used fit distribution lower upper range. summary statistics can also include uncertainty around metrics confidence interval around mean standard deviation. citation <bibentry> citation source data paper inferred distribution parameters, use create_citation() create citation. metadata list metadata, can include: units, sample size, transmission mode disease (e.g. vector-borne directly transmitted), etc. assumed disease vector-borne distribution intrinsic (e.g. extrinsic delay distribution extrinsic incubation period) unless transmission_mode = \"vector_borne\" contained metadata. Use create_metadata() create metadata. method_assess list methodological aspects used fitting distribution, use create_method_assess() create method assessment. notes character string additional information data, inference method disease. auto_calc_params boolean logical determining whether try calculate probability distribution parameters summary statistics distribution parameters provided. Default TRUE. case sufficient summary statistics provided parameter(s) distribution , .calc_dist_params() function called calculate parameters add epiparameter object created. ... dots Extra arguments passed internal functions. commonly used pass arguments distcrete::distcrete() construct discretised distribution S3 object. see arguments can adjusted discretised distributions see distcrete::distcrete().","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/new_epiparameter.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Constructor for <epiparameter> class — new_epiparameter","text":"<epiparameter> object.","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/parameter_tbl.html","id":null,"dir":"Reference","previous_headings":"","what":"Table of epidemiological distributions — parameter_tbl","title":"Table of epidemiological distributions — parameter_tbl","text":"function subsets epidemiological parameter library return chosen epidemiological distribution. results returned data frame containing disease, epidemiological distribution, probability distribution, author study, year publication.","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/parameter_tbl.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Table of epidemiological distributions — parameter_tbl","text":"","code":"parameter_tbl( multi_epiparameter, disease = \"all\", pathogen = \"all\", epi_name = \"all\" )"},{"path":"https://epiverse-trace.github.io/epiparameter/reference/parameter_tbl.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Table of epidemiological distributions — parameter_tbl","text":"multi_epiparameter Either <epiparameter> object list <epiparameter> objects. disease character string name infectious disease. pathogen character string name causative agent disease, NA known. epi_name character string name epidemiological parameter type.","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/parameter_tbl.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Table of epidemiological distributions — parameter_tbl","text":"<parameter_tbl> object subclass <data.frame>.","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/parameter_tbl.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Table of epidemiological distributions — parameter_tbl","text":"Joshua W. Lambert, Adam Kucharski","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/parameter_tbl.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Table of epidemiological distributions — parameter_tbl","text":"","code":"epiparameter_list <- epiparameter_db(disease = \"COVID-19\") #> Returning 27 results that match the criteria (22 are parameterised). #> Use subset to filter by entry variables or single_epiparameter to return a single entry. #> To retrieve the citation for each use the 'get_citation' function parameter_tbl(multi_epiparameter = epiparameter_list) #> # Parameter table: #> # A data frame: 27 × 7 #> disease pathogen epi_name prob_distribution author year sample_size #> <chr> <chr> <chr> <chr> <chr> <dbl> <dbl> #> 1 COVID-19 SARS-CoV-2 incubation pe… NA Men e… 2020 59 #> 2 COVID-19 SARS-CoV-2 incubation pe… NA Rai e… 2022 6241 #> 3 COVID-19 SARS-CoV-2 incubation pe… NA Alene… 2021 1453 #> 4 COVID-19 SARS-CoV-2 serial interv… NA Alene… 2021 3924 #> 5 COVID-19 SARS-CoV-2 serial interv… lnorm Nishi… 2020 28 #> 6 COVID-19 SARS-CoV-2 serial interv… weibull Nishi… 2020 18 #> 7 COVID-19 SARS-CoV-2 incubation pe… weibull Yang … 2020 178 #> 8 COVID-19 SARS-CoV-2 serial interv… norm Yang … 2020 131 #> 9 COVID-19 SARS-CoV-2 incubation pe… NA Elias… 2021 28675 #> 10 COVID-19 SARS-CoV-2 incubation pe… weibull Bui e… 