examples/example_compartmental_models.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "4c030aff-0ea0-4a60-beaa-541bbf210236",
   "metadata": {},
   "source": [
    "[![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/stammler/simframe/HEAD?labpath=examples%2Fexample_compartmental_models.ipynb)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e3e960fb-c87a-417e-a044-b0cf71b25e85",
   "metadata": {},
   "source": [
    "# Example: Compartmental Models"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2f3ed088-6dae-4f44-a74a-7cd3e428bdce",
   "metadata": {},
   "source": [
    "In this notebook we are going to investigate [compartmental models](https://en.wikipedia.org/wiki/Compartmental_models_in_epidemiology) for infectious diseases.  \n",
    "In these models the entire population is divided into different compartments whose evolution is simulated over time.\n",
    "\n",
    "In the first example we are going to create a simple compartmental model of a fictional disease and investigate how the compartments developed with time-dependent parameters.  \n",
    "In the second example we are going to run a more complex model of the SARS-CoV-2 outbreak."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d985b151-cc1f-4928-9350-5eec0e106102",
   "metadata": {},
   "source": [
    "## SIRD Model"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "65e74688-1d3e-406d-a8f0-0659f4eb5c95",
   "metadata": {},
   "source": [
    "One of the simplest models is for example the SIRD model, which consists of four compartments: $S$usceptible, $I$nfectious, $R$ecovered, and $D$eceased. The four compartments are governed by the differential equations\n",
    "\n",
    "$\\frac{\\mathrm{d}}{\\mathrm{d}t} S = -\\alpha R_1 \\frac{SI}{N}$\n",
    "\n",
    "$\\frac{\\mathrm{d}}{\\mathrm{d}t} I = \\alpha R_1 \\frac{SI}{N} - (1-\\beta) R_2 I - \\beta R_3 I$\n",
    "\n",
    "$\\frac{\\mathrm{d}}{\\mathrm{d}t} R = \\left(1-\\beta\\right) R_2 I$\n",
    "\n",
    "$\\frac{\\mathrm{d}}{\\mathrm{d}t} D = \\beta R_3 I$\n",
    "\n",
    "where $R_1$ is the average rate of contacts per person per time, $\\alpha$ the probability of disease transmission per contact, $\\beta$ the mortality, $R_2$ the average recovery time, and $R_3$ the average time until death. $N = S + I + R + D$ is the total size of the population."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e0f40a6d-31f2-4c24-92c2-b15f2918825f",
   "metadata": {},
   "source": [
    "There are a few key parameters to describe the system. The basic reproduction number $R_0$ is the expected number of new infections if there is one infected person with the entire population susceptible. In our model this would be.\n",
    "\n",
    "$R_0 = \\frac{\\alpha R_1}{\\beta R_2 + \\left(1-\\beta\\right)R_3}$\n",
    "\n",
    "In general, the time-dependent reproduction number is the ratio of influx into the infectious comparments to the outflux from those compartments. In our model this is\n",
    "\n",
    "$R_t = R_0\\frac{S}{N}$"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b0dbd6dc-2f6b-47fa-9534-d59e1d28a30d",
   "metadata": {},
   "source": [
    "To set up the model, we need to define the free parameters. We are going to assume that the population has a size of $10$ million people. On average these people have $5$ contacts per day. The transmission probability per contact of our fictitious disease it $5\\,\\%$. Of all infected $97\\,\\%$ will recover within $14$ days, while $3\\,\\%$ will die within $14$ days after infection. Initially we have one infected person: patient zero.\n",
    "\n",
    "The base time unit of the model will be days."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "8ce20436-7e74-463a-afdc-3b7362199d8e",
   "metadata": {},
   "outputs": [],
   "source": [
    "N     = 10_000_000.  # Total population size\n",
    "alpha = 0.05         # Transmission probability\n",
    "beta  = 0.03         # Mortality\n",
    "R1    = 5.           # Contacts per day\n",
    "R2    = 1./14.       # Inverse recovery time\n",
    "R3    = 1./14.       # Inverse time until death\n",
    "\n",
    "I0    = 1.           # Initial infectious population"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "23271265-3d21-4513-bd16-aec8e7b3ec12",
   "metadata": {},
   "source": [
    "We set up the simulation frame and create a field for our state vector $\\vec{Y}$ and initialize it with the initial conditions. The task of the simulation frame is to simulate the evolution of said state vector.\n",
    "\n",
    "$\\vec{Y} = \\begin{pmatrix} S \\\\ I \\\\ R \\\\ D \\end{pmatrix}$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "3cf3e20b-0661-4f0d-883b-60287a7aeefd",
   "metadata": {},
   "outputs": [],
   "source": [
    "Y0 = [\n",
    "    N-I0,  # Susceptible\n",
    "    I0,    # Infectious\n",
    "    0.,    # Recovered\n",
    "    0.     # Deceased\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "87282dff-132a-4492-a7f7-77a8438bdd57",
   "metadata": {},
   "outputs": [],
   "source": [
    "from simframe import Frame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "21e0d763-b8a3-4ca7-add4-42d6419a0414",
   "metadata": {},
   "outputs": [],
   "source": [
    "sim = Frame(description=\"SIRD Model\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "6ab69f40-c8f4-448a-b6c6-8042a94f2a4c",
   "metadata": {},
   "outputs": [],
   "source": [
    "sim.addfield(\"Y\", Y0, description=\"State Vector\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e7e24284-3d0c-46c3-875f-2e242cb51044",
   "metadata": {},
   "source": [
    "We create a group in which we store the model parameters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "7edf30f8-c05c-453e-aa23-6695d47bcbd5",
   "metadata": {},
   "outputs": [],
   "source": [
    "sim.addgroup(\"pars\", description=\"Model Parameters\")\n",
    "sim.pars.addfield(\"alpha\", alpha, description=\"Transmission probability\")\n",
    "sim.pars.addfield(\"beta\", beta, description=\"Recovery probability\")\n",
    "sim.pars.addfield(\"R1\", R1, description=\"Contact rate\")\n",
    "sim.pars.addfield(\"R2\", R2, description=\"Inverse recovery time\")\n",
    "sim.pars.addfield(\"R3\", R3, description=\"Inverse time until death\")\n",
    "sim.pars.addfield(\"N\", N, description=\"Total population size\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e821fb20-6a52-4eed-9c91-256034f90811",
   "metadata": {},
   "source": [
    "Additionally, we add fields for the basic reproduction number and for the reproduction number for later analysis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "d8b3f6e8-f5cd-4b91-965b-e50baa4dfedf",
   "metadata": {},
   "outputs": [],
   "source": [
    "sim.pars.addfield(\"Rt\", 0., description=\"Time-dependent reproduction number\")\n",
