sim/sim-example.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Example Scenario\n",
    "\n",
    "This notebook contains an example setup to run experiments as done in the [paper](https://arxiv.org/abs/2004.07641).\n",
    "\n",
    "As we are in the process of significantly refactoring the code base and experiments, we did not update the entire experiment notebook done to reproduce the paper experiments, as they are now deprecated.\n",
    "\n",
    "__If you want to play around and reproduce results currently shown in the arXiv paper, revert to commit__\n",
    "`89b7b5dad7716b6461fe0bb574bc7c1304cad656`\n",
    "\n",
    "The notebook is organized as follows:\n",
    "\n",
    "* In Section 1, we define all the simulation parameters.\n",
    "* In Section 2, we run all the simulations needed for the experiment.\n",
    "* In Section 3, we plot the results.\n",
    "\n",
    "To just regenerate the figures from a summary file containing all the simulation objects of the experiment, you can skip Section 2, and only run the cells in Sections 1 & 3. \n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Define the experiment parameters"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Import libs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pickle, math\n",
    "import pandas as pd\n",
    "import multiprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "from lib.mobilitysim import MobilitySimulator\n",
    "from lib.parallel import launch_parallel_simulations\n",
    "from lib.distributions import CovidDistributions\n",
    "from lib.data import collect_data_from_df\n",
    "from lib.measures import (MeasureList, Interval,\n",
    "                          BetaMultiplierMeasureByType,\n",
    "                          SocialDistancingForAllMeasure, \n",
    "                          SocialDistancingForPositiveMeasure,\n",
    "                          SocialDistancingForPositiveMeasureHousehold)\n",
    "from lib.inference import gen_initial_seeds, extract_seeds_from_summary\n",
    "from lib.plot import Plotter\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "# converting days to hours\n",
    "TO_HOURS = 24.0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Set the random seed for reproducibility"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Choose random seed\n",
    "c = 0\n",
    "# Set it\n",
    "np.random.seed(c)\n",
    "# Define prefix string used to save plots\n",
    "runstr = f'run{c}_'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Set the number of roll-outs to simulate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "random_repeats = 6 # Set to at least 40 to obtain stable results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Define the number of parallel workers used for simulations "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "num_workers = multiprocessing.cpu_count() - 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Set the time to simulate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "start_date = '2020-03-08'\n",
    "end_date = '2020-03-27'\n",
    "sim_days = (pd.to_datetime(end_date) - pd.to_datetime(start_date)).days\n",
    "max_time = TO_HOURS * sim_days # in hours"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Define population/mobility parameters\n",
    "\n",
    "Import downsampled version of Tübingen (population downsampled by 20x, sites by 10x) that was used used to generate mobility traces on the fly for inference (sites, home locations, etc)\n",
    "\n",
    "Check out `lib/mobility/` and `lib/settings/` for all pre-generated towns in Germany and Switzerland. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "from lib.settings.town_settings_tubingen import *\n",
    "mob_settings = 'lib/mobility/Tubingen_settings_10.pk'  \n",
    "area = 'TU'\n",
    "country = 'GER'\n",
    "\n",
    "# See town-generator.ipynb for an example on how to create the settings\n",
    "with open(mob_settings, 'rb') as fp:\n",
    "    obj = pickle.load(fp)\n",
    "mob = MobilitySimulator(**obj)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Load the case data\n",
    "\n",
    "Adjust the number of cases to the down-scaling of the population"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "new_cases_ = collect_data_from_df(country=country, area=area, datatype='new',\n",
    "    start_date_string=start_date, end_date_string=end_date)\n",
    "new_cases = np.ceil(\n",
    "        (new_cases_ * mob.num_people_unscaled) /\n",
    "        (mob.downsample * mob.region_population))\n",
