examples/example_102.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# OpenABM-Covid19 - Example 102 - Lockdown and Contract-Tracing\n",
    "\n",
    "This second example runs the simulation until 1% of the population has been infected. At which point a lockdown is imposed for 30 days. After which a number of other internventions are implemented such as digital contract-tracing."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "import example_utils as utils\n",
    "params = utils.get_baseline_parameters()\n",
    "params.set_param( \"n_total\", 50000 )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The next step is to get the simulation until 500 people have been infected"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "sim = utils.get_simulation( params )\n",
    "sim.steps(1)\n",
    "while sim.results[\"total_infected\"][ -1] < 500:\n",
    "    sim.steps(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now turn on a lockdown and continue for 30 days."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "sim.env.model.update_running_params(\"lockdown_on\", 1)\n",
    "sim.steps( 30 )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now turn off the lockdown and turn on digitial contract tracing, with the following options.\n",
    "1. 80% of people self-quarantine along with their household when they develop symptoms.\n",
    "2. Tracing happens as soon as somebody devleops symptoms and contacts quarantine themselves.\n",
    "3. The households members of those traced also quarantine\n",
    "\n",
    "We then run the simimulation for another 100 days."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# lockdown off\n",
    "sim.env.model.update_running_params(\"lockdown_on\", 0)\n",
    "\n",
    "# 80% self-quarantine along with their households\n",
    "sim.env.model.update_running_params(\"self_quarantine_fraction\", 0.8 )\n",
    "sim.env.model.update_running_params(\"quarantine_household_on_symptoms\", 1 )\n",
    "  \n",
    "# turn on the app and quarantine those people who have been traced along with their households\n",
    "sim.env.model.update_running_params(\"app_turned_on\", 1 )\n",
    "sim.env.model.update_running_params(\"quarantine_on_traced\", 1 )\n",
    "sim.env.model.update_running_params(\"trace_on_symptoms\", 1 )\n",
    "sim.env.model.update_running_params(\"quarantine_household_on_traced_symptoms\", 1 )\n",
    "\n",
    "# step forwrard another 100 days\n",
    "sim.steps( 100 )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hide_input": false
   },
   "source": [
    "Finally plot the result."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x149cac550>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "timeseries = pd.DataFrame( sim.results )\n",
    "timeseries.plot( x = \"time\", y = \"total_infected\" )"
   ]
  }
 ],
 "metadata": {
  "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": "3.7.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}