    "sim.pars.addfield(\"R0\", 0., description=\"Basic reproduction number\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f795d889-89b2-4522-8dc3-225043148ce7",
   "metadata": {},
   "source": [
    "We then define and add updaters to the reproduction numbers that calculate the numbers from the current state of the simulation frame. The compartments group, that is used in the updater of the time-dependent reproduction number, will be defined later."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "56a63a7b-4684-40a8-b0a4-fb9f2de8956f",
   "metadata": {},
   "outputs": [],
   "source": [
    "def f_R0(sim):\n",
    "    alpha, beta = sim.pars.alpha, sim.pars.beta\n",
    "    R1, R2, R3 = sim.pars.R1, sim.pars.R2, sim.pars.R3\n",
    "    return alpha*R1/(beta*R2+(1-beta)*R3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "5c3fbdca-99a0-4b12-a509-364a2932cac6",
   "metadata": {},
   "outputs": [],
   "source": [
    "def f_Rt(sim):\n",
    "    return sim.pars.R0*sim.comp.S/sim.pars.N"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "2fefe680-262a-4a0a-9928-74196364a5f2",
   "metadata": {},
   "outputs": [],
   "source": [
    "sim.pars.R0.updater = f_R0\n",
    "sim.pars.Rt.updater = f_Rt"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bda04a44-4391-4903-b69a-9258101321c7",
   "metadata": {},
   "source": [
    "Additionally we add an updater for the total population size, which is simply the sum of the state vector. In our model the population size will be constant, size we are not considering births."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "42a5d3cf-51d5-4fee-a199-d420b8ac04b7",
   "metadata": {},
   "outputs": [],
   "source": [
    "def f_N(sim):\n",
    "    return sim.Y.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "d8394a75-43ae-48a5-8a5e-a0dbdb033d6c",
   "metadata": {},
   "outputs": [],
   "source": [
    "sim.pars.N.updater = f_N"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "16f2366f-9189-49ab-b105-dc871f633ca3",
   "metadata": {},
   "source": [
    "Now we tell the simulation frame that it has to update the reproduction numbers and the population size in the parameters group."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "8f96e31b-b1b0-4e15-9266-1650e256ba7c",
   "metadata": {},
   "outputs": [],
   "source": [
    "sim.pars.updater = [\"R0\", \"Rt\", \"N\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3cc3ca77-b65b-4a9f-99b3-63ab0875e29f",
   "metadata": {},
   "source": [
    "For convenience we create a group for the compartments and simply copy the elements of the state vector into them in the systole of the compartment updater."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "85b046bd-1816-4616-833f-871fa73c6802",
   "metadata": {},
   "outputs": [],
   "source": [
    "sim.addgroup(\"comp\", description=\"Model Compartments\")\n",
    "sim.comp.addfield(\"S\", sim.Y[0], description=\"Susceptible\")\n",
    "sim.comp.addfield(\"I\", sim.Y[1], description=\"Infectious\")\n",
    "sim.comp.addfield(\"R\", sim.Y[2], description=\"Recovered\")\n",
    "sim.comp.addfield(\"D\", sim.Y[3], description=\"Deceased\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "33a5f9d0-7a9c-4c79-ae75-4300b057262c",
   "metadata": {},
   "outputs": [],
   "source": [
    "def f_comp_systole(sim):\n",
    "    sim.comp.S[:] = sim.Y[0]\n",
    "    sim.comp.I[:] = sim.Y[1]\n",
    "    sim.comp.R[:] = sim.Y[2]\n",
    "    sim.comp.D[:] = sim.Y[3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "318ee695-e851-4e5d-acec-c6f03f231664",
   "metadata": {},
   "outputs": [],
   "source": [
    "sim.comp.updater.systole = f_comp_systole"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "844808ab-6370-45a1-8aa1-9c8243998e4c",
   "metadata": {},
   "source": [
    "Finally, we have to tell the main simulation frame to update the compartments and parameters groups."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "21379176-0c26-45c7-b43b-d072afcf2316",
   "metadata": {},
   "outputs": [],
   "source": [
    "sim.updater = [\"comp\", \"pars\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8320925c-eb72-4d46-a6e2-d8db41c0dbf3",
   "metadata": {},
   "source": [
    "Now we have to write a function that returns the derivative of our state vector."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "06f73965-4c49-4554-aea7-1df87af86c21",
   "metadata": {},
   "outputs": [],
   "source": [
    "def f_dYdt(sim, x, Y):\n",
    "    \n",
    "    S, I, R, D = Y\n",
    "    alpha, beta, R1, R2, R3, N = sim.pars.alpha, sim.pars.beta, sim.pars.R1, sim.pars.R2, sim.pars.R3, sim.pars.N\n",
    "    \n",
    "    dY = np.empty_like(sim.Y)\n",
    "    \n",
    "    dY[0] = -alpha*R1*S*I/N\n",
    "    dY[2] = (1.-beta)*R2*I\n",
    "    dY[3] = beta*R3*I\n",
    "    dY[1] = -(dY[0]+dY[2]+dY[3])\n",
    "    \n",
    "    return dY"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4b373be5-a56c-4f9c-b7a5-aa9324e01b40",
   "metadata": {},
   "source": [
    "And assign it to the differentiator of the field."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "446f0a99-bf5c-4eb9-992a-76c6c3920f3a",
   "metadata": {},
   "outputs": [],
   "source": [
    "sim.Y.differentiator = f_dYdt"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5732e792-0114-4056-b776-00b1bca036e4",
   "metadata": {},
   "source": [
    "In the next step we have to set up the integration variable."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "223886a5-f54c-48ff-9fa0-d843762e4e19",
   "metadata": {},
   "outputs": [],
   "source": [
    "sim.addintegrationvariable(\"t\", 0., description=\"Time [days]\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4e0e090e-10e9-4394-a664-fde6bb37bad1",
   "metadata": {},
   "source": [
    "We want to run the simulation for $1$ year and have an output every day."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "21995e1a-37b2-4996-befe-3608cbc17bd5",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "12b75e14-8c6e-4e21-8f08-e2aa35390766",
   "metadata": {},
   "outputs": [],
   "source": [
    "sim.t.snapshots = np.arange(1., 366., 1.)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c00fab40-e1dd-493c-82e0-5fba7709e247",
   "metadata": {},
   "source": [
    "We limit the time step, such that no compartment can fall below $0$ or can be larger than the population size. We add an [CFL factor](https://en.wikipedia.org/wiki/Courant%E2%80%93Friedrichs%E2%80%93Lewy_condition) as safety margin."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "0a8ffe96-19e8-41ef-ad59-72a4a8f60a11",
   "metadata": {},
   "outputs": [],
   "source": [
    "def f_dt(sim):\n",
    "    \n",
    "    cfl = 0.3\n",
    "    \n",
    "    dY = sim.Y.derivative()\n",
    "    bounds = (0., sim.pars.N)\n",
    "    \n",
    "    rneg = (sim.Y-bounds[0])/dY\n",
    "    rpos = (bounds[1]-sim.Y)/dY\n",
    "    \n",
    "    dtneg = np.where(dY<0., cfl*np.abs(rneg), 1.e100).min()\n",
