seir-vdwnico-bootstrap.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# SEIR model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from covid19be import load_data\n",
    "data = load_data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "opts = {\n",
    "    \"frac_dh\": 3594 / 7844,  # deaths in hospitals / total deaths\n",
    "    \"hh\": 0.05,              # fraction of hospitalized \n",
    "    \"gamma\": 1 / 12.4,       # inverse recovery time\"\n",
    "    \"epsilon\": 1 / 5.2,      # inverse incubation time \n",
    "    \"dea\": 0.5,              # fatality rate in icu \n",
    "    \"n0\": 11000000,          # population size\n",
    "    \"n0_MRS\": 400000,        # population en MR/MRS + personnel soignant\n",
    "    \"e0_factor\": 37,         # e0 = i0 * factor\n",
    "    \"e0_MRS_factor\": 20,     # e0_MRS = i0_MRS * factor\n",
    "    \"window\": 7,             # size of the window for fitting Re's\"\n",
    "}\n",
    "\n",
    "def smoothen(v, n_pts=opts[\"window\"]):\n",
    "    box = np.ones(n_pts) / n_pts\n",
    "    return np.convolve(v, box, mode=\"same\")\n",
    "\n",
    "def SEIR(r0, i0=3, gg=0.75, n_futures=0, opts=opts):   \n",
    "    # Load hyper-parameters\n",
    "    hh = opts[\"hh\"]\n",
    "    gamma = opts[\"gamma\"]\n",
    "    epsilon = opts[\"epsilon\"]\n",
    "    dea = opts[\"dea\"]\n",
    "    n0 = opts[\"n0\"]\n",
    "    n0_MRS = opts[\"n0_MRS\"]\n",
    "    e0_factor = opts[\"e0_factor\"]\n",
    "    window = opts[\"window\"]\n",
    "    \n",
    "    # Smoothen and extend R0s\n",
    "    r0 = smoothen(r0)\n",
    "    r0 = np.concatenate((r0, np.array([r0[-1]] * (window + n_futures))))\n",
    "    \n",
    "    # Initial conditions\n",
    "    drea = dea * 1 / 5\n",
    "    rrea = (1 - dea) * 1 / 20\n",
    "    hospi = 0.0\n",
    "    \n",
    "    n = [n0-n0_MRS]\n",
    "    i = [i0]\n",
    "    e = [i[-1] * e0_factor]\n",
    "    h = [0.0]\n",
    "    l = [0.0]\n",
    "    r = [0.0]\n",
    "    m = [0.0]\n",
    "    s = [n[-1] - e[-1] - i[-1] - r[-1]]\n",
    "    \n",
    "    # Simulate forward\n",
    "    n_days = len(r0)\n",
    "    \n",
    "    for day in range(n_days):\n",
    "        lam = gamma * r0[day]\n",
    "        \n",
    "        if day == 14:\n",
    "            hospi = hh / 7\n",
    "            \n",
    "        ds = -lam * (i[-1] / 2 + e[-1]) * s[-1] / n[-1]\n",
    "        de = lam * (i[-1] / 2 + e[-1]) * s[-1] / n[-1] - epsilon * e[-1]\n",
    "        di = epsilon * e[-1] - gamma * i[-1] - hospi * i[-1]\n",
    "        dh = hospi * i[-1] - gg * h[-1] / 7 - (1 - gg) * h[-1] / (4 + 2 * np.tanh((l[-1]-500)/300)) + rrea * l[-1]\n",
    "        dl = (1 - gg) * h[-1] / (4 + 2 * np.tanh((l[-1]-500)/300)) - drea * l[-1] - rrea * l[-1]\n",
    "        dr = gamma * i[-1] + gg * h[-1] / 7\n",
    "        dm = drea * l[-1] \n",
    "        \n",
    "        s.append(s[-1] + ds)\n",
    "        e.append(e[-1] + de)\n",
    "        i.append(i[-1] + di)\n",
    "        h.append(h[-1] + dh)\n",