2020 19 #> # ℹ 17 more rows # example filtering an existing list to incubation periods epiparameter_list <- epiparameter_db(disease = \"COVID-19\") #> Returning 27 results that match the criteria (22 are parameterised). #> Use subset to filter by entry variables or single_epiparameter to return a single entry. #> To retrieve the citation for each use the 'get_citation' function parameter_tbl( multi_epiparameter = epiparameter_list, epi_name = \"incubation period\" ) #> # Parameter table: #> # A data frame: 15 × 7 #> disease pathogen epi_name prob_distribution author year sample_size #> <chr> <chr> <chr> <chr> <chr> <dbl> <dbl> #> 1 COVID-19 SARS-CoV-2 incubation pe… NA Men e… 2020 59 #> 2 COVID-19 SARS-CoV-2 incubation pe… NA Rai e… 2022 6241 #> 3 COVID-19 SARS-CoV-2 incubation pe… NA Alene… 2021 1453 #> 4 COVID-19 SARS-CoV-2 incubation pe… weibull Yang … 2020 178 #> 5 COVID-19 SARS-CoV-2 incubation pe… NA Elias… 2021 28675 #> 6 COVID-19 SARS-CoV-2 incubation pe… weibull Bui e… 2020 19 #> 7 COVID-19 SARS-CoV-2 incubation pe… lnorm McAlo… 2020 1357 #> 8 COVID-19 SARS-CoV-2 incubation pe… lnorm McAlo… 2020 1269 #> 9 COVID-19 SARS-CoV-2 incubation pe… lnorm Linto… 2020 52 #> 10 COVID-19 SARS-CoV-2 incubation pe… lnorm Linto… 2020 158 #> 11 COVID-19 SARS-CoV-2 incubation pe… lnorm Linto… 2020 52 #> 12 COVID-19 SARS-CoV-2 incubation pe… lnorm Lauer… 2020 181 #> 13 COVID-19 SARS-CoV-2 incubation pe… lnorm Lauer… 2020 99 #> 14 COVID-19 SARS-CoV-2 incubation pe… lnorm Lauer… 2020 108 #> 15 COVID-19 SARS-CoV-2 incubation pe… lnorm Lauer… 2020 73"},{"path":"https://epiverse-trace.github.io/epiparameter/reference/plot.epiparameter.html","id":null,"dir":"Reference","previous_headings":"","what":"Plot method for <epiparameter> class — plot.epiparameter","title":"Plot method for <epiparameter> class — plot.epiparameter","text":"Plot <epiparameter> object displaying either probability mass function (PMF), (case discrete distributions) probability density function (PDF) (case continuous distributions), cumulative distribution function (CDF).","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/plot.epiparameter.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plot method for <epiparameter> class — plot.epiparameter","text":"","code":"# S3 method for class 'epiparameter' plot(x, cumulative = FALSE, ...)"},{"path":"https://epiverse-trace.github.io/epiparameter/reference/plot.epiparameter.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plot method for <epiparameter> class — plot.epiparameter","text":"x <epiparameter> object. cumulative boolean logical, default FALSE. cumulative = TRUE plots cumulative distribution function (CDF). ... arguments passed methods.","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/plot.epiparameter.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Plot method for <epiparameter> class — plot.epiparameter","text":"default xlim argument specified distribution plotted day 0 99th quantile distribution. Alternatively, numeric vector length 2 first last day plot x-axis can supplied xlim (...).","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/plot.epiparameter.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Plot method for <epiparameter> class — plot.epiparameter","text":"Joshua W. Lambert","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/plot.epiparameter.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Plot method for <epiparameter> class — plot.epiparameter","text":"","code":"# plot continuous epiparameter ep <- epiparameter( disease = \"ebola\", epi_name = \"incubation_period\", prob_distribution = create_prob_distribution( prob_distribution = \"gamma\", prob_distribution_params = c(shape = 2, scale = 1) ) ) #> Citation cannot be created as author, year, journal or title is missing plot(ep) # plot different day range (x-axis) plot(ep, xlim = c(0, 10)) # plot CDF plot(ep, cumulative = TRUE) # plot discrete epiparameter ep <- discretise(ep) plot(ep)"},{"path":"https://epiverse-trace.github.io/epiparameter/reference/plot.multi_epiparameter.html","id":null,"dir":"Reference","previous_headings":"","what":"plot() method for <multi_epiparameter> class — plot.multi_epiparameter","title":"plot() method for <multi_epiparameter> class — plot.multi_epiparameter","text":"Plots list <epiparameter> objects overlaying distributions.","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/plot.multi_epiparameter.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"plot() method for <multi_epiparameter> class — plot.multi_epiparameter","text":"","code":"# S3 method for class 'multi_epiparameter' plot(x, cumulative = FALSE, ...)"