{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Simulation of Epidemological SIR-Model\n", "\n", "`English version last change on 2020-08-13; German version created on 2020-04-02; author: Olaf Behrendt`\n", "\n", "**Contents**\n", "\n", "* [Preface](#Preface)\n", "* [Introduction](#Introduction)\n", " * [An Illustrative First Example](#An-Illustrative-First-Example)\n", " * [Motivation of State Transition Rates](#motivation-of-state-transition-rates)\n", "* [Dynamics of the SIR-model](#Dynamics-of-the-SIR-model)\n", " * [System of Differential Equations](#System-of-Differential-Equations)\n", " * [Verbalization of SIR system of ODEs](#Verbalization-of-SIR-system-of-ODEs)\n", " * [Euler Method](#Euler-Method)\n", "* [Implementation of Euler's Method](#Implementation-of-Euler's-Method)\n", " * [Preparations](#Preparations)\n", " * [Calculate Time Step](#Calculate-Time-Step)\n", " * [Main Function and Plotting](#Main-Function-and-Plotting)\n", "* [Simulations](#Simulations)\n", " * [Parameters for SARS-CoV-2](#Parameters-for-SARS-CoV-2)\n", " * [Running the Simulations](#Running-the-Simulations)\n", " * [Summary of Scenario Simulations](Summary-of-Scenario-Simulations)\n", "* [Glossary](#Glossary)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Preface\n", "\n", "In the following presentation we introduce a classical model for the development of an infectious deseases, the SIR-model. It was first published in 1927 in the proceedings of the Royal Society by Scottish biochemist [William Ogilvy Kermack](https://en.wikipedia.org/wiki/William_Ogilvy_Kermack) and [Anderson Gray McKendrick](https://en.wikipedia.org/wiki/Anderson_Gray_McKendrick). McKendrick was a physican and did not have a mathematical degree, nervertheless he was a brilliant mathematician and pioneer in mathematical epidemiology. Apart from beeing a biochemist William Kermak also had a degree in mathematics. He survived the active service in the first world war, but unfortunately was blinded by a labratory experiment at the age of 26. \n", "\n", "I am trying to present the SIR-model in an most accessible way, so that anyone having had basic calculus at secondary school should have no difficulties. Specifically we some general familarity with simple equations, functions and elementary probability. But even if the technical hurdles are quite low, the reader probably has to spent some amount of time and concentration to follow the presentation. Anyways, if the reader has issues understanding the text, in doubt, the author has failed to clearly and consistently express his thoughts.\n", "\n", "An important part and probably the most \"enjoyable\" part of this notebook are the simulations using Python. The reader can change parameters and re-run simulation, so that she gets a better intuitive understanding of the quantitative nature of the model. The adventurous reader can even change or extend the SIR-model to his likings (e.g. add an compartmen/equation for the SEIR-model).\n", "\n", "## Introduction\n", "\n", "The SIR-model (**s**usceptible-**i**nfected-**r**emoved model) is a mathematical model describing the temporal devolpment of an infectious desease with immunization. An individual of the considered population of size $N$ resides in exactly one of three groups or compartments at a certain point of time:\n", "\n", "* $S(t)$ is the number of **susceptible** individuals at time point $t$. An individual is susceptible if she can be infected.\n", "* $I(t)$ is the number of **infected** individuals at time point $t$. \n", "* $R(t)$ is the number of **recovered** individuals at time point $t$. We assume that recovered individuals are **immune**, that is an recovered individual will remain in state _recovered_ forever.\n", "\n", "It is clear that the sum $S(t)+I(t)+R(t)=N$ is constant for all $t$ in the considered time range. Also note that we regard deceased individuals as _recovered_, which might seem a bit strange at first glance.\n", "\n", "### An Illustrative First Example\n", "\n", "At the start of the simulation $t=0$ we assume ten infected individuals in a population of size $N=1000$ (e.g. a village). So $S(0)=990$, $I(0)=10$, $R(0)=0$.