    "        l.append(l[-1] + dl)\n",
    "        if l[-1] > 1895:\n",
    "            dm = dm + (l[-1] - 1895)\n",
    "            l[-1] = 1895\n",
    "        r.append(r[-1] + dr)\n",
    "        m.append(m[-1] + dm)\n",
    "        n.append(s[-1] + e[-1] + i[-1] + h[-1] + l[-1] + r[-1])\n",
    "        \n",
    "    return np.array(s), np.array(e), np.array(i), np.array(h), np.array(l), np.array(m), np.array(r)\n",
    "\n",
    "def SEIR_MRS(r0_mrs, n_futures=0, opts=opts):\n",
    "    # Load hyper-parameters\n",
    "    gamma = opts[\"gamma\"]\n",
    "    epsilon = opts[\"epsilon\"]\n",
    "    n0_MRS = opts[\"n0_MRS\"]\n",
    "    e0_MRS_factor = opts[\"e0_MRS_factor\"]\n",
    "    window = opts[\"window\"]\n",
    "    \n",
    "    # Smoothen and extend R0s\n",
    "    r0_mrs = smoothen(r0_mrs)\n",
    "    r0_mrs = np.concatenate((r0_mrs, np.array([r0_mrs[-1]] * (window + n_futures))))\n",
    "    \n",
    "    # Initial conditions\n",
    "    alpha = 0.15 / 10\n",
    "    lam = gamma * 4.3\n",
    "    \n",
    "    n = [n0_MRS]\n",
    "    i = [1]\n",
    "    e = [i[-1] * e0_MRS_factor]\n",
    "    r = [0.0]\n",
    "    s = [n[-1] - e[-1] - i[-1] - r[-1]]\n",
    "    m = [0.0]\n",
    "    \n",
    "    # Simulate forward\n",
    "    n_days = len(r0_mrs)\n",
    "    \n",
    "    for day in range(n_days):\n",
    "        lam = gamma * r0_mrs[day]\n",
    "        \n",
    "        ds = -lam * (i[-1] / 2 + e[-1]) * s[-1] / n[-1]\n",
    "        de = lam * (i[-1] / 2 + e[-1]) * s[-1] / n[-1] - epsilon * e[-1]\n",
    "        di = epsilon * e[-1] - (gamma + alpha) * i[-1]\n",
    "        dr = gamma * i[-1]\n",
    "        dm = alpha * i[-1]\n",
    "        \n",
    "        s.append(s[-1] + ds)\n",
    "        e.append(e[-1] + de)\n",
    "        i.append(i[-1] + di)\n",
    "        r.append(r[-1] + dr)\n",
    "        m.append(m[-1] + dm)\n",
    "        n.append(s[-1] + e[-1] + i[-1] + r[-1])\n",
    "        \n",
    "    return np.array(s), np.array(e), np.array(i), np.array(m), np.array(r)   \n",
    "\n",
    "def simulate(theta, n_futures=0):   \n",
    "    # Unpack parameters\n",
    "    r0, r0_mrs, i0, gg = theta\n",
    "    \n",
    "    # Simulate\n",
    "    s, e, i, h, l, m, r = SEIR(r0, i0=i0, gg=gg, n_futures=n_futures)\n",
    "    _, _, _, m_mrs, _ = SEIR_MRS(r0_mrs, n_futures=n_futures)\n",
    "    \n",
    "    return s, e, i, h, l, m, m_mrs, r"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "from distributions import poisson_logpdf\n",
    "from scipy.optimize import minimize\n",
    "\n",
    "\n",
    "# Pack-unpack helpers for passing parameters around\n",
    "def pack(r0, r0_rms, i0, gg):\n",
    "    v = np.zeros(len(r0) + len(r0_rms) + 2)\n",
    "    v[:len(r0)] = r0\n",
    "    v[len(r0):len(r0)+len(r0_rms)] = r0_rms\n",
    "    v[-2] = i0\n",
    "    v[-1] = gg\n",
    "    return v \n",
    "\n",
    "def unpack(v):\n",
    "    return v[:(len(v)-2)//2], v[(len(v)-2)//2:len(v)-2], v[-2], v[-1]\n",
    "\n",
    "# Fit\n",