},{"path":"https://epiverse-trace.github.io/epiparameter/reference/plot.multi_epiparameter.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"plot() method for <multi_epiparameter> class — plot.multi_epiparameter","text":"x <multi_epiparameter> object. cumulative boolean logical, default FALSE. cumulative = TRUE plots cumulative distribution function (CDF). ... arguments passed methods.","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/plot.multi_epiparameter.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"plot() method for <multi_epiparameter> class — plot.multi_epiparameter","text":"Unparameterised discrete <epiparameter> objects plotted (see is_parameterised() is_continuous()).","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/plot.multi_epiparameter.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"plot() method for <multi_epiparameter> class — plot.multi_epiparameter","text":"Joshua W. Lambert","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/plot.multi_epiparameter.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"plot() method for <multi_epiparameter> class — plot.multi_epiparameter","text":"","code":"ebola_si <- epiparameter_db(disease = \"Ebola\", epi_name = \"serial\") #> Returning 4 results that match the criteria (4 are parameterised). #> Use subset to filter by entry variables or single_epiparameter to return a single entry. #> To retrieve the citation for each use the 'get_citation' function plot(ebola_si)"},{"path":"https://epiverse-trace.github.io/epiparameter/reference/print.epiparameter.html","id":null,"dir":"Reference","previous_headings":"","what":"Print method for <epiparameter> class — print.epiparameter","title":"Print method for <epiparameter> class — print.epiparameter","text":"Print method <epiparameter> class","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/print.epiparameter.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Print method for <epiparameter> class — print.epiparameter","text":"","code":"# S3 method for class 'epiparameter' print(x, ...)"},{"path":"https://epiverse-trace.github.io/epiparameter/reference/print.epiparameter.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Print method for <epiparameter> class — print.epiparameter","text":"x <epiparameter> object. ... dots Extra arguments passed method.","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/print.epiparameter.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Print method for <epiparameter> class — print.epiparameter","text":"Invisibly returns <epiparameter>. Called side-effects.","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/print.epiparameter.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Print method for <epiparameter> class — print.epiparameter","text":"","code":"epiparameter <- epiparameter( disease = \"ebola\", epi_name = \"incubation_period\", prob_distribution = create_prob_distribution( prob_distribution = \"gamma\", prob_distribution_params = c(shape = 1, scale = 1) ) ) #> Citation cannot be created as author, year, journal or title is missing epiparameter #> Disease: ebola #> Pathogen: NA #> Epi Parameter: incubation period #> Study: (????). “No citation.” #> Distribution: gamma (NA) #> Parameters: #> shape: 1.000 #> scale: 1.000"},{"path":"https://epiverse-trace.github.io/epiparameter/reference/print.multi_epiparameter.html","id":null,"dir":"Reference","previous_headings":"","what":"Print method for <multi_epiparameter> class — print.multi_epiparameter","title":"Print method for <multi_epiparameter> class — print.multi_epiparameter","text":"Print method <multi_epiparameter> class","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/print.multi_epiparameter.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Print method for <multi_epiparameter> class — print.multi_epiparameter","text":"","code":"# S3 method for class 'multi_epiparameter' print(x, ..., n = NULL)"},{"path":"https://epiverse-trace.github.io/epiparameter/reference/print.multi_epiparameter.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Print method for <multi_epiparameter> class — print.multi_epiparameter","text":"x <multi_epiparameter> object. ... dots Extra arguments passed method. n numeric specifying many <epiparameter> objects print. argument passed head() list printing. Default NULL number elements print controlled package options().","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/print.multi_epiparameter.