\n", "Further we need some assumptions about the _contact rate_ $\\beta$ and _recovery rate_ $\\gamma$, two important terms we will shortly introduce in more detail.\n", "Please note that we cut of the digits after the decimal point, so we might observe some rounding errors.\n", "\n", "![](sir-ex1-en.png)\n", "\n", "$$\n", "\\begin{array}{r|r|r|r|r|r}\n", "\\text{day } t & S(t) & I(t) & R(t) & \\text{new infects}\\\\\n", "\\hline\n", " 0 & 990 & 10 & 0 & 0\\\\\n", " 1 & 980 & 17 & 3 & 10\\\\\n", " 2 & 964 & 27 & 9 & 16\\\\\n", " 3 & 938 & 44 & 18 & 26\\\\\n", " 4 & 896 & 71 & 33 & 42\\\\\n", " 5 & 832 & 112 & 57 & 64\\\\\n", " 6 & 739 & 167 & 94 & 93\\\\\n", " 7 & 616 & 235 & 149 & 124\\\\\n", " 8 & 471 & 301 & 228 & 145\\\\\n", " 9 & 329 & 343 & 328 & 142\\\\\n", "10 & 216 & 341 & 442 & 113\\\\\n", "11 & 143 & 301 & 556 & 74\\\\\n", "12 & 100 & 244 & 657 & 43\\\\\n", "13 & 75 & 187 & 738 & 24\\\\\n", "14 & 61 & 139 & 800 & 14\\\\\n", "15 & 53 & 101 & 846 & 8\\\\\n", "16 & 47 & 73 & 880 & 5\\\\\n", "17 & 44 & 52 & 904 & 3\\\\\n", "18 & 42 & 37 & 921 & 2\\\\\n", "19 & 40 & 26 & 934 & 2\\\\\n", "20 & 39 & 18 & 942 & 1\\\\\n", "21 & 38 & 13 & 949 & 1\\\\\n", "22 & 38 & 9 & 953 & 0\\\\\n", "23 & 38 & 6 & 956 & 0\\\\\n", "\\vdots & \\vdots & \\vdots & \\vdots & \\vdots\\\\\n", "\\end{array}\n", "$$\n", "\n", "For more thourough background on compartemental models in general, please refer to [The Mathematics of Infectious Diseases, H.W. Hethcote, 2000 in SIAM Rev., 42(4), 599–653.](https://epubs.siam.org/doi/abs/10.1137/s0036144500371907)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Assumptions and Definitions\n", "\n", "Let us now introduce a more exact description of the SIR-model together with some adequate assumptions in order to better understand its motivation and dynamics.\n", "\n", "* Following state transitions are possibly: $S\\rightarrow I\\rightarrow R$. In other words, if a susceptible individual in compartment $S$ is infected he moves into compartment $I$. Then, after some time, he recovers (or deceases) and finally moves into compartment $R$. Especially we assume that immunity does not wear off with time, that is there is no transition from state _recovered_ to state _susceptible_.\n", "* $S(t)+I(t)+R(t)=N$ (or all $t\\geq 0$). That means we do not consider births or deaths or any other demographic changes or travel etc..\n", "* If an individual is infectied he is instantly infectious. This is a simplification since usually an infected individual only becomes infectious after the [_pre-infectious period_](#latent-period).\n", "* Usually the unit of a time period is one day.\n", "* $\\pmb\\beta$ (beta) **contact rate** or spreading rate or **infection rate** is the average number of contacts of an infected individual per unit of time. E.g., a contact rate of $\\beta=1.0$ means that an infected individual on average infects one other person per day. The contact rate is implicitly composed of several factors, like\n", " * The (biological) infectivity of the pathogene\n", " * The number of contacts of an infected person with susceptible persons. Let us assume the infectivity is to transfer the desease is $0.25\\%$. Then the probability of infection when the infected individual meets two susceptible persons a day is $1-(0,75)^2\\approx 44\\%$, when meeting five susceptible persons a day it is about $76\\%$ and so on. Also note that if a large proportion of the population is immune, the contact rate will dramatically decrease, which is called [herd immunity](https://en.wikipedia.org/wiki/Herd_immunity).\n", " * There are a lot more factors effecting the contact rate, like duration of contact, proximity or locality (in the open or in a closed room), wearing face masks, ventilation, temperature, huminidy, etc..