    "    dtpos = np.where(dY>0., cfl*np.abs(rpos), 1.e100).min()\n",
    "    \n",
    "    dt = np.minimum(dtneg, dtpos)\n",
    "    \n",
    "    return dt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "e0d736d6-abed-4b8f-97b5-cbf0bc26839d",
   "metadata": {},
   "outputs": [],
   "source": [
    "sim.t.updater = f_dt"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "66a85739-babe-4e8b-b3ca-83ef57060b9a",
   "metadata": {},
   "source": [
    "We add a single integration instruction and integrate the state vector with the classical 4th order Runge-Kutta scheme."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "e91159d5-c8da-4ddd-bf45-9154c46a02a4",
   "metadata": {},
   "outputs": [],
   "source": [
    "from simframe import Integrator\n",
    "from simframe import Instruction\n",
    "from simframe import schemes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "eca58539-212b-4b36-b0f4-efdcf1b81e03",
   "metadata": {},
   "outputs": [],
   "source": [
    "sim.integrator = Integrator(sim.t)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "dca8dcf6-cec6-41cd-9e3c-38b2c00c45cf",
   "metadata": {},
   "outputs": [],
   "source": [
    "sim.integrator.instructions = [\n",
    "    Instruction(schemes.expl_4_runge_kutta, sim.Y)\n",
    "]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1077caf3-8d46-47fc-a8b2-a143c901f0e3",
   "metadata": {},
   "source": [
    "We do not want to write output files. Instead we save the data in the simulation frame using the namespace writer. We set the verbosity to zero, because we do not want the writer to print every output on screen."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "75f016c5-2f76-4daf-9476-dd2ab0b27d7e",
   "metadata": {},
   "outputs": [],
   "source": [
    "from simframe import writers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "08ba0649-d33a-4ea0-8d82-123ef4f52991",
   "metadata": {},
   "outputs": [],
   "source": [
    "sim.writer = writers.namespacewriter\n",
    "sim.writer.verbosity = 0"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f445ce3c-8c19-4b5b-9f71-6a9067ab1822",
   "metadata": {},
   "source": [
    "Before we start the simulation we have to update the frame to calculate the initial values of the reproduction numbers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "957f1f6c-c3cc-4259-8978-3b8dc98d2c3e",
   "metadata": {},
   "outputs": [],
   "source": [
    "sim.update()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c1e4b8b1-25d4-4608-946e-fac7bc2a9ce0",
   "metadata": {},
   "source": [
    "The complete structure of the model looks like this"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "44669b16-ea40-43a4-87f9-cb7900e7f546",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[94mFrame (SIRD Model)\u001b[0m\n",
      "    - \u001b[94mcomp\u001b[0m: \u001b[94mGroup (Model Compartments)\u001b[0m\n",
      "        - \u001b[94mD\u001b[0m: Field (Deceased)\n",
      "        - \u001b[94mI\u001b[0m: Field (Infectious)\n",
      "        - \u001b[94mR\u001b[0m: Field (Recovered)\n",
      "        - \u001b[94mS\u001b[0m: Field (Susceptible)\n",
      "    - \u001b[94mpars\u001b[0m: \u001b[94mGroup (Model Parameters)\u001b[0m\n",
      "        - \u001b[94malpha\u001b[0m: Field (Transmission probability)\n",
      "        - \u001b[94mbeta\u001b[0m: Field (Recovery probability)\n",
      "        - \u001b[94mN\u001b[0m: Field (Total population size)\n",
      "        - \u001b[94mR0\u001b[0m: Field (Basic reproduction number)\n",
      "        - \u001b[94mR1\u001b[0m: Field (Contact rate)\n",
      "        - \u001b[94mR2\u001b[0m: Field (Inverse recovery time)\n",
      "        - \u001b[94mR3\u001b[0m: Field (Inverse time until death)\n",
      "        - \u001b[94mRt\u001b[0m: Field (Time-dependent reproduction number)\n",
      "    - \u001b[94mt\u001b[0m: IntVar (Time [days]), \u001b[95mIntegration variable\u001b[0m\n",
      "    - \u001b[94mY\u001b[0m: Field (State Vector)\n"
     ]
    }
   ],
   "source": [
    "sim.toc"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b608a49c-35c9-4d0b-b799-6587949e1bc5",
   "metadata": {},
   "source": [
    "The simulation is now ready to run."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "abe506b4-c097-435a-8dfc-1628bb010a0d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Execution time: \u001b[94m0:00:02\u001b[0m\n"
     ]
    }
   ],
   "source": [
    "sim.run()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d0dc9670-04c0-4fef-8786-ea609e1ee073",
   "metadata": {},
   "source": [
    "We can now read the data and plot the results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "488df91e-bfb7-47d1-99dc-42be07e8f841",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = sim.writer.read.all()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "43c7a1c2-e5d1-407e-a9a0-2208ada47465",
   "metadata": {},
   "outputs": [],
   "source": [
    "from matplotlib import pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "3fb73fa7-012c-4775-8f23-3367ddab74d4",
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_model(data):\n",
    "    fig, ax = plt.subplots(dpi=150.)\n",
    "    for c in data.comp.__dict__:\n",
    "        ax.plot(data.t, data.comp.__dict__[c]/data.pars.N, label=c)\n",
    "    ax.plot(0., 0., \":\", c=\"black\", label=\"$R_t$\")\n",
    "    ax.legend(loc=\"upper right\")\n",
    "    ax.set_xlim(data.t[0], data.t[-1])\n",
    "    ax.set_ylim(-0.1, 1.1)\n",
    "    ax.set_xlabel(\"Time [days]\")\n",
    "    ax.set_ylabel(\"Compartment fraction\")\n",
    "    axr = ax.twinx()\n",
    "    axr.plot(data.t, data.pars.Rt, \":\", c=\"black\")\n",
    "    axr.axhline(1., ls=\"--\", lw=1, c=\"black\", alpha=0.25, zorder=-1)\n",
    "    axr.axhline(data.pars.R0[0], ls=\"--\", lw=1, c=\"black\", alpha=0.25, zorder=-1)\n",
    "    axr.set_ylabel(\"Reproduction number\")\n",
    "    axr.set_ylim(0., sim.pars.R0[0]+0.25)\n",
    "    fig.tight_layout()\n",
    "    return fig, ax"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "7b242e76-05fb-49bb-965d-e31409b0e82d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 900x600 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plot_model(data)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e698ab67-87a6-4171-b964-52f376ac47a4",
   "metadata": {},
   "source": [
    "In addition to the compartments we plot the reproduction number and horizontal lines for values of reproduction numbers for $1$ and the base reproduction number $R_0$. If the reproduction number is smaller than $1$, the population of the infectious compartment decreases, which can be easily seen in the plot."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f03481b0-dbc0-4106-a7dc-a94ee859e924",
   "metadata": {},
   "source": [
    "We can also print the final state on screen"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "357d1315-eedf-408c-9e9a-beeece550a28",