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Print method for <multi_epiparameter> class — print.multi_epiparameter","text":"Invisibly returns <multi_epiparameter>. Called side-effects.","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/print.multi_epiparameter.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Print method for <multi_epiparameter> class — print.multi_epiparameter","text":"","code":"# entire database db <- epiparameter_db() #> Returning 125 results that match the criteria (100 are parameterised). #> Use subset to filter by entry variables or single_epiparameter to return a single entry. #> To retrieve the citation for each use the 'get_citation' function db #> # List of 125 <epiparameter> objects #> Number of diseases: 23 #> ❯ Adenovirus ❯ COVID-19 ❯ Chikungunya ❯ Dengue ❯ Ebola Virus Disease ❯ Hantavirus Pulmonary Syndrome ❯ Human Coronavirus ❯ Influenza ❯ Japanese Encephalitis ❯ MERS ❯ Marburg Virus Disease ❯ Measles ❯ Mpox ❯ Parainfluenza ❯ Pneumonic Plague ❯ RSV ❯ Rhinovirus ❯ Rift Valley Fever ❯ SARS ❯ Smallpox ❯ West Nile Fever ❯ Yellow Fever ❯ Zika Virus Disease #> Number of epi parameters: 13 #> ❯ case fatality risk ❯ generation time ❯ hospitalisation to death ❯ hospitalisation to discharge ❯ incubation period ❯ notification to death ❯ notification to discharge ❯ offspring distribution ❯ onset to death ❯ onset to discharge ❯ onset to hospitalisation ❯ onset to ventilation ❯ serial interval #> [[1]] #> Disease: Adenovirus #> Pathogen: Adenovirus #> Epi Parameter: incubation period #> Study: Lessler J, Reich N, Brookmeyer R, Perl T, Nelson K, Cummings D (2009). #> “Incubation periods of acute respiratory viral infections: a systematic #> review.” _The Lancet Infectious Diseases_. #> doi:10.1016/S1473-3099(09)70069-6 #> <https://doi.org/10.1016/S1473-3099%2809%2970069-6>. #> Distribution: lnorm (days) #> Parameters: #> meanlog: 1.723 #> sdlog: 0.231 #> #> [[2]] #> Disease: Human Coronavirus #> Pathogen: Human_Cov #> Epi Parameter: incubation period #> Study: Lessler J, Reich N, Brookmeyer R, Perl T, Nelson K, Cummings D (2009). #> “Incubation periods of acute respiratory viral infections: a systematic #> review.” _The Lancet Infectious Diseases_. #> doi:10.1016/S1473-3099(09)70069-7 #> <https://doi.org/10.1016/S1473-3099%2809%2970069-7>. #> Distribution: lnorm (days) #> Parameters: #> meanlog: 1.163 #> sdlog: 0.140 #> #> [[3]] #> Disease: SARS #> Pathogen: SARS-Cov-1 #> Epi Parameter: incubation period #> Study: Lessler J, Reich N, Brookmeyer R, Perl T, Nelson K, Cummings D (2009). #> “Incubation periods of acute respiratory viral infections: a systematic #> review.” _The Lancet Infectious Diseases_. #> doi:10.1016/S1473-3099(09)70069-8 #> <https://doi.org/10.1016/S1473-3099%2809%2970069-8>. #> Distribution: lnorm (days) #> Parameters: #> meanlog: 1.386 #> sdlog: 0.593 #> #> # ℹ 122 more elements #> # ℹ Use `print(n = ...)` to see more elements. #> # ℹ Use `parameter_tbl()` to see a summary table of the parameters. #> # ℹ Explore database online at: https://epiverse-trace.github.io/epiparameter/articles/database.html # a single disease db <- epiparameter_db(disease = \"Ebola\") #> Returning 17 results that match the criteria (17 are parameterised). #> Use subset to filter by entry variables or single_epiparameter to return a single entry. #> To retrieve the citation for each use the 'get_citation' function db #> # List of 17 <epiparameter> objects #> Number of diseases: 1 #> ❯ Ebola Virus Disease #> Number of epi parameters: 9 #> ❯ hospitalisation to death ❯ hospitalisation to discharge ❯ incubation period ❯ notification to death ❯ notification to discharge ❯ offspring distribution ❯ onset to death ❯ onset to discharge ❯ serial interval #> [[1]] #> Disease: Ebola Virus Disease #> Pathogen: Ebola Virus #> Epi Parameter: offspring distribution #> Study: Lloyd-Smith J, Schreiber S, Kopp P, Getz W (2005). “Superspreading and #> the effect of individual variation on disease emergence.” _Nature_. #> doi:10.1038/nature04153 <https://doi.org/10.1038/nature04153>. #> Distribution: nbinom (No units) #> Parameters: #> mean: 1.500 #> dispersion: 5.100 #> #> [[2]] #> Disease: Ebola Virus Disease #> Pathogen: Ebola Virus-Zaire Subtype #> Epi Parameter: incubation period #> Study: Eichner M, Dowell S, Firese N (2011). “Incubation period of ebola #> hemorrhagic virus subtype zaire.” _Osong Public Health and Research #> Perspectives_. doi:10.1016/j.phrp.2011.04.001 #> <https://doi.org/10.1016/j.phrp.2011.04.001>. #> Distribution: lnorm (days) #> Parameters: #> meanlog: 2.487 #> sdlog: 0.330 #> #> [[3]] #> Disease: Ebola Virus Disease #> Pathogen: Ebola Virus-Zaire Subtype #> Epi Parameter: onset to death #> Study: The Ebola Outbreak