agent_based_disease_modelling.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "d00a6825-afd7-4abf-ab7a-3978cd1b438e",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib\n",
    "import pandas as pd\n",
    "from tqdm import tqdm\n",
    "import matplotlib.animation as animation\n",
    "from scipy.spatial import distance_matrix\n",
    "import imageio\n",
    "\n",
    "%matplotlib widget"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "c6727be8-3478-48d4-ab8f-3412438e738f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mkdir: cannot create directory ‘frames’: File exists\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_3717/538327406.py:89: DeprecationWarning: Starting with ImageIO v3 the behavior of this function will switch to that of iio.v3.imread. To keep the current behavior (and make this warning disappear) use `import imageio.v2 as imageio` or call `imageio.v2.imread` directly.\n",
      "  images.append(imageio.imread(\"frames/\" + str(counter)+ \".png\"))\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "6ee38b2ed5364d98ad4c4115e2d4e8c7",
       "version_major": 2,
       "version_minor": 0
      },
      "image/png": "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",
      "text/html": [
       "\n",
       "            <div style=\"display: inline-block;\">\n",
       "                <div class=\"jupyter-widgets widget-label\" style=\"text-align: center;\">\n",
       "                    Figure\n",
       "                </div>\n",
       "                <img src='data:image/png;base64,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' width=500.0/>\n",
       "            </div>\n",
       "        "
      ],
      "text/plain": [
       "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from scipy.spatial import distance_matrix\n",
    "\n",
    "class Agent():\n",
    "    def __init__(self,x,y,infected,resistance,infectedCounter = 0):\n",
    "        #initalize position\n",
    "        self.x = x\n",
    "        self.y = y\n",
    "        self.infected = infected\n",
    "        self.resistance = resistance\n",
    "        self.infectedCounter = infectedCounter \n",
    "        \n",
    "    def movement(self,stepSize,xBounds,yBounds):\n",
    "        #step\n",
    "        self.x += stepSize*np.random.uniform(-1,1)\n",
    "        self.y += stepSize*np.random.uniform(-1,1)\n",
    "        \n",
    "        #check boundaries\n",
    "        if self.x < xBounds[0]:\n",
    "            self.x = xBounds[0]\n",
    "        elif self.x > xBounds[1]:\n",
    "            self.x = xBounds[1]\n",
    "        \n",
    "        if self.y < yBounds[0]:\n",
    "            self.y = yBounds[0]\n",
    "        elif self.y > yBounds[1]:\n",
    "            self.y = yBounds[1]\n",
    "            \n",
    "        #Tick down infeced counter\n",
    "        if self.infected == True:\n",
    "            self.infectedCounter -= 1\n",
    "            if self.infectedCounter <= 0:\n",
    "                self.infected = False\n",
    "        \n",
    "    def infect(self):\n",
    "        infectRoll = np.random.uniform()\n",
    "        \n",
    "        if self.infected == False and infectRoll > self.resistance:\n",
    "            self.infected = True\n",
    "            self.infectedCounter = 50\n",
    "            self.resistance *= 1.5\n",
    "\n",
    "def getPosition(agents):\n",
    "    positions = []\n",
    "    for agent in agents:\n",
    "        positions.append([agent.x,agent.y])\n",
    "    positions = np.array(positions)\n",
    "    return positions\n",
    "\n",
    "def moveAgents(agents,stepSize,xBounds,yBounds):\n",
    "    for i in range(0,len(agents)):\n",
    "        agents[i].movement(stepSize,xBounds,yBounds)\n",
    "    return agents\n",
    "\n",
    "def getInfected(agents):\n",
    "    infected  = []\n",
    "    for agent in agents:\n",
    "        infected.append(agent.infected)\n",
    "    return infected\n",
    "\n",
    "def getCloseAgents(distanceMatrix,agentNumber):\n",
    "    sort = np.argsort(distanceMatrix[agentNumber])\n",
    "    closeMask = distanceMatrix[agentNumber][sort] < 10\n",
    "    closeAgents = np.argsort(distanceMatrix[agentNumber])[closeMask][1:]\n",
    "    return closeAgents\n",
    "\n",
    "def rollInfect(agents):\n",
    "    positions = getPosition(agents)\n",
    "    distanceMatrix = distance_matrix(positions,positions)\n",
    "    for i in range(0,len(agents)):\n",
    "        closeAgents = getCloseAgents(distanceMatrix,i)\n",
    "        for j in closeAgents:\n",
    "            if agents[j].infected == True:\n",
    "                agents[i].infect()\n",
    "    return agents\n",
    "\n",
    "def makeGif(frames,name):\n",
    "    !mkdir frames\n",
    "    \n",
    "    counter=0\n",
    "    images = []\n",
    "    for i in range(0,len(frames)):\n",
    "        plt.figure(figsize = (6,6))\n",
    "        plt.scatter(frames[i][0],frames[i][1], c = frames[i][2], cmap = \"RdYlGn_r\")\n",
    "        plt.title(\"Infected = \" + str(np.sum(frames[i][2])))\n",
    "        plt.tick_params(left = False, right = False , labelleft = False,labelbottom = False, bottom = False)\n",
    "        plt.savefig(\"frames/\" + str(counter)+ \".png\")\n",
    "        images.append(imageio.imread(\"frames/\" + str(counter)+ \".png\"))\n",
    "        counter += 1\n",
    "        plt.close()\n",
    "        \n",
    "    imageio.mimsave(name, images)\n",
    "        \n",
    "    !rm -r frames\n",
    "\n",
    "agents = []\n",
    "for i in range(0,500):\n",
    "    x = np.random.uniform(0,500)\n",
    "    y = np.random.uniform(0,500)\n",
    "    agents.append(Agent(x,y,0,.3))\n",
    "    \n",
    "for i in range(0,10):\n",
    "    agents[i].infected = 1\n",
    "    agents[i].infectedCounter = 50\n",
    "    \n",
    "stepSize = 5\n",
    "xBounds = [0,500]\n",
    "yBounds = [0,500]\n",
    "\n",
    "frames = []\n",
    "for i in range(0,500):\n",
    "    agents = moveAgents(agents,stepSize,xBounds,yBounds)\n",
    "    agents = rollInfect(agents)\n",
    "    positions = getPosition(agents)\n",
    "    infected = getInfected(agents)\n",
    "    frames.append([positions[:,0] , positions[:,1], infected])\n",
    "    \n",
    "\n",
    "fig, ax = plt.subplots(1, 1)\n",
    "fig.set_size_inches(5,5)\n",
    "def animate(i):\n",
    "    x, y = frames[i]\n",
    "    ax.clear()\n",
    "    # Plot that point using the x and y coordinates\n",
    "    ax.scatter(x, y)\n",
    "\n",
    "makeGif(frames,\"Simulation.gif\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7104376d-34e7-44d4-82d8-a61c61385311",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}