   "metadata": {},
   "outputs": [],
   "source": [
    "def print_state(sim):\n",
    "    for c in sim.comp.__dict__:\n",
    "        if c.startswith(\"_\"): continue\n",
    "        print(\"{:}: {:5.1f} %\".format(c, sim.comp.__dict__[c]/sim.pars.N*100.))\n",
    "    print(\"Rt: {:5.2f}\".format(sim.pars.Rt))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "bc956490-bc35-4719-9c19-34c65719ab4a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "S:   3.4 %\n",
      "I:   0.0 %\n",
      "R:  93.7 %\n",
      "D:   2.9 %\n",
      "Rt:  0.12\n"
     ]
    }
   ],
   "source": [
    "print_state(sim)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "71916f00-da4b-4ce8-a52d-a79bd5917b9e",
   "metadata": {},
   "source": [
    "At the end of the simulation about 2.9 % of the total population is deceased. This is very close to $\\beta$ as it should be, since almost the entire population got infected by the end of the simulation."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8329bafe-b58f-4972-84f8-83c2f2e7b8f9",
   "metadata": {},
   "source": [
    "At the peak of the infection wave about $100$ days after patient zero about $36\\,\\%$ of the population is infectious."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "a14a1933-a747-4f3f-b20b-e5080a2e2fe6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "I_max: 35.6 %\n"
     ]
    }
   ],
   "source": [
    "print(\"I_max: {:4.1f} %\".format((data.comp.I/data.pars.N).max()*100.))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "de2d57bd-bcfd-4390-96d4-cfe5ed0bbe05",
   "metadata": {},
   "source": [
    "So far our model parameters have been constant with time. But let us assume the following situation.\n",
    "\n",
    "About $6\\,\\%$ of the people infected with our fictitious disease need to be treated in an intensive care unit (ICU). That means that at the peak of the wave the ICUs needed a capacity of about $2\\,\\%$ of the total population. Typical OECD countries have about $10$ ICU beds per $100~000$ population. In our scenario the health care system would be completely overwhelmed by the critial patients potentially increasing the chance of dying because of insufficiant medical care.\n",
    "\n",
    "We are going to run the simulation again assuming that $6\\,\\%$ of the infectious require intensive care and further assuming that those patients without a needed ICU bed have a $30$ times higher chance of dying. We can achieve this by increasing our $\\beta$ parameter accordingly and calculate an effective $\\beta$ parameter.\n",
    "\n",
    "$\\beta = \\beta_0 \\left[ 1 + \\frac{I_\\mathrm{w/o}}{I} \\left( f-1 \\right) \\right]$\n",
    "\n",
    "with $\\beta_0$ being the unmodified mortality, $I_\\mathrm{w/o}$ the number of infected without an ICU bed that need one, and $f$ a factor that increases the mortality for the latter."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "a3d76367-3fe5-46f3-831b-1ddf109e093d",
   "metadata": {},
   "outputs": [],
   "source": [
    "def f_beta_effective(sim):\n",
    "    \n",
    "    N = sim.pars.N\n",
    "    I = sim.comp.I\n",
    "    \n",
    "    # Maximum number of ICU beds\n",
    "    max_icu = 10./100_000. * N\n",
    "    # Infected requiring ICU bed\n",
    "    icu_req = 0.06*I\n",
    "    # Patients without ICU bed\n",
    "    I_wo = np.maximum(icu_req - max_icu, 0.)\n",
    "    # Increase in mortality\n",
    "    f = 30.\n",
    "    \n",
    "    return beta*(1+I_wo/I*(f-1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "180a2140-1c0d-486e-9c88-6e79af74f731",
   "metadata": {},
   "outputs": [],
   "source": [
    "sim.pars.beta.updater = f_beta_effective"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1e02c4db-8266-4554-bd74-698e2d37ee09",
   "metadata": {},
   "source": [
    "We then have to add the $\\beta$ parameters to the update order of the parameters group."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "056e6c90-3ab6-4060-bfa1-f99541dbf38e",
   "metadata": {},
   "outputs": [],
   "source": [
    "sim.pars.updater = sim.pars.updateorder + [\"beta\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cada7e64-708e-49a3-8a04-c5915ad90169",
   "metadata": {},
   "source": [
    "We can now reset the simulation and run it again."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "85b9164c-3241-47b9-9073-12e23c178b9c",
   "metadata": {},
   "outputs": [],
   "source": [
    "sim.Y = Y0\n",
    "sim.t = 0.\n",
    "sim.update()\n",
    "sim.writer.reset()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "42cf54cf-bdbd-4cfa-817f-c1cd243e087c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Execution time: \u001b[94m0:00:01\u001b[0m\n"
     ]
    }
   ],
   "source": [
    "sim.run()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "5b1bb896-debf-4bac-9e8a-fedbb6d0d13f",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_icu = sim.writer.read.all()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "a0c2630c-6d59-48f2-90d1-0ce56173ba06",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 900x600 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plot_model(data_icu)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "a04c61f6-fca2-41dc-8d1f-1191105e12ea",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "S:   3.4 %\n",
      "I:   0.0 %\n",
      "R:  88.8 %\n",
      "D:   7.8 %\n",
      "Rt:  0.12\n"
     ]
    }
   ],
   "source": [
    "print_state(sim)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "81e2a1af-30ae-430f-8b45-3644f27fe6c3",
   "metadata": {},
   "source": [
    "As can be seen the fraction of deceased is with almost $8\\,\\%$ more than twice as large as before, which accounts in our case to almost $500~000$ additional casualties."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "342a5d92-8aa4-48a9-be37-7358d839add0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Additional casualties: 490000\n"
     ]
    }
   ],
   "source": [
    "print(\"Additional casualties: {:d}\".format(int(N*(0.078-0.029))))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7faf8e59-b28e-4afb-a30b-a111f1f4e15c",
   "metadata": {},
   "source": [
    "We can now further assume that some fictitious government implements some kind of contact restrictions as soon as the the ICU occupancy reaches a certain threshold. We can implement this in our model by reducing our $R_1$ parameter – the contact rate – accordingly. We assume that contact restrictions are implemented as soon as the ICU occupancy reaches $90\\,\\%$, which reduces the contact rate to $50\\,\\%$ of the initial value, and are lifted again as soon as the ICU occupation drops below $50\\,\\%$ again. We have therefore two thresholds and we need to know if restrictions are in place when crossing them. We are going to introduce a boolean parameter that stores if contact restrictions are in place or not."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "201aa757-2105-43de-86e5-e455b7009208",
