-C02R10QDGG7N-2.Rhistory

net_fig
net_fig <- plot_grid(net_figa,
plot_grid(NULL,net_exa,net_exb,net_exc,net_exd,nrow = 1,
rel_widths = c(0.25,1,1,1,1)),
nrow = 2,
rel_heights = c(1,0.3),
labels = "AUTO")
net_fig
?map
devtools::load_all(".")
sce_figa <- sce  %>%
mutate(intervention = recode(intervention,
nothing = "No control",
isolation = "Case isolation",
primary_quarantine = "Primary tracing",
secondary_quarantine = "Secondary tracing")) %>%
mutate(intervention = factor(intervention,
levels = c("No control",
"Case isolation",
"Primary tracing",
"Secondary tracing"))) %>%
unnest(cols = "results") %>%
case_plot(nrow = 1)+
theme(legend.position = "top")
sce_figa
?replicate
sce <- read_rds("data-raw/scenarios.rds")
sce_figa <- sce  %>%
mutate(intervention = recode(intervention,
nothing = "No control",
isolation = "Case isolation",
primary_quarantine = "Primary tracing",
secondary_quarantine = "Secondary tracing")) %>%
mutate(intervention = factor(intervention,
levels = c("No control",
"Case isolation",
"Primary tracing",
"Secondary tracing"))) %>%
unnest(cols = "results") %>%
case_plot(nrow = 1)+
theme(legend.position = "top")
plot_network2 <- purrr::partial(plot_network,
am = am_list[[1]],
day = 20,
num.initial.cases = 1,
prop.asym = 0.4,
delay_shape =  1,
delay_scale = 1.4,
prop.ascertain = 0.9,
presymrate = 0.2,
R = 0.8,
outside = 0.001,
testing = FALSE,
s = 333)
sce_figb <- function(){
par(mar = c(1,0,0,0))
plot_network2(isolation = FALSE,
quarantine = FALSE,
tracing = FALSE,
secondary = FALSE)
}
sce_figc <- function(){
par(mar = c(1,0,0,0))
plot_network2(isolation = TRUE,
quarantine = FALSE,
tracing = FALSE,
secondary = FALSE)
}
sce_figd <- function(){
par(mar = c(1,0,0,0))
plot_network2(isolation = TRUE,
quarantine = TRUE,
tracing = TRUE,
secondary = FALSE)
}
sce_fige <- function(){
par(mar = c(1,0,0,0))
plot_network2(isolation = TRUE,
quarantine = TRUE,
tracing = TRUE,
secondary = TRUE)
}
sce_fig <- plot_grid(sce_figa,
plot_grid(NULL,sce_figb,sce_figc,sce_figd,sce_fige,nrow = 1,
rel_widths = c(0.25,1,1,1,1)),
nrow = 2,
rel_heights = c(1,0.7),
labels = "AUTO")
pdf("inst/plots/Figure_2.pdf",
width = 12,
height = 8)
sce_fig
dev.off()
nrow(sce$results[[1]])
```r
library(covidhm)
#Load association matrices
load("data-raw/am_list.RData")
#First item in the list is data across all days
m <- am_list[[1]]
#Plot network
plot_network(
am = m,
day = 20,
num.initial.cases = 1,
prop.asym = 0.4,
delay_shape =  1,
delay_scale = 1.4,
prop.ascertain = 0.9,
presymrate = 0.2,
R = 0.8,
outside = 0.001,
sensitivity = "high",
testing = "none",
s = 333,
isolation = FALSE,
secondary = FALSE,
tracing = FALSE,
quarantine = FALSE)
#Plot network
plot_network(
am = m,
day = 20,
num.initial.cases = 1,
prop.asym = 0.4,
delay_shape =  1,
delay_scale = 1.4,
prop.ascertain = 0.9,
presymrate = 0.2,
R = 0.8,
outside = 0.001,
testing = FALSE,
s = 333,
isolation = FALSE,
secondary = FALSE,
tracing = FALSE,
quarantine = FALSE)
res <- scenario_sim(net = haslemere, n.sim = 10, num.initial.cases = 1,prop.asym=0.4,
prop.ascertain = 0.9, cap_max_days = 70,
delay_shape = 1, delay_scale = 1.4, R = 0.8, presymrate = 0.2, scenario = "nothing",
sensitivity = "high", testing = FALSE, outside = 0.001, distancing = 0)
res <- scenario_sim(net = haslemere, n.sim = 10, num.initial.cases = 1,prop.asym=0.4,
prop.ascertain = 0.9, cap_max_days = 70,
delay_shape = 1, delay_scale = 1.4, R = 0.8, presymrate = 0.2, scenario = "nothing",
testing = FALSE, outside = 0.001, distancing = 0)