Epidemiology Team, Barry A, Ahuka-Mundeke S, Ali #> Ahmed Y, Allarangar Y, Anoko J, Archer B, Abedi A, Bagaria J, Belizaire #> M, Bhatia S, Bokenge T, Bruni E, Cori A, Dabire E, Diallo A, Diallo B, #> Donnelly C, Dorigatti I, Dorji T, Waeber A, Fall I, Ferguson N, #> FitzJohn R, Tengomo G, Formenty P, Forna A, Fortin A, Garske T, #> Gaythorpe K, Gurry C, Hamblion E, Djingarey M, Haskew C, Hugonnet S, #> Imai N, Impouma B, Kabongo G, Kalenga O, Kibangou E, Lee T, Lukoya C, #> Ly O, Makiala-Mandanda S, Mamba A, Mbala-Kingebeni P, Mboussou F, #> Mlanda T, Makuma V, Morgan O, Mulumba A, Kakoni P, Mukadi-Bamuleka D, #> Muyembe J, Bathé N, Ndumbi Ngamala P, Ngom R, Ngoy G, Nouvellet P, Nsio #> J, Ousman K, Peron E, Polonsky J, Ryan M, Touré A, Towner R, Tshapenda #> G, Van De Weerdt R, Van Kerkhove M, Wendland A, Yao N, Yoti Z, Yuma E, #> Kalambayi Kabamba G, Mwati J, Mbuy G, Lubula L, Mutombo A, Mavila O, #> Lay Y, Kitenge E (2018). “Outbreak of Ebola virus disease in the #> Democratic Republic of the Congo, April–May, 2018: an epidemiological #> study.” _The Lancet_. doi:10.1016/S0140-6736(18)31387-4 #> <https://doi.org/10.1016/S0140-6736%2818%2931387-4>. #> Distribution: gamma (days) #> Parameters: #> shape: 2.400 #> scale: 3.333 #> #> # ℹ 14 more elements #> # ℹ Use `print(n = ...)` to see more elements. #> # ℹ Use `parameter_tbl()` to see a summary table of the parameters. #> # ℹ Explore database online at: https://epiverse-trace.github.io/epiparameter/articles/database.html # a single epi parameter db <- epiparameter_db(epi_name = \"offspring distribution\") #> Returning 10 results that match the criteria (10 are parameterised). #> Use subset to filter by entry variables or single_epiparameter to return a single entry. #> To retrieve the citation for each use the 'get_citation' function db #> # List of 10 <epiparameter> objects #> Number of diseases: 6 #> ❯ Ebola Virus Disease ❯ Hantavirus Pulmonary Syndrome ❯ Mpox ❯ Pneumonic Plague ❯ SARS ❯ Smallpox #> Number of epi parameters: 1 #> ❯ offspring distribution #> [[1]] #> Disease: SARS #> Pathogen: SARS-Cov-1 #> Epi Parameter: offspring distribution #> Study: Lloyd-Smith J, Schreiber S, Kopp P, Getz W (2005). “Superspreading and #> the effect of individual variation on disease emergence.” _Nature_. #> doi:10.1038/nature04153 <https://doi.org/10.1038/nature04153>. #> Distribution: nbinom (No units) #> Parameters: #> mean: 1.630 #> dispersion: 0.160 #> #> [[2]] #> Disease: SARS #> Pathogen: SARS-Cov-1 #> Epi Parameter: offspring distribution #> Study: Lloyd-Smith J, Schreiber S, Kopp P, Getz W (2005). “Superspreading and #> the effect of individual variation on disease emergence.” _Nature_. #> doi:10.1038/nature04153 <https://doi.org/10.1038/nature04153>. #> Distribution: nbinom (No units) #> Parameters: #> mean: 0.940 #> dispersion: 0.170 #> #> [[3]] #> Disease: Smallpox #> Pathogen: Smallpox-Variola-Major #> Epi Parameter: offspring distribution #> Study: Lloyd-Smith J, Schreiber S, Kopp P, Getz W (2005). “Superspreading and #> the effect of individual variation on disease emergence.” _Nature_. #> doi:10.1038/nature04153 <https://doi.org/10.1038/nature04153>. #> Distribution: nbinom (No units) #> Parameters: #> mean: 3.190 #> dispersion: 0.370 #> #> # ℹ 7 more elements #> # ℹ Use `print(n = ...)` to see more elements. #> # ℹ Use `parameter_tbl()` to see a summary table of the parameters. #> # ℹ Explore database online at: https://epiverse-trace.github.io/epiparameter/articles/database.html"},{"path":"https://epiverse-trace.github.io/epiparameter/reference/reexports.html","id":null,"dir":"Reference","previous_headings":"","what":"Objects exported from other packages — reexports","title":"Objects exported from other packages — reexports","text":"objects imported packages. Follow links see documentation. distributional cdf, generate","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/test_epiparameter.html","id":null,"dir":"Reference","previous_headings":"","what":"Test whether an object is a valid <epiparameter> object — test_epiparameter","title":"Test whether an object is a valid <epiparameter> object — test_epiparameter","text":"Test whether object valid <epiparameter> object","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/test_epiparameter.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Test whether an object is a valid <epiparameter> object — test_epiparameter","text":"","code":"test_epiparameter(x)"},{"path":"https://epiverse-trace.github.io/epiparameter/reference/test_epiparameter.