   "metadata": {},
   "outputs": [],
   "source": [
    "sim.pars.addfield(\"contact_restrictions\", False, description=\"Contact Restrictions\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "a9495408-1689-45cd-b23d-d90b0cf0a394",
   "metadata": {},
   "outputs": [],
   "source": [
    "def f_R1(sim):\n",
    "    \n",
    "    N = sim.pars.N\n",
    "    I = sim.comp.I\n",
    "    restr = sim.pars.contact_restrictions\n",
    "    \n",
    "    # Maximum number ICU beds\n",
    "    max_icu = 10./100_000. * N\n",
    "    # Infected requiring ICU bed\n",
    "    icu_req = 0.06*I\n",
    "    # ICU occupation\n",
    "    icu_occup = icu_req/max_icu\n",
    "    \n",
    "    # Lower and upper thresholds\n",
    "    threshold = (0.5, 0.9)\n",
    "    \n",
    "    # Check if the restriction status changes\n",
    "    # If restrictions are in place and occupancy falls below threshold or\n",
    "    # if restrictions are not in place and occupancy goes above threshold\n",
    "    # we flip the contact restriction state\n",
    "    if (restr and icu_occup<threshold[0]) or (not restr and icu_occup>threshold[1]):\n",
    "        sim.pars.contact_restrictions = not restr\n",
    "        \n",
    "    # If we are restricted return lower contact rate\n",
    "    if restr:\n",
    "        return 0.5*R1\n",
    "    \n",
    "    # If we are not restriction return unperturbed contact rate\n",
    "    return R1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "a0bbfb28-5ceb-418f-a08d-1235e8987193",
   "metadata": {},
   "outputs": [],
   "source": [
    "sim.pars.R1.updater = f_R1"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c03df9be-eb72-42b6-81bd-d06b4c2b01d9",
   "metadata": {},
   "source": [
    "And we have to add the updater to the list. The updater has to be inserted before the reproduction numbers, since they depend on $R_1$."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "0f30444f-a8b6-42f4-b907-11768f3212a4",
   "metadata": {},
   "outputs": [],
   "source": [
    "sim.pars.updater = [\"R1\"] + sim.pars.updateorder"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "39fcc517-5943-408d-9314-dfe8b4093659",
   "metadata": {},
   "source": [
    "We can reset the simulation and run it again."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "1115b0a6-57ac-4dae-9b79-52241a5c6964",
   "metadata": {},
   "outputs": [],
   "source": [
    "sim.Y = Y0\n",
    "sim.t = 0.\n",
    "sim.update()\n",
    "sim.writer.reset()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "242ea530-0546-47b9-a346-2bbf387c027b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Execution time: \u001b[94m0:00:01\u001b[0m\n"
     ]
    }
   ],
   "source": [
    "sim.run()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "45ad656d-3217-4167-8d2e-29960b3d2020",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_restr = sim.writer.read.all()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "17e8afa0-9e39-4f80-b6c1-48f94478b183",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 900x600 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plot_model(data_restr)\n",
    "i0 = data_restr.pars.contact_restrictions.argmax()\n",
    "i1 = len(data.t)-data_restr.pars.contact_restrictions[::-1].argmax()-1\n",
    "ax.axvspan(data_restr.t[i0], data_restr.t[i1], edgecolor=\"#000000\", facecolor=\"None\", hatch=\"//\", alpha=0.2)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "38c22df0-a047-4625-8806-616d351db546",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "S:  28.6 %\n",
      "I:   0.1 %\n",
      "R:  65.7 %\n",
      "D:   5.7 %\n",
      "Rt:  1.00\n"
     ]
    }
   ],
   "source": [
    "print_state(sim)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5e5f3b49-f695-4b02-be4d-e6ddc82ca59f",
   "metadata": {},
   "source": [
    "It is immeadiately visible that the peak of the infectious compartment is at only $11\\,\\%$ as opposed to the $36\\,\\%$ without restrictions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "ce4ddd7e-2521-49bf-8aa5-0bdee8d4cc45",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "I_max: 10.9 %\n"
     ]
    }
   ],
   "source": [
    "print(\"I_max: {:4.1f} %\".format((data_restr.comp.I/data_restr.pars.N).max()*100.))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b6a51158-0654-472f-aea9-16ba466943e3",
   "metadata": {},
   "source": [
    "The contact restrictions – the shaded region – are in place for about $330$ days."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "685b1953-105d-4a54-860a-4a71d1a56f86",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Contact restrictions in place for 331 days.\n"
     ]
    }
   ],
   "source": [
    "i0 = data_restr.pars.contact_restrictions.argmax()\n",
    "i1 = len(data_restr.pars.contact_restrictions) - data_restr.pars.contact_restrictions[::-1].argmax() - 1\n",
    "print(\"Contact restrictions in place for {:d} days.\".format(int(data_restr.t[i1]-data_restr.t[0])))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "152ec784-2cfc-453c-b029-800c8fa2ad92",
   "metadata": {},
   "source": [
    "At the end of the simulation the fraction of deceased is at about $6\\,\\%$. Compared to our simulation without restrictions about $210~000$ lives were saved."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "12fed7ea-95be-4cd5-ac9b-545b1bb7111a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Lives saved by contact restrictions: 209999\n"
     ]
    }
   ],
   "source": [
    "print(\"Lives saved by contact restrictions: {:d}\".format(int(N*(0.078-0.057))))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "95cb43df-964f-4cca-a1e0-4251364d4d42",
   "metadata": {},
   "source": [
    "After the contact restrictions have been lifted, the reproduction number is stable at one, meaning the spread of the disease is contained."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "64176ceb-d2ac-4451-bbb6-78d54bd58545",
   "metadata": {},
   "source": [
    "This model could also be used in the opposite direction. If the structured of the compartmental model is known, the model could be fitted to the data to get the disease parameters."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "45029d7f-4032-417d-8db3-7c78bd586ee0",
   "metadata": {},
   "source": [
    "## SECIR Model"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c1e1783a-a6cb-4164-acc0-c7c50d22d383",
   "metadata": {},
   "source": [
    "As a second example we want to discuss the SECIR model described by [Khailaie et al. (2020)](https://doi.org/10.1101/2020.04.04.20053637) to model the outbreak of SARS-CoV-2.\n",
    "\n",