    "\n",
    "plt.plot(new_cases.sum(1));"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Instantiate correct state transition distributions (estimated from in literature)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "distributions = CovidDistributions(country=country)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Define helper I/O functions to load/save the output of the experiment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "def save_summary(summary, filename):\n",
    "    with open('summaries/' + filename, 'wb') as fp:\n",
    "        pickle.dump(summary, fp)\n",
    "    \n",
    "def load_summary(filename):\n",
    "    with open('summaries/' + filename, 'rb') as fp:\n",
    "        summary = pickle.load(fp)\n",
    "    return summary"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Define initial seed count per state \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "heuristic_seeds = True\n",
    "\n",
    "# (a) define heuristically based on true cases and literature distribution estimates\n",
    "if heuristic_seeds:\n",
    "    initial_seeds = gen_initial_seeds(new_cases)\n",
    "\n",
    "# (b) define based state of previous batch of simulations, \n",
    "# using the random rollout that best matched the true cases in terms of squared error\n",
    "else:\n",
    "    seed_summary_ = load_summary('summary_example.pk')\n",
    "    seed_day_ = 7\n",
    "    initial_seeeds = extract_seeds_from_summary(seed_summary_, seed_day_, new_cases)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Set epidemic parameters\n",
    "\n",
    "Need to be calibrated using Bayesian optimization in order to match real case data, but possible to design experiments without calibrated parameters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "beta = 0.50 # this needs to be calibrated\n",
    "\n",
    "inferred_params = {\n",
    "    'betas' : {\n",
    "        'education': beta,\n",
    "        'social': beta,\n",
    "        'bus_stop': beta,\n",
    "        'office': beta,\n",
    "        'supermarket': beta}, \n",
    "    'beta_household' : beta\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Define standard testing parameters\n",
    "\n",
    "The parameters are the same used for inference."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "def standard_testing(max_time):\n",
    "    daily_increase = new_cases.sum(axis=1)[1:] - new_cases.sum(axis=1)[:-1]\n",
    "    standard_testing_params = {\n",
    "        'testing_t_window'    : [0.0, max_time], # in hours\n",
    "        'testing_frequency'   : 1 * TO_HOURS,     # in hours\n",
    "        'test_reporting_lag'  : 2 * TO_HOURS,     # in hours (actual and self-report delay)\n",
    "        'tests_per_batch'     : int(daily_increase.max()), # test capacity based on empirical positive tests\n",
    "        'test_fpr'            : 0.0, # test false positive rate\n",
    "        'test_fnr'            : 0.0, # test false negative rate\n",
    "        'test_smart_delta'    : 3 * TO_HOURS, # in hours\n",
    "        'test_smart_duration' : 7 * TO_HOURS, # in hours\n",
    "        'test_smart_action'   : 'isolate', \n",
    "        'test_smart_num_contacts'   : 10, \n",
    "        'test_targets'        : 'isym',\n",
    "        'test_queue_policy'   : 'fifo',\n",
    "        'smart_tracing'       : None, \n",
    "    }\n",
    "    return standard_testing_params"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Run the simulations"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Define a helper function to run general type of experiment, fixing all parameters above."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "def run(tparam, measure_list, t, local_seeds, dynamic_tracing=False):\n",
    "\n",
    "    # add standard measure of positives staying isolated\n",
    "    measure_list +=  [\n",
    "        SocialDistancingForPositiveMeasure(\n",
    "            t_window=Interval(0.0, t), p_stay_home=1.0),\n",
    "        \n",
    "        SocialDistancingForPositiveMeasureHousehold(\n",
    "            t_window=Interval(0.0, t), p_isolate=1.0)\n",
    "    ]\n",
    "    measure_list = MeasureList(measure_list)\n",
    "\n",
    "    # run simulations\n",
    "    summary = launch_parallel_simulations(\n",
    "        mob_settings, \n",
    "        distributions, \n",
    "        random_repeats, num_workers, \n",
    "        inferred_params, local_seeds, tparam, measure_list, \n",
    "        max_time=t, \n",
    "        num_people=mob.num_people, \n",