res <- scenario_sim(net = am_list[[1]], n.sim = 10, num.initial.cases = 1,prop.asym=0.4,
prop.ascertain = 0.9, cap_max_days = 70,
delay_shape = 1, delay_scale = 1.4, R = 0.8, presymrate = 0.2, scenario = "nothing",
testing = FALSE, outside = 0.001, distancing = 0)
# Plot of raw cumulative cases
ggplot(data=res, aes(x=week, y=cumcases,col = sim)) +
geom_line(show.legend = FALSE, alpha=0.6, aes(group = sim)) +
scale_y_continuous(name="Weekly number of cases") +
theme_bw()
dis %>% filter(week == 3) %>%
group_by(intervention,distancing) %>%
summarise(medcases = median(weekly_quarantine)/468,
Ucases = quantile(weekly_quarantine,0.75)/468,
Lcases = quantile(weekly_quarantine, 0.25)/468)
dis
dis %>% filter(week == 3) %>%
unnest(cols = "results") %>%
group_by(intervention,distancing) %>%
summarise(medcases = median(weekly_quarantine)/468,
Ucases = quantile(weekly_quarantine,0.75)/468,
Lcases = quantile(weekly_quarantine, 0.25)/468)
dis %>% filter(week == 3) %>%
unnest(cols = "results")
dis %>%
unnest(cols = "results") %>%
filter(week == 3)
dis %>%
unnest(cols = "results") %>%
filter(week == 3) %>%
group_by(intervention,dist) %>%
summarise(medcases = median(weekly_quarantine)/468,
Ucases = quantile(weekly_quarantine,0.75)/468,
Lcases = quantile(weekly_quarantine, 0.25)/468)
dis %>%
unnest(cols = "results") %>%
filter(week == 3) %>%
group_by(intervention,dist) %>%
summarise(medcases = median(weekly_quarantine))
dis %>%
unnest(cols = "results") %>%
filter(week == 3) %>%
group_by(intervention,dist) %>%
summarise(medcases = median(weekly_quarantine)/468,
Ucases = quantile(weekly_quarantine,0.75)/468,
Lcases = quantile(weekly_quarantine, 0.25)/468)
dis_fig <- dis  %>%
mutate(dist = paste0(dist*100,"% reduction"),
intervention = recode(intervention,
nothing = "No control",
isolation = "Case isolation",
primary_quarantine = "Primary tracing",
secondary_quarantine = "Secondary tracing")) %>%
unnest(cols = "results") %>%
case_plot(facet = "grid", gridvar = "dist")+
theme(legend.position = "none")
dis_fig
#distancing
dis %>%
unnest(cols = "results") %>%
filter(week == 3) %>%
group_by(intervention,dist) %>%
summarise(medcases = median(cumcases)/468,
Ucases = quantile(cumcases,0.75)/468,
Lcases = quantile(cumcases, 0.25)/468)
#distancing
dis %>%
unnest(cols = "results") %>%
filter(week == 9) %>%
group_by(intervention,dist) %>%
summarise(medcases = median(cumcases)/468,
Ucases = quantile(cumcases,0.75)/468,
Lcases = quantile(cumcases, 0.25)/468)
-.68-0.31
0.68-0.31
0.37/0.68
tes %>% filter(week == 3) %>%
group_by(intervention,testing) %>%
summarise(medcases = median(weekly_tests)/468,
Ucases = quantile(weekly_tests,0.75)/468,
Lcases = quantile(weekly_tests, 0.25)/468)
tes %>%
unnest(cols = "results") %>%
filter(week == 9)
#testing
tes %>%
unnest(cols = "results") %>%
filter(week == 9) %>%
group_by(intervention,tests) %>%
summarise(medcases = median(cumcases)/468,
Ucases = quantile(cumcases,0.75)/468,
Lcases = quantile(cumcases, 0.25)/468)
468/20
tes %>% unnest(cols = "results") %>%
filter(week == 3) %>%
group_by(intervention,testing) %>%
summarise(medcases = median(weekly_tests)/468,
Ucases = quantile(weekly_tests,0.75)/468,
Lcases = quantile(weekly_tests, 0.25)/468)
tes %>% unnest(cols = "results") %>%
filter(week == 3) %>%
group_by(intervention,tests) %>%
summarise(medcases = median(weekly_tests)/468,
Ucases = quantile(weekly_tests,0.75)/468,
Lcases = quantile(weekly_tests, 0.25)/468)
468*0.07
devtools::load_all(".")
