implementation_strategy.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Strategy for Lifting Non-Pharmaceutical Implementations\n",
    "This notebook explores a decision support tool, which schedules the lifting of imposed restrictions. The basic idea is to only lift restrictions when a forward simulation guarantees that the load on the health-care system stays below a certain threshold.\n",
    "\n",
    "The simulation is done by the Epidexus agent-based simulation engine. At certain intervals, the population is tested and a probabilistic estimator predicts the estimated mean and variance of the SEIR parameters."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Setting up the Simulation\n",
    "First we will set up a very simplified simulation with everybody in the same home location and empty itineraries. We will start out with 1000 people and infect the first 10."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from epidexus import EpidexusModel\n",
    "from epidexus.world_creation import create_family\n",
    "from epidexus.scenarios.onelocation import OneLocation\n",
    "\n",
    "from datetime import datetime, timedelta\n",
    "import numpy.random\n",
    "\n",
    "poulation_size = 1000\n",
    "infected_people = 10\n",
    "infection_rate = 0.5\n",
    "start_date = datetime(2020, 3, 31)\n",
    "scenario = OneLocation(poulation_size, infected_people, infection_rate, start_date)\n",
    "scenario.set_seed(0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Setup a Strategy\n",
    "\n",
    "The strategy is just a list of tuples of restriction values and the duration it should be enforced. A restriction value 1 is full restriction and 0 is none."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "strategy = [(0,timedelta(days=10)),\n",
    "            (1/2,timedelta(days=30)),\n",
    "            (1/3,timedelta(days=20)),\n",
    "            (1/6,timedelta(days=10)),\n",
    "            (0,timedelta(days=20))]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Run the Simulation\n",
    "\n",
    "We will run through each part of the strategy. First, unpacking and applying the restriction value then simulating the associated time interval."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "for s in strategy:\n",
    "    scenario.control_variable = s[0]\n",
    "    scenario.simulate(s[1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plotting the output\n",
    "Let's see if things look right. First the stackplot:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "(<Figure size 432x288 with 1 Axes>,\n <matplotlib.axes._subplots.AxesSubplot at 0x1ab88162c70>)"
     },
     "metadata": {},
     "execution_count": 4
    },
    {
     "output_type": "display_data",
     "data": {
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "from epidexus.plotting import seir_stackplot, infected_plot\n",
    "seir_stackplot(scenario.sim_model)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will run a reference simulation with the same parameters but without the strategy enforced, and plot the two infected portions against each other:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "scenario_ref = OneLocation(poulation_size, infected_people, infection_rate, start_date)\n",
    "scenario_ref.set_seed(0)\n",
    "\n",
    "total_interval = timedelta()\n",
    "for s in strategy:\n",
    "    total_interval += s[1]\n",
    "scenario_ref.simulate(total_interval)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "<matplotlib.collections.LineCollection at 0x1ab885fb370>"
     },
     "metadata": {},
     "execution_count": 6
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from itertools import accumulate\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "fig.autofmt_xdate()\n",
    "\n",
    "seir = scenario.sim_model.datacollector.get_model_vars_dataframe()\n",
    "ax.fill_between(seir[\"Date\"], seir[\"I\"], color='C1')\n",
    "\n",
    "seir_ref = scenario_ref.sim_model.datacollector.get_model_vars_dataframe()\n",
    "ax.plot(seir_ref[\"Date\"], seir_ref[\"I\"], color='r', alpha=0.3)\n",
    "\n",
    "strat_dates = list(accumulate([start_date]+[s[1] for s in strategy]))\n",
    "ax.hlines([s[0]*100 for s in strategy], strat_dates[0:-1], strat_dates[1:])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*Note that the implementation strategy has been scaled by 100.*"
   ]
  }
 ],
 "metadata": {
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.2-final"
  },
  "orig_nbformat": 2,
  "kernelspec": {
   "name": "python38264bitconda1c1260c8cb184625ac4ead2480c22a75",
   "display_name": "Python 3.8.2 64-bit (conda)"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}