\n", " \n", " \n", "* $\\pmb\\gamma$ (gamma) **recovery rate** is the rate at which an infected person recovers. More precisely formulated, we can assume that the the time periods for a change from compartment $I$ to compartment $R$ are [exponetially distributed](https://en.wikipedia.org/wiki/Exponential_distribution). This means that the mean duration in compartment $I$ is equal to $1/\\gamma$ and that the probability after $t$ units of time is equal to $\\exp(-\\gamma t)$. For example if we assume $\\gamma=0.2$, then we have a mean duration period in compartment $I$ of $D_I=1/\\gamma=1/0.2=5$ days. The probabilty that a person is still infectious after one day is $exp(-0.2)\\approx 0.82$ and that person is still infectious after five days is $exp(-0.2\\cdot5)\\approx 0.37$ and so on. The technicalities are not so important for the further understanding. The take-away point here is that \n", "$$1/\\gamma = D_I$$\n", "constitutes the **mean infectious period**. Also note that the SIR-model does not explicitly model a [pre-infectious period](#pre-infectious-period) (but it can quite easily be extended into a SEIR-model).\n", "* The **[basic reproduction number](https://en.wikipedia.org/wiki/Basic_reproduction_number)** $R_0$ is defined as the average number of secondary infections produced by\n", "a single infected individual introduced into a completely susceptible population. If infection rate $\\beta$ and recovery rate $\\gamma$ are equal, then the overal number of infected persons is constant (on average). If $\\beta > \\gamma$ the average number of infections rises and for $\\beta < \\gamma$ the average number of infections drops. For the SIR-model it holds that \n", "\n", " $$\n", " R_0=\\frac{\\beta}{\\gamma}.\n", " $$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Motivation of State Transition Rates\n", "\n", "Let us denote the population size by $N$. \n", "Then the total number of transitions $S\\rightarrow I$, or in other words the number of individuals who get infected during a time period is given by\n", "\n", "$$S\\left[\\beta\\left(\\frac{I}{N}\\right)\\right] = \\frac{\\beta IS}{N}.$$\n", "\n", "But how can we motivate the left side of the above equation (the right side is just a simple reformulation)? Let's break down the expression on the left hand side of the above equation:\n", "\n", "* Since $N$ is the total population size we can interprete $\\frac{I}{N}$ as the [classical probability](https://en.wikipedia.org/wiki/Probability_interpretations#Classical_definition) (sometimes also called _Laplace probability_) for the random event _\"contact with an infected individual\"_. \n", "* It follows that $\\beta\\frac{I}{N}$ is the probability to meet an infected individual **and** to get infected.\n", "* But only susceptible individuals can be infected, so that the average number of newly infected individuals is given by $S\\left[\\beta\\left(\\frac{I}{N}\\right)\\right] = \\frac{\\beta IS}{N}$. Voilà.\n", " \n", "To sum up, per unit time period a total of $\\beta IS\\over N$ individuals transition from compartment $S$ into compartment $I$, which we write as $S\\stackrel{\\beta\\cdot IS\\over N}\\longrightarrow I$.\n", "The average number of recovered individuals per unit time period is given by $\\gamma I$ so we can write all transitions in the SIR-model as\n", "\n", "$$S\\stackrel{\\beta IS\\over N}\\longrightarrow I \\stackrel{\\gamma I}\\longrightarrow R.$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Dynamics of the SIR-model\n", "\n", "### System of Differential Equations\n", "\n", "We can model the [dynamics](#dynamical-system) of the SIR-model $-$ or more specifically the evolution of the numbers of individuals in the respective compartments as a function of time $-$ as a system of three [ordinary differential equations](#ordinary-differential-equation) (ODEs) as shown below. If you don't know what an ODE is, don't worry, just keep on reading, you should get an sufficient intuitive understanding.