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Test whether an object is a valid <epiparameter> object — test_epiparameter","text":"x R object.","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/test_epiparameter.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Test whether an object is a valid <epiparameter> object — test_epiparameter","text":"boolean logical whether object valid <epiparameter> object (prints message invalid <epiparameter> object provided).","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/reference/test_epiparameter.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Test whether an object is a valid <epiparameter> object — test_epiparameter","text":"","code":"ep <- epiparameter_db(single_epiparameter = TRUE) #> Using Linton N, Kobayashi T, Yang Y, Hayashi K, Akhmetzhanov A, Jung S, Yuan #> B, Kinoshita R, Nishiura H (2020). “Incubation Period and Other #> Epidemiological Characteristics of 2019 Novel Coronavirus Infections #> with Right Truncation: A Statistical Analysis of Publicly Available #> Case Data.” _Journal of Clinical Medicine_. doi:10.3390/jcm9020538 #> <https://doi.org/10.3390/jcm9020538>.. #> To retrieve the citation use the 'get_citation' function test_epiparameter(ep) #> [1] TRUE # example with invalid <epiparameter> ep$disease <- NULL test_epiparameter(ep) #> <epiparameter> is invalid due to: #> - <epiparameter> must contain $disease. #> - <epiparameter> must contain one disease. #> [1] FALSE"},{"path":"https://epiverse-trace.github.io/epiparameter/news/index.html","id":"epiparameter-development-version","dir":"Changelog","previous_headings":"","what":"epiparameter (development version)","title":"epiparameter (development version)","text":"library epidemiological parameters (parameters.json) removed {epiparameter} package moved {epiparameterDB} R package taken dependency. {epiparameter} package licensed solely MIT dual licensing CC0 removed (#415). data dictionary (data_dictionary.json) JSON validation workflow (validate-json.yaml) removed package (#415).","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/news/index.html","id":"epiparameter-030","dir":"Changelog","previous_headings":"","what":"epiparameter 0.3.0","title":"epiparameter 0.3.0","text":"third minor release {epiparameter} R package contains range updates improvements package. principal aim release simplify, clarify enhance classes class methods working epidemiological parameters R. large number breaking changes release, primarily functions function arguments renamed restructured, see Breaking changes section overview.","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/news/index.html","id":"new-features-0-3-0","dir":"Changelog","previous_headings":"","what":"New features","title":"epiparameter 0.3.0","text":"library epidemiological parameters updated include 3 new Chikungunya parameter entries. Mpox parameters previously missing Guzzetta et al. entry added (#346 & #374). c() method added <epiparameter> <multi_epiparameter> objects (#368). aggregate() method added <multi_epiparameter> enable consensus distributions built utilising mixture distribution class {distributional} (#388). Infrastructure added package allow translations messages/warnings/errors printed console. (@Bisaloo, #367). convert_summary_stats_to_params() can now convert median dispersion lognormal distribution (#378). data_dictionary.json enhanced improve validation library epidemiological parameters (parameters.json) (#379). interactive network showing <epiparameter> S3 methods added design_principles.Rmd vignette (#383). data_from_epireview.Rmd article improved updated new changes {epireview} (@CarmenTamayo & @cm401 & @kellymccain28, #305 & #373). Parameter units added every entry {epiparameter} library (parameters.json) $metadata element <epiparameter> objects. create_metadata() function now units argument construct metadata lists (#391). Improved database.Rmd vignette adding short citation reference column (@jamesmbaazam, #348). family() method <epiparameter> improved allow access distribution names transformed (e.g. mixture truncated distributions) untransformed (e.g. gamma lognormal) distributions new argument base_dist new internal function .distributional_family() (#398). as_epiparameter() can now work SARS parameters {epireview} (#407).","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/news/index.html","id":"breaking-changes-0-3-0","dir":"Changelog","previous_headings":"","what":"Breaking changes","title":"epiparameter 0.3.0","text":"<epidist> class renamed <epiparameter> avoid confusion similar R package {epidist} (#360). Many functions used epidist names renamed use epiparameter due