    "The model has eight compartments: $S$usceptible, $E$xposed, $C$arrier, $I$nfected, $R$ecovered, $H$ospitalized, Intensive Care $U$nit, and $D$ead. Compartments $C$ and $I$ are infectious and can infect individuals from compartment $S$. The differential equations governing the system are given by\n",
    "\n",
    "$\\frac{\\mathrm{d}}{\\mathrm{d}t} S = - R_1 \\frac{C + \\beta I}{N} S$\n",
    "\n",
    "$\\frac{\\mathrm{d}}{\\mathrm{d}t} E =  R_1 \\frac{C + \\beta I}{N} S - R_2 E$\n",
    "\n",
    "$\\frac{\\mathrm{d}}{\\mathrm{d}t} C =  R_2 E - \\left[\\left(1 - \\alpha\\right)R_3 + \\alpha R_9 \\right] C$\n",
    "\n",
    "$\\frac{\\mathrm{d}}{\\mathrm{d}t} I = \\left(1 - \\alpha\\right) R_3 C - \\left[\\left(1-\\rho\\right) R_4 + \\rho R_6 \\right] I$\n",
    "\n",
    "$\\frac{\\mathrm{d}}{\\mathrm{d}t} H = \\rho R_6 I  - \\left[\\left(1-\\vartheta\\right) R_5 + \\vartheta R_7 \\right] H$\n",
    "\n",
    "$\\frac{\\mathrm{d}}{\\mathrm{d}t} U = \\vartheta R_7 H - \\left[\\left(1-\\delta\\right) R_8 + \\delta d \\right] U$\n",
    "\n",
    "$\\frac{\\mathrm{d}}{\\mathrm{d}t} R = \\alpha R_9 C + \\left(1-\\rho\\right) R_4 I + \\left(1-\\vartheta\\right) R_5 H + \\left(1-\\delta\\right) R_8 U$\n",
    "\n",
    "$\\frac{\\mathrm{d}}{\\mathrm{d}t} D = \\delta d U$"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0768ab9f-1d25-464a-8ff4-e38b0b0e145b",
   "metadata": {},
   "source": [
    "The system can be graphically represented with [this chart](https://www.medrxiv.org/content/medrxiv/early/2020/04/07/2020.04.04.20053637/F1.large.jpg) from [Khailaie et al. (2020)](https://doi.org/10.1101/2020.04.04.20053637):"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6c2a9b9c-259c-42f8-8922-51a38eb703e4",
   "metadata": {},
   "source": [
    "$R_1$ is the product of median contact frequency and the transmission probability per contact of a susceptible $\\left(S\\right)$ and an infectious ($C$ or $I$) person. $\\beta < 1$ represents the combined risk attenuation factor of infection from symptomatic patients and patients that have not self-isolated effectively, yet. $\\frac{1}{R_2}$ is the median time until an exposed person becomes infectious. $\\frac{1}{R_2}+\\frac{1}{R_3}$ is the incubation period. $\\alpha$ is the fraction of asymptomatic cases.\n",
    "\n",
    "$\\rho$ is the fraction of symptomatic cases that require to be hopitalized. $\\frac{1}{R_4}$ is the median time until a symptomatic case recovers. $\\frac{1}{R_6}$ is the median time until a symptomatic case gets admitted to the hospital. $\\vartheta$ is the fraction of hospitalized patients that require intesive care. And $\\delta$ is the fraction of intensive care patients that die. $\\frac{1}{R_5}$ and $\\frac{1}{R_8}$ are the median times until hospitalized patients and intensive care patients recover. $\\frac{1}{R_9}$ is the median time an asymptomatic case remains infectious. And $\\frac{1}{d}$ is the median time until intensive care patients die."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "344239ac-9685-4352-bb32-557c0f2f9645",
   "metadata": {},
   "source": [
    "In this model the basic reproduction number is given by\n",
    "\n",
    "$R_0 = R_1 \\frac{R_4\\left(1-\\rho\\right) + R_3\\beta\\left(1-\\alpha\\right) + R_6\\rho}{\\left[ R_3\\left(1-\\alpha\\right)+R_9\\alpha \\right] \\left[ R_4\\left(1-\\rho\\right)+R_6\\rho \\right]}$.\n",
    "\n",
    "The time-dependent reproduction number is given by \n",
    "\n",
    "$R_t = R_0\\frac{S}{N}$"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d54796da-75bc-4010-95f1-5f10628423df",
   "metadata": {},
   "source": [
    "These parameters can all vary with time, country, culture, and healthcare system. [Khailaie et al. (2020)](https://doi.org/10.1101/2020.04.04.20053637) used parameters from a fit to early data from Italy.\n",
    "\n",
    "We are going to use the following values in this example\n",
    "\n",
    "* $R_1 = 0.587\\,\\mathrm{days}$\n",
    "* $\\frac{1}{R_2} = 5.2\\,\\mathrm{days} - \\frac{1}{R_3}$ (Incubation period is $5.2\\,\\mathrm{days}$)\n",
    "* $R_3 = \\frac{1}{4.2\\,\\mathrm{days}}$\n",
    "* $R_4 = \\frac{1}{14\\,\\mathrm{days}}$\n",
    "* $R_5 = \\frac{1}{16\\,\\mathrm{days}}$\n",
    "* $R_6 = \\frac{1}{2.5\\,\\mathrm{days}}$\n",
    "* $R_7 = \\frac{1}{3.5\\,\\mathrm{days}}$\n",
    "* $R_8 = \\frac{1}{16\\,\\mathrm{days}}$\n",
    "* $\\frac{1}{R_9} = \\frac{1}{R_3} + \\frac{1}{2R_4}$\n",
    "* $d = \\frac{1}{6.92\\,\\mathrm{days}}$\n",
    "* $\\alpha = 0.01$\n",
    "* $\\beta = 0.05$\n",
    "* $\\delta = 0.15$\n",
    "* $\\vartheta = 0.15$\n",
    "* $\\delta = 0.35$"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e32cec49-4b2e-48dd-ba7b-1aa81fde4705",
   "metadata": {},
   "source": [
    "As before we are going to set up the model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "50f0e046-22a4-476e-a5c6-18116121137e",
   "metadata": {},
   "outputs": [],
   "source": [
    "sim = Frame(description=\"Khailaie et al. (2020)\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "22de7453-c768-48a6-a1d0-a402743f47a1",
   "metadata": {},
   "outputs": [],
   "source": [
    "sim.addgroup(\"pars\", description=\"Model parameters\")\n",
    "sim.pars.addfield(\"N\", 0., description=\"Population size\")\n",
    "sim.pars.addfield(\"R0\", 0., description=\"Basic reproduction number\")\n",
    "sim.pars.addfield(\"Rt\", 0., description=\"Time-dependent reproduction number\")\n",
    "sim.pars.addfield(\"R1\", 0., description=\"S -> E\")\n",
    "sim.pars.addfield(\"R2\", 0., description=\"E -> C\")\n",
    "sim.pars.addfield(\"R3\", 0., description=\"C -> I\")\n",
    "sim.pars.addfield(\"R4\", 0., description=\"I -> R\")\n",
    "sim.pars.addfield(\"R5\", 0., description=\"H -> R\")\n",
    "sim.pars.addfield(\"R6\", 0., description=\"I -> H\")\n",
    "sim.pars.addfield(\"R7\", 0., description=\"H -> U\")\n",
    "sim.pars.addfield(\"R8\", 0., description=\"U -> R\")\n",
    "sim.pars.addfield(\"R9\", 0., description=\"C -> R\")\n",
    "sim.pars.addfield(\"d\", 0., description=\"U -> D\")\n",
    "sim.pars.addfield(\"alpha\", 0., description=\"Probability of asymptotic case\")\n",
    "sim.pars.addfield(\"beta\", 0., description=\"Infection attenuation\")\n",
    "sim.pars.addfield(\"delta\", 0., description=\"Mortality\")\n",
    "sim.pars.addfield(\"theta\", 0., description=\"ICU probability\")\n",
    "sim.pars.addfield(\"rho\", 0., description=\"Hospitalization probability\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "bd16bdd3-660c-41d1-980f-647232c5149b",
   "metadata": {},
   "outputs": [],
   "source": [
    "sim.pars.N = 10_000_000.\n",
    "sim.pars.R1 = 0.587\n",
    "sim.pars.R3 = 1./4.2\n",
    "sim.pars.R4 = 1./14.\n",
    "sim.pars.R5 = 1./16.\n",
    "sim.pars.R6 = 1./2.5\n",
    "sim.pars.R7 = 1./3.5\n",
    "sim.pars.R8 = 1./16.\n",
    "sim.pars.d = 1./6.92\n",
    "sim.pars.alpha = 0.01\n",
    "sim.pars.beta = 0.05\n",
    "sim.pars.delta = 0.15\n",