\n", "\n", "$$\n", "\\begin{aligned}\n", "{\\frac {dS(t)}{dt}} &= -{\\beta I(t)S(t)\\over N}\\\\\n", "{\\frac {dI(t)}{dt}} &= {\\beta I(t)S(t)\\over N}-\\gamma I(t)\\\\\n", "{\\frac {dR(t)}{dt}} &= \\gamma I(t)\n", "\\end{aligned}\n", "$$\n", "\n", "#### Verbalization of SIR system of ODEs\n", "\n", "We can regard $\\frac {df(t)}{dt}$ as the change in the value of the function $f$ in a (\"very short\") time period $dt$. So we can verbalize the three equations above in the following way:\n", "\n", "* The number of **susceptible** individuals $S(t)$ is reduced by the number of newly infected $\\beta I(t)S(t)\\over N$ during time period $dt$.\n", "* The number of **infected** $I(t)$ is increased by the number of newly infected $\\beta I(t)S(t)\\over N$ and decreased by the newly recovered individuals $\\gamma I(t)$ during time period $dt$.\n", "* The number of **recovered** $R(t)$ is increased by the number of newly recovered individuals $\\gamma I(t)$ during time period $dt$.\n", "\n", "Not that our requirenment $S(t)+I(t)+R(t)=N$ for all $t$ from section [Assumptions und Definitions](#Assumptions-and-Definitions) is fullfilled, since for each time step the total number of individuals does not change:\n", "\n", "$${dS(t)\\over dt}+{dI(t)\\over dt}+{dR(t)\\over dt}= -{\\beta I(t)S(t)\\over N} + \\left({\\beta I(t)S(t)\\over N}-\\gamma I(t)\\right) + \\gamma I(t) = 0.$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Euler Method\n", "\n", "How can we solve the SIR ODE system? It turns out we can use the [Euler method](https://en.wikipedia.org/wiki/Euler_method) (named after Swiss mathematician Leonhard Euler 1707-1783) to numerically solve the ODEs. \n", "\n", "Remember from school, that $\\frac {df(t)}{dt}=f'(t)$ is the [derivative](https://en.wikipedia.org/wiki/Derivative) of the function $f$. For example for a quadratic function $f(x)=ax^2+bx+c$ the derivative $f'$ is a straight line $\\left(f'(x)=2ax+b\\right)$. Locally we can approximate a function with the help of its first derivative: For a quadratic function, the first derivative is a straight line and we can approximate $f$ at any x-value $x_0$ by a straight line going through the coordinate $\\left(x_0,f(x_0)\\right)$ with gradient $f'(x_0)$. This is also called a (first-order) [Taylor approximation](https://en.wikipedia.org/wiki/Taylor%27s_theorem) (Brook Taylor was an English mathematician who lived 1685 - 1731). For a differentiable function $f$ the first Taylor approximation in point $x_0$ formally looks like this:\n", "\n", "$$\n", "f(x_0+h)\\;\\approx\\; f(x_0)+f'(x_0)h \\;\\overset{\\text{def}}=\\; f(x_0)+{df(x_0)\\over dt}h\n", "$$\n", "\n", "A picture may be better than a many words:\n", "\n", "

\n", "\"Illustration\n", " \n", "\n", "

\n", "\n", "\n", "Now we can drop the above formulated [SIR ODEs](#Dynamics-of-the-SIR-model) into the first-order Taylor approximation and arrive at the Euler method to [iteratively (step by step)](https://en.wikipedia.org/wiki/Iteration) calculate (approximate) values of the SIR variables:\n", "\n", "$$\n", "\\begin{aligned}\n", " S_{k} &= S_{k-1} + \\Delta t \\left(-{\\beta I_{k-1} S_{k-1}\\over N}\\right),\\\\\n", " I_{k} &= I_{k-1} + \\Delta t \\left({\\beta I_{k-1} S_{k-1}\\over N}-\\gamma I_{k-1}\\right),\\\\\n", " R_{k} &= S_{k-1} + \\Delta t \\left(\\gamma I_{k-1}\\right),\n", "\\end{aligned}\n", "$$\n", "where we define\n", "\n", "* $S(t_{k-1}) := S_{k-1}$ and so forth for a more convenient notation, and\n", "* $\\Delta t := t_{k+1}-t_k$ for $k=0,1,2,\\ldots$.\n", "\n", "At start of simulation $k=0$ we have to set the start values appropriately, e.g. $S_0=N-I_0-R_0$, $I_0>0$ and $R_0=0$.\n", "\n", "\n", "\n", "\n", "\n", "> Aside: Often the Euler method ist introduced by integrating the ODE and performing a _box approximation_ of the integral. I think the above motivation using first-order Taylor approximations might be better suited, since it is more elementary. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Implementation of Euler's Method\n", "\n", "### Preparations\n", "\n", "In a first step we import two libraries that we want to use and also set some parameters for `pyplot`. `matplotlib` is responsible for creating plots and `numpy` is adding advanced numerical functionality, e.g arrays and matrices." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import matplotlib.pyplot as plt\n", "import numpy as np" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Calculate Time Step\n", "\n", "Using Euler's method from above it is straight-forward to implement a function calculating SIR values for the next time step." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "### Function to calculate the change of value during a time step\n", "def step_calc(S,I,R,newI,beta,gamma,i):\n", " Delta_S = - beta * I[i] * S[i] / N * Delta_t\n", " Delta_R = gamma * I[i] * Delta_t\n", " Delta_I = - Delta_S - Delta_R\n", " S[i+1] = S[i] + Delta_S \n", " I[i+1] = I[i] + Delta_I\n", " R[i+1] = R[i] + Delta_R\n", " newI[i+1] += -Delta_S\n", " #print(\"%2d & %.0f & %.0f & %.0f & %.0f\\\\\\\\\" % (i+1, S[i+1]*num, I[i+1]*num, R[i+1]*num, newI[i+1]*num))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Main Function and Plotting\n", "\n", "We still need a main function `simulate()` which calculates all SIR values step by step using above function `step_calc()`:" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "def simulate(beta,gamma):\n", " ### initializations\n", " global S,I,R,newI\n", " S = np.zeros(n); I = np.zeros(n); R = np.zeros(n); newI = np.zeros(n) # initialize arry of length n\n", " I[0] = I0\n", " R[0] = R0\n", " S[0] = N - I[0] - R[0]\n", " ### iteration loop\n", " for t in range(0, n-1):\n", " #print(\"%2d & %.0f & %.0f & %.0f\\\\\\\\\" % (t, S[t]*N, I[t]*N, R[t]*N))\n", " step_calc(S,I,R,newI,beta,gamma,t)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Simulations\n", "\n", "We will run three different simulations for the spread of SARS-CoV-2 infection. \n", "\n", "1. First wie use the basic reproduction number $R_0=3.0$.\n", " We observe approximately a maximum of 26 million infected persons at the peak.\n", " If we conservatively assume only %$1\\%$ rate of intensive care patients, then $250,000$ thousand beds in ICUs\n", " are needed.\n", "2. Then we change the reproduction number to $R_0=1.5$. \n", " Now \"only\" about $5.0\\text{ million }\\cdot 1\\%=50,000$ ICU beds are needed (still exceeding German ICU capacity, which is about 35,000 in total).\n", "3. Using basic reproduction number $R_0=1.0$ leads to a constant scenario, where\n", " the number of infected approximately equals the starting number." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Parameters for SARS-CoV-2\n", "\n", "For simulations we will use parameters from the 2020 SARS-CoV-2 pandemic to derive values for $\\beta$, $\\gamma$ and $R_0$. Often only \n", "$R_0$ and $D_I$ are are available, from which we can readily deduce the model parametrs $\\gamma=1/D_I$ and $\\beta=\\gamma\\cdot R_0$ (see section [Assumptions and Definitions](#Assumptions-and-Definitions)). Here are some parameter values from the literature we can use for our simulations:\n", "\n", "$$\n", "\\begin{array}{l|r|r|r|r}\n", "\\textbf{Source} & \\pmb{D_I} & \\pmb{\\gamma}\\text{ (Tage)} & \\pmb{R_0} & \\pmb{\\beta}\\\\\n", "\\hline\n", "\\text{RKI from 3. April 2020 (1)} & 7,0 & 0.14 & 3,0 & 0.42\\\\\n", "\\text{Kucharski et.al, 26. Feb 2020 (2)} & 5,2 & 0.19 & 2,5 & 0.48\\\\\n", "\\text{Dehning et.al. 2. Apr 2020 (3)} & 8,0 & 0.125 & 3.3 & 0.41\n", "\\end{array}\n", "$$\n", "\n", "**Comments** \n", "\n", "* In (1) [RKI (Robert Koch Institut in German)]((https://www.rki.de/DE/Content/InfAZ/N/Neuartiges_Coronavirus/Steckbrief.html?nn=13490888)) we used \n", "the mean time between contracting of desease and hospitalization (_Zeit von Erkrankungsbeginn bis Hospitalisierung_) for $D_I$. This seems to be a conservative (low-risk) assumption.\n", "* For (3) Dehning et.al. we used \"delay in days between contracting the disease and being recorded\" for $D_I$. It seems unclear how the authors motivated $D_I \\approx \\text{ reporting delay}$? We recall, that $D_I$ is defined as the the mean [infectious period](#infectious-period) ...