###########################################
#SIMULATE CONTACT TRACING SCENARIOS
###########################################
rm(list=ls())
library(covidhm)
library(dplyr)
library(purrr)
source("inst/scripts/default_params.R")
future::plan("multiprocess")
# Simulate scenarios ------------------------------------------------------
scenarios <- tibble(intervention = c("nothing","isolation", "primary_quarantine","secondary_quarantine"))
res <- scenarios %>%
mutate(results = map(intervention, ~ scenario_sim2(scenario = .,
outside = 0.001,
distancing = 0,
testing = FALSE)))
saveRDS(res, file = "data-raw/scenarios.rds")
res <- scenarios %>%
mutate(results = map(intervention, ~ scenario_sim2(scenario = .,
outside = 0.001,
distancing = 0,
testing = FALSE)))
res <- scenarios %>%
mutate(results = map(intervention, ~ scenario_sim2(scenario = .,
outside = 0.001,
distancing = 0,
testing = FALSE,
test_neg = 0)))
tes <- read_rds("data-raw/testing.rds")
tes_fig <- tes %>%
mutate(tests = paste(tests,"tests per day")) %>%
bind_rows(sce %>%
filter(intervention %in% c("primary_quarantine",
"secondary_quarantine")) %>%
mutate(tests = "No testing")) %>%
mutate(tests = factor(tests, levels = c("No testing",
"5 tests per day",
"25 tests per day",
"50 tests per day")),
intervention = recode(intervention,
primary_quarantine = "Primary tracing",
secondary_quarantine = "Secondary tracing")) %>%
unnest(cols = "results") %>%
case_plot(facet = "grid",gridvar = "tests",testing = TRUE)+
theme(legend.position = "top")
tes_fig
sce <- read_rds("data-raw/scenarios.rds")
tes <- read_rds("data-raw/testing.rds")
tes_fig <- tes %>%
mutate(tests = paste(tests,"tests per day")) %>%
bind_rows(sce %>%
filter(intervention %in% c("primary_quarantine",
"secondary_quarantine")) %>%
mutate(tests = "No testing")) %>%
mutate(tests = factor(tests, levels = c("No testing",
"5 tests per day",
"25 tests per day",
"50 tests per day")),
intervention = recode(intervention,
primary_quarantine = "Primary tracing",
secondary_quarantine = "Secondary tracing")) %>%
unnest(cols = "results") %>%
case_plot(facet = "grid",gridvar = "tests",testing = TRUE)+
theme(legend.position = "top")
tes_fig
sce <- read_rds("data-raw/scenarios.rds")
tes <- read_rds("data-raw/testing.rds")
#Scenarios
sce %>% filter(week == 9) %>%
group_by(intervention) %>%
summarise(medcases = median(cumcases)/468,
Ucases = quantile(cumcases,0.95)/468,
Lcases = quantile(cumcases, 0.05)/468)
#Scenarios
sce %>%
unnest(cols = "results") %>%
filter(week == 9) %>%
group_by(intervention) %>%
summarise(medcases = median(cumcases)/468,
Ucases = quantile(cumcases,0.95)/468,
Lcases = quantile(cumcases, 0.05)/468)
sce %>%
unnest(cols = "results") %>%
filter(week == 3) %>%
group_by(intervention) %>%
summarise(medcases = median(weekly_quarantine)/468,
Ucases = quantile(weekly_quarantine, 0.95)/468,
Lcases = quantile(weekly_quarantine, 0.05)/468)
sce %>%
unnest(cols = "results") %>%
filter(week == 9) %>%
group_by(intervention) %>%
