Covid-19.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import numpy as np\n",
    "import scipy\n",
    "import pandas as pd\n",
    "\n",
    "sns.set(font_scale = 1.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Starting Feb 24\n",
    "new_ontario_march = np.array([4,3,2,0,3,5,0,4,3,1,6,17,20,24,42,32,12,25,43])\n",
    "new_canada_march = np.array([4,3,3,4,13,7,6,7,11,18,21,40,39,54,74,101,144,121,81])\n",
    "\n",
    "cases_canada_march = np.cumsum(new_canada_march)\n",
    "cases_ontario_march = np.cumsum(new_ontario_march)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def exponential_growth(t,r0,r):\n",
    "    return r0 * (1 + r) ** t\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "# Ontario Predicted Cases (March)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "r0:4.7132058894839615 r:0.23486759213044794\n",
      "Current Total: 246\n",
      "New Cases Ontario Tomorrow: 149.68051825632892\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f21b61aeb90>]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x_ontario_march = np.linspace(1,len(cases_ontario_march),len(cases_ontario_march))\n",
    "\n",
    "params = scipy.optimize.curve_fit(exponential_growth, x_ontario_march, cases_ontario_march, p0=(4,0.5))\n",
    "r0,r = params[0]\n",
    "\n",
    "n = len(cases_canada_march) + 1\n",
    "\n",
    "n_pred = n + 1\n",
    "x_pred = np.linspace(1, n_pred, n_pred)\n",
    "y_pred = exponential_growth(x_pred,r0,r)\n",
    "\n",
    "print(f\"r0:{r0} r:{r}\")\n",
    "print(f\"Current Total: {cases_ontario_march[-1]}\")\n",
    "print(f\"New Cases Ontario Tomorrow: {y_pred[-1] - cases_ontario_march[-1]}\")\n",
    "plt.bar(x_ontario_march, cases_ontario_march)\n",
    "plt.plot(x_pred, y_pred, c=\"r\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "# Canada Predicted Cases (March)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "r0:8.104311959277686 r:0.2738351549481528\n",
      "Current Total: 751\n",
      "New Cases Canada Tomorrow: 555.4535860242981\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f21b61ae410>]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x_canada_march = np.linspace(1,len(cases_canada_march), len(cases_canada_march))\n",
    "\n",
    "params = scipy.optimize.curve_fit(exponential_growth, x_canada_march, cases_canada_march, p0=(4,0.5))\n",
    "r0,r = params[0]\n",
    "\n",
    "n = len(cases_canada_march) + 1\n",
    "\n",
    "n_pred = n + 1\n",
    "x_pred = np.linspace(1, n_pred, n_pred)\n",
    "y_pred = exponential_growth(x_pred,r0,r)\n",
    "\n",
    "print(f\"r0:{r0} r:{r}\")\n",
    "print(f\"Current Total: {cases_canada_march[-1]}\")\n",
    "print(f\"New Cases Canada Tomorrow: {y_pred[-1] - cases_canada_march[-1]}\")\n",
    "plt.bar(x_canada_march, cases_canada_march)\n",
    "plt.plot(x_pred, y_pred, c=\"r\")"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "argv": [
    "python",
    "-m",
    "ipykernel_launcher",
    "-f",
    "{connection_file}"
   ],
   "display_name": "Python 3",
   "env": null,
   "interrupt_mode": "signal",
   "language": "python",
   "metadata": null,
   "name": "python3"
  },
  "name": "Covid-19.ipynb"
 },
 "nbformat": 4,
 "nbformat_minor": 4
}