    "        num_sites=mob.num_sites, \n",
    "        site_loc=mob.site_loc, \n",
    "        home_loc=mob.home_loc,\n",
    "        dynamic_tracing=dynamic_tracing,\n",
    "        verbose=False)\n",
    "    return summary\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Define Measures to take place during simulation\n",
    "\n",
    "See `measures.py` for other options."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "lockdown_at_day = 12\n",
    "\n",
    "example_measures = [\n",
    "    \n",
    "    # education, social sites, and offices close after 1 week\n",
    "    BetaMultiplierMeasureByType(\n",
    "        t_window=Interval(lockdown_at_day * TO_HOURS, max_time), \n",
    "        beta_multiplier={ \n",
    "            'education': 0.0, \n",
    "            'social': 0.0, \n",
    "            'bus_stop': 1.0, \n",
    "            'office': 0.0, \n",
    "            'supermarket': 1.0\n",
    "        }),\n",
    "    \n",
    "    # less activities of all due to contact constraints after 1 week\n",
    "    SocialDistancingForAllMeasure(\n",
    "     t_window=Interval(lockdown_at_day * TO_HOURS, max_time), \n",
    "        p_stay_home=0.5)\n",
    "]\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Run the experiment using the parameters defined above and simulate in the future, with additional measures of varying duration.\n",
    "\n",
    "**WARNING: this cell might take a long time to run depending of the parameters defined above!**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "testing_params = standard_testing(max_time)\n",
    "summary_example = run(testing_params, example_measures, max_time, initial_seeds)\n",
    "save_summary(summary_example, 'summary_example.pk')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Plot the results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Load the summary holding the results of the experiment to plot."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "summary_example = load_summary('summary_example.pk')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Example plot (a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "plotter = Plotter()\n",
    "plotter.plot_positives_vs_target(\n",
    "    summary_example, new_cases.sum(axis=1), \n",
    "    title='Example', \n",
    "    filename=runstr + 'ex_00',\n",
    "    figsize=(6, 4),\n",
    "    start_date=start_date,\n",
    "    errorevery=1, acc=1000, \n",
    "    lockdown_at=lockdown_at_day,\n",
    "    ymax=50)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Example plot (b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimize sigma using maximum likelihood estimation...\n",
      " 0.50: 5\n",
      " 0.50: 5\n",
      " 0.01: 5\n",
      " 0.01: 5\n",
      "done. Best sigma found at: 0.01\n"
     ]
    },
    {
     "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": [
    "plotter = Plotter()\n",
    "plotter.plot_daily_rts(\n",
    "    summary_example,\n",
    "    filename=runstr + 'ex_01',\n",
    "    start_date=start_date,\n",
    "    sigma=None,\n",
    "    figsize=(6, 4), \n",
    "    subplot_adjust=None,\n",
    "    lockdown_label='Lockdown', \n",
    "    lockdown_at=lockdown_at_day,\n",
    "    lockdown_label_y=2.6,\n",
    "    ymax=4.0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Example plot (c)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "targets = new_cases.sum(axis=1)\n",
    "targets = np.hstack((targets[0], np.diff(targets)))\n",
    "\n",
    "plotter = Plotter()\n",
    "plotter.plot_daily_infected(\n",
    "    summary_example,\n",
    "    title='', \n",
    "    filename=runstr + 'ex_02',\n",
    "    errorevery=100, acc=1000, \n",
    "    figsize=(6, 4),\n",
    "    start_date=start_date,\n",
    "    show_target=targets,\n",
    "    lockdown_at=lockdown_at_day,\n",
    "    lockdown_label_y=30,\n",
    "    lockdown_label='Lockdown',\n",
    "    ymax=50)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Example plot (d)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "plotter = Plotter()\n",
    "plotter.plot_cumulative_infected(\n",
    "    summary_example,\n",
    "    title='', \n",
    "    filename=runstr + 'ex_03',\n",
    "    figsize=(6, 4),\n",
    "    legend_loc='upper left',\n",
    "    errorevery=100, acc=1000, \n",
    "    start_date=start_date,\n",
    "    lockdown_at=lockdown_at_day,\n",
    "    lockdown_label_y=60,\n",
    "    show_target=new_cases.sum(axis=1),\n",
    "    ymax=60)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}