summarise(medcases = median(weekly_quarantine)/468,
Ucases = quantile(weekly_quarantine, 0.95)/468,
Lcases = quantile(weekly_quarantine, 0.05)/468)
#testing
tes %>%
unnest(cols = "results") %>%
filter(week == 9) %>%
group_by(intervention,tests) %>%
summarise(medcases = median(cumcases)/468,
Ucases = quantile(cumcases,0.95)/468,
Lcases = quantile(cumcases, 0.05)/468)
tes %>% unnest(cols = "results") %>%
filter(week == 3) %>%
group_by(intervention,tests) %>%
summarise(medcases = median(weekly_tests)/468,
Ucases = quantile(weekly_tests,0.95)/468,
Lcases = quantile(weekly_tests, 0.05)/468)
scenarios
scenarios <- tidyr::expand_grid(
## Put parameters that are grouped by disease into this data.frame
delay_group = list(tibble::tibble(
delay = c("Medium", "Short"),
delay_shape = c(1.651524, 1),
delay_scale = c(4.287786, 1.4)
)),
presymrate = c(0.2,0.4),
prop.asym = c(0.2, 0.4),
control_effectiveness = c(0.3, 0.6, 0.9),
num.initial.cases = c(1, 5),
scenario = c("primary_quarantine", "secondary_quarantine"),
R = c(0.5,0.8,2)) %>%
tidyr::unnest("delay_group") %>%
dplyr::mutate(scenarioID = 1:dplyr::n())
scenarios
1.5/5
0.3*288
sce <- read_rds("data-raw/scenarios.rds")
net <- read_rds("data-raw/network.rds")
sen <- read_rds("data-raw/sensitivity.rds")
dis <- read_rds("data-raw/distancing.rds")
dis2 <- read_rds("data-raw/distancing2.rds")
out <- read_rds("data-raw/outside.rds")
tes <- read_rds("data-raw/testing.rds")
sce7 <- read_rds("data-raw/scenarios7.rds")
sce16 <- read_rds("data-raw/scenarios16.rds")
load("data-raw/am_list.RData")
tes_fig <- tes %>%
mutate(tests = paste(tests,"tests per day")) %>%
bind_rows(sce %>%
filter(intervention %in% c("primary_quarantine",
"secondary_quarantine")) %>%
mutate(tests = "No testing")) %>%
mutate(tests = factor(tests, levels = c("No testing",
"5 tests per day",
"25 tests per day",
"50 tests per day")),
intervention = recode(intervention,
primary_quarantine = "Primary tracing",
secondary_quarantine = "Secondary tracing")) %>%
unnest(cols = "results") %>%
case_plot(facet = "grid",gridvar = "tests",testing = TRUE)+
theme(legend.position = "top")
dis_fig <- dis  %>%
mutate(dist = paste0(dist*100,"% reduction"),
intervention = recode(intervention,
nothing = "No control",
isolation = "Case isolation",
primary_quarantine = "Primary tracing",
secondary_quarantine = "Secondary tracing")) %>%
unnest(cols = "results") %>%
case_plot(facet = "grid", gridvar = "dist")+
theme(legend.position = "none")
fig3 <- plot_grid(tes_fig,dis_fig,nrow = 2,labels = "AUTO",rel_heights = c(0.45,0.55))
pdf("inst/plots/Figure_3.pdf",
width = 12,
height = 16)
fig3
dev.off()
#distancing
dis %>%
unnest(cols = "results") %>%
filter(week == 9) %>%
group_by(intervention,dist) %>%
summarise(medcases = median(cumcases)/468,
Ucases = quantile(cumcases,0.95)/468,
Lcases = quantile(cumcases, 0.05)/468)
0.746-0.293
0.453/0.746
0.162-0.113
0.046/0.162
dis %>%
unnest(cols = "results") %>%
filter(week == 3) %>%
group_by(intervention,dist) %>%