renaming class (#360). function signatures epiparameter() new_epiparameter() functions (previously epidist() new_epidist()) updated collapse prob_dist, prob_dist_params, discretise truncation arguments prob_distribution, accepts output create_prob_distribution() (#381). epi_dist argument renamed epi_name. clarify {epiparameter} can work epidemiological parameters take variety forms (e.g. point estimates, ranges, probability distributions, etc.) (#390). <vb_epidist> class ’s methods removed package. used increasing complexity maintenance load package (#359). create_prob_dist() renamed create_prob_distribution() (#381). validate_epiparameter() (previously validate_epidist()) renamed assert_epiparameter(), test_epiparameter() added, aim harmonise design {contactmatrix} messages errors improved (#366 & #402). minimum version R required package now 4.1.0 due use base R pipe (|>) dependencies, R-CMD-check workflow GitHub actions now explicitly runs minimum version R stated DESCRIPTION (#384 & #405).","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/news/index.html","id":"bug-fixes-0-3-0","dir":"Changelog","previous_headings":"","what":"Bug fixes","title":"epiparameter 0.3.0","text":"Epidemiological parameter entries library stored lognormal distributions, parameterised median dispersion now converted meanlog sdlog correctly creating <epiparameter> (auto_calc_params = TRUE) (#381).","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/news/index.html","id":"deprecated-and-defunct-0-3-0","dir":"Changelog","previous_headings":"","what":"Deprecated and defunct","title":"epiparameter 0.3.0","text":"epidist_db() deprecated. replaced epiparameter_db() (#360 & #399).","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/news/index.html","id":"epiparameter-020","dir":"Changelog","previous_headings":"","what":"epiparameter 0.2.0","title":"epiparameter 0.2.0","text":"second release {epiparameter} R package focuses interoperability {epireview} R package. Several functions refactored enhanced. release benefited feedback participants EpiParameter Community workshop hosted World Health Organisation.","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/news/index.html","id":"new-features-0-2-0","dir":"Changelog","previous_headings":"","what":"New features","title":"epiparameter 0.2.0","text":"as_epidist() S3 generic added package coercion R objects <epidist> objects. as_epidist.data.frame() method added, well internal functions is_epireview() determines <data.frame> {epireview}, epireview_to_epidist() performs conversion (#298, #334 & #335) epireview_core_cols.rda data added package. used determine whether input as_epidist.data.frame() parameter table {epireview} objects recognisable class attribute (#298). new website vignette (.e. article) data_from_epireview.Rmd added explains use as_epidist() data {epireview} (#298 & #335). new vignette database.Rmd added package provide web interface {epiparameter} library epidemiological parameters. Contributed @sbfnk (#311). plotting method <epidist> objects (plot.epidist()) improved better differentiate continuous discrete discretised distributions (#315). epidist_db(..., single_epidist = TRUE) now prioritises parameter entries account right truncation (#323). create_epidist_prob_dist() (previously named create_prob_dist()) now exported enables control discretisation settings allowing arguments passed distcrete::distcrete() via ... (#324). <multi_epidist> print method (print.multi_epidist()) improved provides object information print header, first elements list elements list short, extra links advice print footer. design print method follows design pattern {pillar} (#326). <epidist> objects functions work <epidist> objects now work exponential distributions (#333). package now explicit data license: CC0 LICENSE file.","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/news/index.html","id":"breaking-changes-0-2-0","dir":"Changelog","previous_headings":"","what":"Breaking changes","title":"epiparameter 0.2.0","text":"list_distributions() replaced parameter_tbl() enhances printing leveraging {pillar} (#321). <vb_epidist> plotting method (plot.vb_epidist()) removed package. provided minimal functionality unnecessarily complicating function signature plot.epidist() (#315).","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/news/index.html","id":"bug-fixes-0-2-0","dir":"Changelog","previous_headings":"","what":"Bug fixes","title":"epiparameter 0.2.0","text":"DOI PMID lowercase throughout package resolve issues older versions R (see issue #301) (#317).","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/news/index.html","id":"deprecated-and-defunct-0-2-0","dir":"Changelog","previous_headings":"","what":"Deprecated and defunct","title":"epiparameter 0.2.0","text":"None","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/news/index.html","id":"epiparameter-010","dir":"Changelog","previous_headings":"","what":"epiparameter 0.1.0","title":"epiparameter 0.1.0","text":"Initial release {epiparameter} R package. {epiparameter} provides: library epidemiological parameters extracted literature range diseases. Functions classes (class methods) work epidemiological parameters distributions.","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/news/index.html","id":"new-features-0-1-0","dir":"Changelog","previous_headings":"","what":"New features","title":"epiparameter 0.1.0","text":"library 122 epidemiological parameter set epidemiological literature. accessible package system data (sysdata.rda, epiparameter::multi_epidist) internal data (inst/extdata/parameters.json). epidist_db() function loads epidemiological parameters library. Distribution parameter conversion extraction functions (convert_params_to_summary_stats() & convert_summary_stats_to_params(), extract_param()). S3 class work epidemiological parameters <epidist>. class S3 methods aid users easily work data structures. include printing, plotting, distribution functions PDF/PMF, CDF, random number generation distribution quantiles. <epidist> class constructor function, validator function, accessors (get_*()), checkers (is_*()). also <vb_epidist> S3 class vector-borne parameters, internal <multi_epidist> class improved printing lists <epidist> objects. package contains utility functions. list_distributions() helper function provide information list <epidist> objects tabular form. calc_disc_dist_quantile() calculates quantiles probability distribution based vector probabilities time data. Five vignettes included initial release. One introduction package (epiparameter.Rmd), one tutorial converting extracting parameters (extract_convert.Rmd), one protocol used collect entries library epidemiological parameters (data_protocol.Rmd), design vignette (design_principles.Rmd), supplementary vignette quantifies bias using parameter extraction (extract_param()) {epiparameter} (extract-bias.Rmd). Unit tests documentation files. Continuous integration workflows R package checks, rendering README.md, calculating test coverage, deploying pkgdown website, updates package citation, linting package code, checking package system dependency changes, updating copyright year, validating parameter library JSON file.","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/news/index.html","id":"breaking-changes-0-1-0","dir":"Changelog","previous_headings":"","what":"Breaking changes","title":"epiparameter 0.1.0","text":"None","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/news/index.html","id":"bug-fixes-0-1-0","dir":"Changelog","previous_headings":"","what":"Bug fixes","title":"epiparameter 0.1.0","text":"None","code":""},{"path":"https://epiverse-trace.github.io/epiparameter/news/index.html","id":"deprecated-and-defunct-0-1-0","dir":"Changelog","previous_headings":"","what":"Deprecated and defunct","title":"epiparameter 0.1.0","text":"None","code":""}]
diff --git a/sitemap.xml b/sitemap.xml
index bf66d495a..936f96fd4 100644
--- a/sitemap.xml
+++ b/sitemap.xml
@@ -77,10 +77,12 @@
<url><loc>https://epiverse-trace.github.io/epiparameter/reference/is_epiparameter_params.html</loc></url>
<url><loc>https://epiverse-trace.github.io/epiparameter/reference/is_parameterised.html</loc></url>
<url><loc>https://epiverse-trace.github.io/epiparameter/reference/is_truncated.html</loc></url>
+<url><loc>https://epiverse-trace.github.io/epiparameter/reference/lines.epiparameter.html</loc></url>
<url><loc>https://epiverse-trace.github.io/epiparameter/reference/mean.epiparameter.html</loc></url>
<url><loc>https://epiverse-trace.github.io/epiparameter/reference/new_epiparameter.html</loc></url>
<url><loc>https://epiverse-trace.github.io/epiparameter/reference/parameter_tbl.html</loc></url>
<url><loc>https://epiverse-trace.github.io/epiparameter/reference/plot.epiparameter.html</loc></url>
+<url><loc>https://epiverse-trace.github.io/epiparameter/reference/plot.multi_epiparameter.html</loc></url>
<url><loc>https://epiverse-trace.github.io/epiparameter/reference/print.epiparameter.html</loc></url>
<url><loc>https://epiverse-trace.github.io/epiparameter/reference/print.multi_epiparameter.html</loc></url>
<url><loc>https://epiverse-trace.github.io/epiparameter/reference/reexports.html</loc></url>
+