    "sim.pars.theta = 0.15\n",
    "sim.pars.rho = 0.35"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "24da0d82-a655-4cc2-a8b5-caf5d0361ace",
   "metadata": {},
   "source": [
    "We define updater for some of the parameters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "33f471dd-018c-4a46-ad8a-fb361f47030b",
   "metadata": {},
   "outputs": [],
   "source": [
    "def f_N(sim):\n",
    "    return sim.Y.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "3bdf48f3-d503-4a6e-beb8-ccca884c2995",
   "metadata": {},
   "outputs": [],
   "source": [
    "def f_R0(sim):\n",
    "    R1, R3, R4, R6, R9 = sim.pars.R1, sim.pars.R3, sim.pars.R4, sim.pars.R6, sim.pars.R9\n",
    "    alpha, beta, rho = sim.pars.alpha, sim.pars.beta, sim.pars.rho\n",
    "    return R1 * (R4*(1.-rho) + R3*beta*(1.-alpha) + R6*rho) / (R3*(1.-alpha) + R9*alpha) / (R4*(1.-rho) + R6*rho)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "b3f5bb51-c270-41e4-b20b-e4b978928d0f",
   "metadata": {},
   "outputs": [],
   "source": [
    "def f_Rt(sim):\n",
    "    return sim.pars.R0*sim.comp.S/sim.pars.N"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "6bdd2d58-e963-45f8-b0f1-743e0769e96b",
   "metadata": {},
   "outputs": [],
   "source": [
    "def f_R2(sim):\n",
    "    inv = 5.2 - 1./sim.pars.R3\n",
    "    return 1./inv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "dface461-5ea4-4499-b08b-6025d3de6c91",
   "metadata": {},
   "outputs": [],
   "source": [
    "def f_R9(sim):\n",
    "    inv = 1./sim.pars.R3 + 0.5/sim.pars.R4\n",
    "    return 1./inv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "2e1ceaa4-d25a-4cc9-b603-4cfe14d6eaac",
   "metadata": {},
   "outputs": [],
   "source": [
    "sim.pars.N.updater = f_N\n",
    "sim.pars.R0.updater = f_R0\n",
    "sim.pars.Rt.updater = f_Rt\n",
    "sim.pars.R2.updater = f_R2\n",
    "sim.pars.R9.updater = f_R9"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "659ad165-b509-4f27-87ba-9fc0e387f95d",
   "metadata": {},
   "outputs": [],
   "source": [
    "sim.pars.updater = [\"N\", \"R2\", \"R9\", \"R0\", \"Rt\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a492c232-caf4-4619-8cd5-d42302975e21",
   "metadata": {},
   "source": [
    "Again we store the comnpartments in the state vecotr $\\vec{Y}$."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "e9b36bc2-e276-4750-a97a-b77781878a8f",
   "metadata": {},
   "outputs": [],
   "source": [
    "Y = [sim.pars.N[0]-1, 0., 1., 0., 0., 0., 0., 0.]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "5db5fd09-89b0-4ec7-adc5-684174dc9632",
   "metadata": {},
   "outputs": [],
   "source": [
    "sim.addfield(\"Y\", Y, description=\"State vector\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "2ada6b2b-d2ae-4261-afe9-39a52154aad8",
   "metadata": {},
   "outputs": [],
   "source": [
    "sim.addgroup(\"comp\", description=\"Compartments\")\n",
    "sim.comp.addfield(\"S\", 0., description=\"Susceptible\")\n",
    "sim.comp.addfield(\"E\", 0., description=\"Exposed\")\n",
    "sim.comp.addfield(\"C\", 0., description=\"Carrier\")\n",
    "sim.comp.addfield(\"I\", 0., description=\"Infected\")\n",
    "sim.comp.addfield(\"H\", 0., description=\"Hospitalized\")\n",
    "sim.comp.addfield(\"U\", 0., description=\"Intensive Care Unit\")\n",
    "sim.comp.addfield(\"R\", 0., description=\"Recovered\")\n",
    "sim.comp.addfield(\"D\", 0., description=\"Dead\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "86078890-7703-4449-80e9-376a9bd7267e",
   "metadata": {},
   "source": [
    "And just copy their values into the compartments group in the systole."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "b959ce1f-704d-4dca-a48e-b227ee30f989",
   "metadata": {},
   "outputs": [],
   "source": [
    "def f_comp_systole(sim):\n",
    "    sim.comp.S = sim.Y[0]\n",
    "    sim.comp.E = sim.Y[1]\n",
    "    sim.comp.C = sim.Y[2]\n",
    "    sim.comp.I = sim.Y[3]\n",
    "    sim.comp.H = sim.Y[4]\n",
    "    sim.comp.U = sim.Y[5]\n",
    "    sim.comp.R = sim.Y[6]\n",
    "    sim.comp.D = sim.Y[7]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "fc14e13d-5407-42ef-91b3-975adbed6762",
   "metadata": {},
   "outputs": [],
   "source": [
    "sim.comp.updater.systole = f_comp_systole"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "5d317f52-4975-4f97-b3b1-296aae699114",
   "metadata": {},
   "outputs": [],
   "source": [
    "sim.updater = [\"comp\", \"pars\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9717cf6e-bb50-4d15-a4f1-02ec07f47fd1",
   "metadata": {},
   "source": [
    "The function with the differential equations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "7e38005d-bcde-4d5c-b22b-5ff47e1badb2",
   "metadata": {},
   "outputs": [],
   "source": [
    "def f_dYdt(sim, x, Y):\n",
    "    \n",
    "    S, E, C, I, H, U, R, D = Y\n",
    "    alpha, beta, delta, theta, rho = sim.pars.alpha, sim.pars.beta, sim.pars.delta, sim.pars.theta, sim.pars.rho\n",
    "    N, d = sim.pars.N, sim.pars.d\n",
    "    R1, R2, R3, R4, R5, R6, R7, R8, R9 = sim.pars.R1, sim.pars.R2, sim.pars.R3, sim.pars.R4, sim.pars.R5, sim.pars.R6, sim.pars.R7, sim.pars.R8, sim.pars.R9\n",
    "    \n",
    "    dY = np.zeros_like(Y)\n",
    "    \n",
    "    dY[0] = -R1*(C+beta*I)*S/N\n",
    "    dY[1] = -dY[0] - R2*E\n",
    "    dY[2] = R2*E - ((1.-alpha)*R3 + alpha*R9)*C\n",
    "    dY[3] = (1.-alpha)*R3*C - ((1.-rho)*R4+rho*R6)*I\n",
    "    dY[4] = rho*R6*I - ((1.-theta)*R5+theta*R7)*H\n",
    "    dY[5] = theta*R7*H - ((1.-delta)*R8+delta*d)*U\n",
    "    dY[6] = alpha*R9*C + (1.-rho)*R4*I + (1.-theta)*R5*H + (1.-delta)*R8*U\n",
    "    dY[7] = -dY.sum()  # Since the total population size is constant, the last comaprtment is not independent\n",
    "    \n",
    "    return dY"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "e8aad400-5020-4292-b0da-e33bb925659c",
   "metadata": {},
   "outputs": [],
   "source": [
    "sim.Y.differentiator = f_dYdt"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6ed8c0e9-54b7-4ac3-b38f-567187314a32",
   "metadata": {},
   "source": [
    "And the integration variable. We are going to simulate the model for $180$ days."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "abff4684-1e85-45fc-9b4c-601ae32fb46e",
   "metadata": {},
   "outputs": [],
   "source": [
    "sim.addintegrationvariable(\"t\", 0., description=\"Time [days]\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "b29d8cb3-3701-440a-a1c7-64aa981c7bbb",
   "metadata": {},
   "outputs": [],
   "source": [
    "sim.t.snapshots = np.arange(1., 181., 1.)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c29f3ddf-e269-41aa-b81d-f1b9ac8b362f",
   "metadata": {},
   "source": [
    "We are not going to define a custom function for the time step, that ensures that the compartments are always inbound. Instead we are using an adaptive integration scheme. Therefore the time step updater needs to return the suggested time step."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "6285f9ca-17ae-4e49-bd4a-1700d0210347",