    "def fit(data, logpdf=poisson_logpdf, window=opts[\"window\"], frac_dh=opts[\"frac_dh\"]):\n",
    "    def cost(x):\n",
    "        # parameters\n",
    "        r0, r0_mrs, i0, gg = unpack(x)\n",
    "\n",
    "        # cost\n",
    "        c = 0.0\n",
    "\n",
    "        s, e, i, h, l, m, r = SEIR(r0, i0=i0, gg=gg)\n",
    "        c -= logpdf(data[\"n_hospitalized\"].values[9:], mu=h[10:]+l[10:])\n",
    "        c -= logpdf(data[\"n_icu\"].values[9:], mu=l[10:])\n",
    "        c -= logpdf(frac_dh * data[\"n_deaths\"].values[9:-2], mu=m[10:-2])\n",
    "\n",
    "        # fit on deaths with SEIR_MRS\n",
    "        s, e, i, m_mrs, r = SEIR_MRS(r0_mrs)\n",
    "        c -= logpdf(data[\"n_deaths\"].values[:-2], mu=m[1:-2] + m_mrs[1:-2])\n",
    "\n",
    "        # ^ we omit the last two death data points, because not consolidated yet\n",
    "\n",
    "        return c\n",
    " \n",
    "    # x0\n",
    "    r0 = [4.3] * (len(data) - window)\n",
    "    r0_rms = [4.3] * (len(data) - window)\n",
    "    i0 = 3.0\n",
    "    gg = 0.75\n",
    "    x0 = pack(r0, r0_rms, i0, gg)\n",
    "\n",
    "    # bounds\n",
    "    bounds = []\n",
    "    for _ in range(len(r0)):\n",
    "        bounds.append((0.25, 7.5))\n",
    "    for _ in range(len(r0_rms)):\n",
    "        bounds.append((0.25, 7.5))\n",
    "    bounds.append((1, 500))\n",
    "    bounds.append((0.65, 0.85))\n",
    "\n",
    "    # fit\n",
    "    res = minimize(cost, x0=x0, bounds=bounds, options={\"maxfun\": 25000})\n",
    "    \n",
    "    return unpack(res.x)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Parametric bootstrap"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "from distributions import poisson_rvs\n",
    "\n",
    "from copy import deepcopy\n",
    "from joblib import Parallel, delayed\n",
    "\n",
    "def _fit(data, h, l, m, m_mrs, seed, logpdf=poisson_logpdf, rvs=poisson_rvs):\n",
    "    data_i = deepcopy(data)\n",
    "    rs = np.random.RandomState(seed=seed)\n",
    "    \n",
    "    # resample\n",
    "    data_i[\"n_hospitalized\"] = rvs(h[1:] + l[1:], random_state=rs)\n",
    "    data_i[\"n_icu\"] = rvs(l[1:], random_state=rs)\n",
    "    data_i[\"n_deaths\"] = rvs(m[1:] + m_mrs[1:], random_state=rs)\n",
    "    \n",
    "    # fit on bootstrap\n",
    "    theta_i = fit(data_i, logpdf=logpdf)\n",
    "    \n",
    "    return theta_i\n",
    "\n",
    "def parametric_bootstrap(data, n_replicas=1, logpdf=poisson_logpdf, n_jobs=-1):\n",
    "    # Best fit on original data\n",
    "    theta_mle = fit(data, logpdf=logpdf)\n",
    "    _, _, _, h, l, m, m_mrs, _ = simulate(theta_mle)\n",
    "    \n",
    "    # Bootstrap    \n",
    "    thetas_bootstrap = Parallel(n_jobs=n_jobs, verbose=10, backend=\"multiprocessing\")(\n",
    "        delayed(_fit)(data, h, l, m, m_mrs, i) for i in range(n_replicas))\n",
    "        \n",
    "    return theta_mle, thetas_bootstrap"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend MultiprocessingBackend with 8 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done   2 tasks      | elapsed:  1.6min\n",
