Game_Cost.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "id": "W3K4saiP5b6W"
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.widgets import Slider, Button\n",
    "import matplotlib.patches as patches"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "id": "KEx_CLW85g_s"
   },
   "outputs": [],
   "source": [
    "p = 19747823.9 #population\n",
    "\n",
    "i = 0.03*p  #infected\n",
    "s = p-i #susceptible\n",
    "r = 0.1 #recovered/removed\n",
    "e=0.2*p #exposed\n",
    "d= 0    #dead\n",
    "\n",
    "alpha = 1.3  #transmission parameter\n",
    "beta = 0.2 #recovery parameter\n",
    "gamma = 0.03 #mortality parameter\n",
    "\n",
    "#Defence Strategy Parameters\n",
    "m = 0.3 #masking\n",
    "sd = 0.2 #social distancing\n",
    "ct = 5 #contact tracing\n",
    "\n",
    "#Attacking Strategy Parameters\n",
    "ri = 1.2 #reduced incubation\n",
    "e = 1.35 #evolution of the disease\n",
    "rz = 1.5 #creating red zones\n",
    "\n",
    "#Cost Function Parameters for Defender\n",
    "CMM = 2\n",
    "CM = 1\n",
    "CSSD = 5\n",
    "CSD = 4\n",
    "CCT = 25\n",
    "\n",
    "#Cost Function Parameters for Attacker\n",
    "CRI = 3.5\n",
    "CE = 4.8\n",
    "CRZ = 6.7\n",
    "\n",
    "initialTime = 0\n",
    "deltaTime = 0.03 #dividing time into small parts\n",
    "maxTime = 500 #maximum time over which the model is observed\n",
    "maxTimeInitial = maxTime"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "id": "yCVKU2uy5iS8"
   },
   "outputs": [],
   "source": [
    "#alpha is the transmission rate\n",
    "#beta is the recovery rate\n",
    "#gamma is the mortality rate\n",
    "\n",
    "def susceptible(S, I, alpha):    \n",
    "    return -1*((alpha*S*I)/p)\n",
    "\n",
    "def exposed(S, I, E, alpha, beta): \n",
    "    return ((alpha*S*I)/p)-beta*E\n",
    "\n",
    "def infected(I, S, alpha, beta, gamma):\n",
    "    return (((alpha*S)/p)-beta)*I - gamma*I\n",
    "\n",
    "def recovered(I, beta):\n",
    "    return beta*I\n",
    "\n",
    "def dead(I, gamma):\n",
    "    return gamma*I\n",
    "\n",
    "def infect_after_strategy(I, S, strategy_factor, alpha, beta, gamma): \n",
    "    return strategy_factor*((((alpha*S)/p)-beta)*I - gamma*I)\n",
    "\n",
    "def masking_cost(S, I):\n",
    "    return CMM*I + CM*S\n",
    "                          \n",
    "def socialdistancing_cost(S, I):\n",
    "    return CSSD*I + CSD*S\n",
    "                          \n",
    "def contacttracing_cost(I):\n",
    "    return CCT*I\n",
    "    \n",
    "def reducedincubation_cost(S, R):\n",
    "    return (S + R)*CRI\n",
    "                          \n",
    "def evolution_cost(S, R):\n",
    "    return (S + R)*CE\n",
    "                          \n",
    "def redzones_cost(S, R):\n",
    "    return (S + R)*CRZ\n",
    "                          \n",
    "                          "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "id": "bC_EAIT95qQp"
   },
   "outputs": [],
   "source": [
    "def Cost_generation(transRate, recovRate, maxT): # defining a data generating function for generating variable s, e, i, r, d values\n",
    "    global alpha, beta, gamma, strategy_factor, maxTimeInitial #defining the variables for generating data\n",
    "    \n",
    "    # initializing the variables\n",
    "    \n",
    "    alpha = transRate\n",
    "    beta = recovRate\n",
    "    \n",
    "    maxTimeInitial = maxT\n",
    "\n",
    "    sInitial = s\n",
    "    iInitial = i\n",
    "    rInitial = r\n",
    "    mInitial = m\n",
    "    sdInitial = sd\n",
    "    ctInitial = ct\n",
    "    riInitial = ri\n",
    "    eInitial = e\n",
    "    redInitial = rz\n",
    "    \n",
    "    \n",
    "    time = np.arange(maxTimeInitial+1) \n",
    "    mVals = np.zeros(maxTimeInitial+1) \n",
    "    sdVals = np.zeros(maxTimeInitial+1) \n",
    "    ctVals = np.zeros(maxTimeInitial+1) \n",
    "    riVals = np.zeros(maxTimeInitial+1) \n",
    "    eVals = np.zeros(maxTimeInitial+1) \n",
    "    redVals = np.zeros(maxTimeInitial+1)\n",
    "\n",
    "    for t in range(initialTime, maxTimeInitial+1): #generating new values with change of time t\n",
    "        mVals[t] = mInitial\n",
    "        sdVals[t] = sdInitial\n",
    "        ctVals[t] = ctInitial\n",
    "        riVals[t] = riInitial\n",
    "        eVals[t] = eInitial\n",
    "        redVals[t] = redInitial\n",
    "        \n",
    "        newDeltas = (masking_cost(sInitial, iInitial), socialdistancing_cost(iInitial, sInitial), \n",
    "                     contacttracing_cost(iInitial), reducedincubation_cost(sInitial, rInitial),\n",
    "                     evolution_cost(sInitial, rInitial), redzones_cost(sInitial, rInitial))\n",
    "        \n",
    "        mInitial += newDeltas[0]*deltaTime \n",
    "        sdInitial += newDeltas[1]*deltaTime \n",
    "        ctInitial += newDeltas[2]*deltaTime \n",
    "        riInitial += newDeltas[3]*deltaTime \n",
    "        eInitial += newDeltas[4]*deltaTime\n",
    "        redInitial += newDeltas[5]*deltaTime\n",
    "      \n",
    "        if mInitial < 0 or sdInitial < 0 or ctInitial < 0 or riInitial <0 or eInitial < 0 or redInitial <0 :\n",
    "            break \n",
    "            \n",
    "    return (time, mVals, sdVals, ctVals, riVals, eVals, redVals)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "id": "zQ9TRfw75vbi"
   },
   "outputs": [],
   "source": [
    "t, c_m, c_sd, c_ct, c_ri, c_e, c_red = Cost_generation(alpha, beta, maxTimeInitial)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 290
    },
    "id": "lnHJqeSkl4GT",
    "outputId": "e7638131-20bc-4d3c-a4cc-23585b946263"
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "plt.plot(t, c_m, label='Masking')\n",
    "plt.plot(t, c_sd, label='Social Distancing')\n",
    "plt.plot(t, c_ct, label='Contact Tracing')\n",
    "plt.plot(t, c_ri, label='Reduced Incubation')\n",
    "plt.plot(t, c_e, label='Evolution')\n",
    "plt.plot(t, c_red, label='Red Zones')\n",
    "plt.xlabel('Time(Days)')\n",
    "plt.ylabel('Costs for New York ($)')\n",
    "plt.legend()\n",
    "plt.savefig(\"cost_func.pdf\", bbox_inches = 'tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "2wf-RVu-nYct"
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "colab": {
   "collapsed_sections": [],
   "name": "GameTheoryC.ipynb",
   "provenance": []
  },
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}