   "metadata": {},
   "outputs": [],
   "source": [
    "def f_dt(sim):\n",
    "    return sim.t.suggested"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "0645c348-39cb-4a4f-95b2-08b7799d28be",
   "metadata": {},
   "outputs": [],
   "source": [
    "sim.t.updater = f_dt"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "459bdc0b-0f48-4e21-8f2b-ea448a977e94",
   "metadata": {},
   "source": [
    "And we initialize it with a value of $1$ day."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "f866f646-0a05-4b0c-9729-d16283f608b8",
   "metadata": {},
   "outputs": [],
   "source": [
    "sim.t.suggest(1.)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bff2c192-915c-4cc9-a218-bda0e491b23b",
   "metadata": {},
   "source": [
    "Then we define the integrator with a single integration instruction."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "12450de6-62ed-49b9-a21e-f9feb8c66a39",
   "metadata": {},
   "outputs": [],
   "source": [
    "sim.integrator = Integrator(sim.t)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "cfdb49e3-51fd-4cd3-8175-4f127f3f7da9",
   "metadata": {},
   "outputs": [],
   "source": [
    "sim.integrator.instructions = [\n",
    "    Instruction(schemes.expl_5_cash_karp_adptv, sim.Y)\n",
    "]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "024b9850-723e-41da-ba38-bc503c01498d",
   "metadata": {},
   "source": [
    "We are using the namespace writer and turn off the verbosity."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "f29235e3-cef8-4f51-9de9-4d7c2a601fde",
   "metadata": {},
   "outputs": [],
   "source": [
    "sim.writer = writers.namespacewriter()\n",
    "sim.writer.verbosity = 0"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "32f8b312-790f-4200-a2a1-97b9f9e4d74a",
   "metadata": {},
   "source": [
    "The model is now complete."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "ac76d03d-8caf-43fd-9887-fae4db4f7136",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[94mFrame (Khailaie et al. (2020))\u001b[0m\n",
      "    - \u001b[94mcomp\u001b[0m: \u001b[94mGroup (Compartments)\u001b[0m\n",
      "        - \u001b[94mC\u001b[0m: Field (Carrier)\n",
      "        - \u001b[94mD\u001b[0m: Field (Dead)\n",
      "        - \u001b[94mE\u001b[0m: Field (Exposed)\n",
      "        - \u001b[94mH\u001b[0m: Field (Hospitalized)\n",
      "        - \u001b[94mI\u001b[0m: Field (Infected)\n",
      "        - \u001b[94mR\u001b[0m: Field (Recovered)\n",
      "        - \u001b[94mS\u001b[0m: Field (Susceptible)\n",
      "        - \u001b[94mU\u001b[0m: Field (Intensive Care Unit)\n",
      "    - \u001b[94mpars\u001b[0m: \u001b[94mGroup (Model parameters)\u001b[0m\n",
      "        - \u001b[94malpha\u001b[0m: Field (Probability of asymptotic case)\n",
      "        - \u001b[94mbeta\u001b[0m: Field (Infection attenuation)\n",
      "        - \u001b[94md\u001b[0m: Field (U -> D)\n",
      "        - \u001b[94mdelta\u001b[0m: Field (Mortality)\n",
      "        - \u001b[94mN\u001b[0m: Field (Population size)\n",
      "        - \u001b[94mR0\u001b[0m: Field (Basic reproduction number)\n",
      "        - \u001b[94mR1\u001b[0m: Field (S -> E)\n",
      "        - \u001b[94mR2\u001b[0m: Field (E -> C)\n",
      "        - \u001b[94mR3\u001b[0m: Field (C -> I)\n",
      "        - \u001b[94mR4\u001b[0m: Field (I -> R)\n",
      "        - \u001b[94mR5\u001b[0m: Field (H -> R)\n",
      "        - \u001b[94mR6\u001b[0m: Field (I -> H)\n",
      "        - \u001b[94mR7\u001b[0m: Field (H -> U)\n",
      "        - \u001b[94mR8\u001b[0m: Field (U -> R)\n",
      "        - \u001b[94mR9\u001b[0m: Field (C -> R)\n",
      "        - \u001b[94mrho\u001b[0m: Field (Hospitalization probability)\n",
      "        - \u001b[94mRt\u001b[0m: Field (Time-dependent reproduction number)\n",
      "        - \u001b[94mtheta\u001b[0m: Field (ICU probability)\n",
      "    - \u001b[94mt\u001b[0m: IntVar (Time [days]), \u001b[95mIntegration variable\u001b[0m\n",
      "    - \u001b[94mY\u001b[0m: Field (State vector)\n"
     ]
    }
   ],
   "source": [
    "sim.toc"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "94761f8f-215c-4eca-9dfa-175ad0ccbef0",
   "metadata": {},
   "source": [
    "Before we can run the simulation we have to update the simulation frame."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "d27906c3-4ac3-4778-95ec-0961b5047118",
   "metadata": {},
   "outputs": [],
   "source": [
    "sim.update()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "63e675fd-2050-4fd1-95f6-86e446742a95",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Execution time: \u001b[94m0:00:01\u001b[0m\n"
     ]
    }
   ],
   "source": [
    "sim.run()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4e315fb0-3f3f-4f61-8ca6-bd8bc7b42b35",
   "metadata": {},
   "source": [
    "After the simulation we can plot the results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "6ed8443a-f60f-42c4-9df5-5c665a588e67",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = sim.writer.read.all()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "c3fd3a97-d1eb-4c31-83a4-b87a3a9fb3cf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 900x600 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plot_model(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "22583a48-7499-4a45-a9fe-142a76e9e726",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "S:   9.1 %\n",
      "E:   0.0 %\n",
      "C:   0.0 %\n",
      "I:   0.0 %\n",
      "H:   0.0 %\n",
      "U:   0.1 %\n",
      "R:  82.1 %\n",
      "D:   8.8 %\n",
      "Rt:  0.24\n"
     ]
    }
   ],
   "source": [
    "print_state(sim)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "63d5c61a-0379-4b81-997a-9daa3979a8bc",
   "metadata": {},
   "source": [
    "At the end of this simulation about $9\\,\\%$ of the population died."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "5dde7e41-8f07-4da2-8da8-8c466ba857d9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "I_max:  9.3 %\n"
     ]
    }
   ],
   "source": [
    "print(\"I_max: {:4.1f} %\".format((data.comp.U/data.pars.N).max()*100.))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "34228706-53f0-44b4-b327-4b4dd2313d5b",
   "metadata": {},
   "source": [
    "At the peak of the infection wave. At the peak of the wave about $9\\,\\%$ of the population would require intensive care overwhelming any healthcare system in the world."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cef27d4f-64cb-42ce-b8a1-292f55e9d10a",
   "metadata": {},
   "source": [
    "Please note that there are more complex models and updated parameters available by now."
   ]
  }
 ],
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