      "[Parallel(n_jobs=-1)]: Done   9 tasks      | elapsed:  3.3min\n",
      "[Parallel(n_jobs=-1)]: Done  16 tasks      | elapsed:  3.4min\n",
      "[Parallel(n_jobs=-1)]: Done  25 tasks      | elapsed:  6.8min\n",
      "[Parallel(n_jobs=-1)]: Done  34 tasks      | elapsed:  8.6min\n",
      "[Parallel(n_jobs=-1)]: Done  45 tasks      | elapsed: 10.4min\n",
      "[Parallel(n_jobs=-1)]: Done  56 tasks      | elapsed: 12.0min\n",
      "[Parallel(n_jobs=-1)]: Done  69 tasks      | elapsed: 15.4min\n",
      "[Parallel(n_jobs=-1)]: Done  82 tasks      | elapsed: 18.8min\n",
      "[Parallel(n_jobs=-1)]: Done  96 out of 100 | elapsed: 20.7min remaining:   51.7s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed: 21.4min finished\n"
     ]
    }
   ],
   "source": [
    "# this will take a while...\n",
    "theta_mle, thetas_bootstrap = parametric_bootstrap(data, n_replicas=100, n_jobs=-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Plots\n",
    "n_futures = 365\n",
    "n_days = len(data)\n",
    "window = opts[\"window\"]\n",
    "\n",
    "bootstraps = {\n",
    "    \"h\": [],\n",
    "    \"l\": [],\n",
    "    \"m\": [],\n",
    "    \"m_mrs\": [],\n",
    "    \"r0\": [],\n",
    "    \"r0_mrs\": []\n",
    "}\n",
    "\n",
    "for theta_i in thetas_bootstrap:\n",
    "    _, _, _, h, l, m, m_mrs, _ = simulate(theta_i, n_futures=n_futures)\n",
    "    bootstraps[\"h\"].append(h)\n",
    "    bootstraps[\"l\"].append(l)\n",
    "    bootstraps[\"m\"].append(m)\n",
    "    bootstraps[\"m_mrs\"].append(m_mrs)\n",
    "    \n",
    "    r0, r0_mrs, _, _ = theta_i    \n",
    "    _r0 = smoothen(r0)\n",
    "    _r0_mrs = smoothen(r0_mrs)\n",
    "    _r0 = np.concatenate((_r0, np.array([_r0[-1]] * (window + n_futures))))\n",
    "    _r0_mrs = np.concatenate((_r0_mrs, np.array([_r0_mrs[-1]] * (window + n_futures))))\n",
    "    \n",
    "    bootstraps[\"r0\"].append(_r0)\n",
    "    bootstraps[\"r0_mrs\"].append(_r0_mrs)\n",
    "    \n",
    "for k, v in bootstraps.items():\n",
    "    bootstraps[k] = np.array(v)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "hl_50 = np.percentile(bootstraps[\"h\"]+bootstraps[\"l\"], 50, axis=0)\n",
    "hl_5 = np.percentile(bootstraps[\"h\"]+bootstraps[\"l\"], 5, axis=0)\n",
    "hl_95 = np.percentile(bootstraps[\"h\"]+bootstraps[\"l\"], 95, axis=0)\n",
    "\n",
    "l_50 = np.percentile(bootstraps[\"l\"], 50, axis=0)\n",
    "l_5 = np.percentile(bootstraps[\"l\"], 5, axis=0)\n",
    "l_95 = np.percentile(bootstraps[\"l\"], 95, axis=0)\n",
    "\n",
    "m_50 = np.percentile(bootstraps[\"m\"]+bootstraps[\"m_mrs\"], 50, axis=0)\n",
    "m_5 = np.percentile(bootstraps[\"m\"]+bootstraps[\"m_mrs\"], 5, axis=0)\n",
    "m_95 = np.percentile(bootstraps[\"m\"]+bootstraps[\"m_mrs\"], 95, axis=0)\n",
    "\n",
    "r0_50 = np.percentile(bootstraps[\"r0\"], 50, axis=0)\n",
    "r0_5 = np.percentile(bootstraps[\"r0\"], 5, axis=0)\n",
    "r0_95 = np.percentile(bootstraps[\"r0\"], 95, axis=0)\n",
    "\n",