summarise(medcases = median(weekly_quarantine)/468,
Ucases = quantile(weekly_quarantine,0.95)/468,
Lcases = quantile(weekly_quarantine, 0.05)/468)
dis2 <- read_rds("data-raw/distancing2.rds")
dis2_fig <- dis2  %>%
case_plot(facet = "grid", gridvar = "distancing")
dis2
dis2_fig <- dis2  %>%
mutate(dist = paste0(dist*100,"% reduction"),
intervention = recode(intervention,
nothing = "No control",
isolation = "Case isolation",
primary_quarantine = "Primary tracing",
secondary_quarantine = "Secondary tracing")) %>%
unnest(cols = "results") %>%
case_plot(facet = "grid", gridvar = "dist")+
theme(legend.position = "none")
pdf("inst/plots/EDF_8.pdf",
width =12,
height = 8)
dis2_fig
dev.off()
devtools::document()
roxygen2::roxygenise()
?format_network
?scenario_sim
devtools::document()
roxygen2::roxygenise()
?scenario_sim
## Not run:
#Load association matrices
load("data-raw/am_list.RData")
#First item in the list is data across all days
m <- am_list[[1]]
res <- scenario_sim(net = m, n.sim = 10, num.initial.cases = 1,prop.asym=0.4,
prop.ascertain = 0.9, cap_max_days = 70,
delay_shape = 1, delay_scale = 1.4, R = 0.8, presymrate = 0.2, scenario = "nothing",
testing = FALSE, outside = 0.001, distancing = 0)
res
>outbreak_model
?outbreak_model
devtools::document()
roxygen2::roxygenise()
?outbreak_model
load("data-raw/am_list.RData")
haslemere <- format_network(am_list[[1]])
outbreak_model(net = haslemere, num.initial.cases = 1, prop.ascertain = 0.9, cap_max_days = 69, R = 0.8, presymrate = 0.2,
delay_shape = 1,delay_scale = 1.4,prop.asym = 0.4, quarantine = TRUE, isolation = TRUE, tracing = TRUE, secondary = TRUE,
outside = 0.001, testing = FALSE, test_neg = 0.1)
?outbreak_step
load("data-raw/am_list.RData")
haslemere <- format_network(am_list[[1]])
# incubation period sampling function
incfn <- dist_setup(dist_shape = 2.322737,dist_scale = 6.492272)
# delay distribution sampling function
delayfn <- dist_setup(1, 1.4)
# generate initial cases
case_data <- outbreak_setup(net = haslemere, num.initial.cases = 1,incfn,delayfn,prop.asym=0.4, isolation = TRUE)
# generate next generation of cases
outbreak_step(day = 1, case_data = case_data,net = haslemere, prop.asym = 0.4, incfn = incfn, delayfn = delayfn, prop.ascertain = 1,
presymrate = 0.2, R = 0.8, quarantine = TRUE, isolation = TRUE, tracing = TRUE, secondary = TRUE, outside = 0.001, testing = FALSE,test_neg = 0.1)
?outbreak_setup
devtools::document()
roxygen2::roxygenise()
?outbreak_setup
load("data-raw/am_list.RData")
haslemere <- format_network(am_list[[1]])
# incubation period sampling function
incfn <- dist_setup(dist_shape = 2.322737,dist_scale = 6.492272)
# delay distribution sampling function
delayfn <- dist_setup(dist_shape = 1,dist_scale = 1.4)
outbreak_setup(net, num.initial.cases = 1,incfn,delayfn,prop.asym=0, isolation = TRUE)
outbreak_setup(haslemere, num.initial.cases = 1,incfn,delayfn,prop.asym=0, isolation = TRUE)
devtools::document()
roxygen2::roxygenise()