    "r0_mrs_50 = np.percentile(bootstraps[\"r0_mrs\"], 50, axis=0)\n",
    "r0_mrs_5 = np.percentile(bootstraps[\"r0_mrs\"], 5, axis=0)\n",
    "r0_mrs_95 = np.percentile(bootstraps[\"r0_mrs\"], 95, axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(2, 1, figsize=(10, 10), gridspec_kw={\"height_ratios\": (4, 1)})\n",
    "\n",
    "# Plot hospitalizations, icus and deaths\n",
    "ax[0].plot(range(1, n_days+1 + n_futures), hl_50[1:], c=\"b\")\n",
    "ax[0].fill_between(range(1, n_days+1 + n_futures), hl_5[1:], hl_95[1:], color=\"b\", alpha=0.2)\n",
    "ax[0].plot(range(1, n_days+1), data[\"n_hospitalized\"].values, \".\", c=\"b\", label=\"hospitalized\")\n",
    "\n",
    "ax[0].plot(range(1, n_days+1 + n_futures), l_50[1:], c=\"r\")\n",
    "ax[0].fill_between(range(1, n_days+1 + n_futures), l_5[1:], l_95[1:], color=\"r\", alpha=0.2)\n",
    "ax[0].plot(range(1, n_days+1), data[\"n_icu\"].values, \".\", c=\"r\", label=\"icu\")\n",
    "\n",
    "ax[0].plot(range(1, n_days+1 + n_futures), m_50[1:], c=\"k\")\n",
    "ax[0].fill_between(range(1, n_days+1 + n_futures), m_5[1:], m_95[1:], color=\"k\", alpha=0.2)\n",
    "ax[0].plot(range(1, n_days+1), data[\"n_deaths\"].values, \".\", c=\"k\", label=\"deaths\")\n",
    "\n",
    "ax[0].grid()\n",
    "ax[0].set_ylim(0, 10000)\n",
    "ax[0].set_xlim(0, 100)\n",
    "ax[0].set_xticks(np.arange(1, 100, 5))\n",
    "ax[0].set_xticklabels([d.strftime('%Y-%m-%d') for d in pd.date_range(start=\"2020-02-28\", end=\"2020-12-31\")[:100:5]], rotation=90)\n",
    "ax[0].legend()\n",
    "\n",
    "# Plots R0s\n",
    "ax[1].plot(range(1, n_days+1 + n_futures), r0_50, c=\"orange\", label=\"R0 in population\")\n",
    "ax[1].fill_between(range(1, n_days+1 + n_futures), r0_5, r0_95, color=\"orange\", alpha=0.2)\n",
    "\n",
    "ax[1].plot(range(1, n_days+1 + n_futures), r0_mrs_50, c=\"brown\", label=\"R0 in MRS\")\n",
    "ax[1].fill_between(range(1, n_days+1 + n_futures), r0_mrs_5, r0_mrs_95, color=\"brown\", alpha=0.2)\n",
    "\n",
    "ax[1].grid(which=\"both\")\n",
    "ax[1].set_ylim(0, 5)\n",
    "ax[1].set_yticks(np.arange(0, 5, step=0.5))\n",
    "for j, label in enumerate(ax[1].get_yticklabels()):\n",
    "    if j % 2 != 0:\n",
    "        label.set_visible(False)\n",
    "ax[1].set_xlim(0, 100)\n",
    "ax[1].set_xticks(np.arange(1, 100, 5))\n",
    "ax[1].set_xticklabels([d.strftime('%Y-%m-%d') for d in pd.date_range(start=\"2020-02-28\", end=\"2020-12-31\")[:100:5]], rotation=90)\n",
    "ax[1].legend()\n",
    "\n",
    "plt.subplots_adjust(hspace=0.5)\n",
    "\n",
    "#plt.savefig(\"plot-bootstrap.png\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The parametric bootstrap uncertainty estimates only account for the variability of the best fit, had the data been resampled following the distribution assumed at the best fit parameter estimates. \n",
    "\n",
    "Importantly, this does *not* account for the epistemic uncertainty in the model hyper-parameters. Accounting for those would produce much larger uncertainty estimates."
   ]
  }
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