\n", "\n", "**Links**\n", "\n", "* (1) [Steckbrief zur Coronavirus-Krankheit-2019 (COVID-19)](https://www.rki.de/DE/Content/InfAZ/N/Neuartiges_Coronavirus/Steckbrief.html?nn=13490888)\n", "* (2) [Analysis and projections of transmission dynamics of nCoV in Wuhan](https://www.thelancet.com/journals/laninf/article/PIIS1473-3099(20)30144-4/fulltext), [Github Parameters](https://cmmid.github.io/topics/covid19/current-patterns-transmission)\n", "* (3) [Inferring COVID-19 spreading rates and potential change points for case number forecasts, 2020-04-02](https://arxiv.org/abs/2004.01105)\n" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "### start values\n", "beta = 0.41 # contact rate, can be detrmined as beta = R_0 * gamma\n", "gamma = 0.125 # recovery rate as 1/D_I and D_I the mean infectious period \n", "N = 83e6 # population in your in Germany (put in your country of choice)\n", "I0 = 50000 # infected persons at start of simulation\n", "R0 = 1000000 # number of recovered (non-infectious)\n", "n = 200 # number of time steps \n", "Delta_t = 1 # length of time interval in days (simulation length = n * Delta_t)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Running the Simulations" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# setup plot with 2*3 subplots\n", "fig, ax = plt.subplots(nrows=2, ncols=3, sharex=True, figsize=(16, 6), dpi=120)\n", "x = np.arange(start=0.0, stop=n*Delta_t, step=Delta_t) # x-axis values (time in days) \n", "fig.suptitle(\"SIR-Model ($N=%.2f$ mio, $I_0=%.2f$ mio, $R_0=%.2f$ mio, $\\\\beta=%.2f$)\" % (N/1e6, I0/1e6, R0/1e6, beta))\n", "\n", "# define plotting function\n", "def my_plot(ax, title, zoom):\n", " if zoom:\n", " title = \"\"\n", " ax.set_ylim(0,int(newI.max())*1.5)\n", " ax.set_title(title)\n", " ax.plot(x, S, label='$S(t)$ (susceptible)')\n", " ax.plot(x, I, label='$I(t)$ (infected)')\n", " ax.plot(x, R, label='$R(t)$ (removed)')\n", " ax.plot(x, newI, label='incidence')\n", " ax.legend(loc='upper right')\n", " ax.grid()\n", "\n", "# Run three simulations for three different reproduction rates\n", "# (1) R_0 = 3.0\n", "simulate(gamma*3.0, gamma)\n", "my_plot(ax[0,0], \"$R_0$=3.0\", 0)\n", "my_plot(ax[1,0], \"$R_0$=3.0\", 1)\n", "\n", "# (2) R_0 reduced to 1.5\n", "simulate(gamma*1.5, gamma)\n", "my_plot(ax[0,1], \"$R_0$=1.5\", 0)\n", "my_plot(ax[1,1], \"$R_0$=1.5\", 1)\n", "\n", "# (3) R_0 reduced to 1.2\n", "simulate(gamma*1.2, gamma)\n", "my_plot(ax[0,2], \"$R_0$=1.2\", 0)\n", "my_plot(ax[1,2], \"$R_0$=1.2\", 1)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Summary of Scenario Simulations\n", "\n", "In order to avoid potentially tens of thousand avoidable deaths caused by lack of ICU capacity, the reproduction rate $R_0$ should be near (or best below) $1.0$. Also note that even for small starting numbers of infected persons, the exponentiel nature (\"avalanche effect\") of the dynamics!" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Glossary\n", "\n", "### basic reproduction number\n", "\n", "The basic reproduction number $R_0$ (\"R naught\") is defined as the average number of secondary infections that occur when one infective individual \n", "is introduced into a completely susceptible population. See also [replacement number](#replacement-number). \n", "The relationship between $R_0$ and the [recover-rate](#recocery-rate) $\\gamma$ is $$R_0=\\frac{\\beta}{\\gamma}$$\n", "\n", "### beta\n", "\n", "see [contact rate](#contact-rate).\n", "\n", "### contact rate\n", "\n", "$\\beta$, the contact rate or spreading rate or infection rate is the average number of contacts of an infected individual \n", "per unit of time (often a single day). In the SIR model $\\beta$ is the average rate at which an individual moves from compartment $S$ (susceptible) into compartment $I$ (infected).\n", "\n", "### dynamical system \n", "\n", "Some examples for dynamical systems:\n", "\n", "* In mathematics, a system in which a function describes the time dependence of a point in a geometrical space. \n", "* In physics, movement of a particle or ensemble of particles whose state (e.g. position, velocity etc.) varies over time.\n", "\n", "In the context of the SIR-model the size of the three compartments vary over time.\n", "\n", "### gamma\n", "\n", "See [recovery rate](#recovery-rate).\n", "\n", "### incidence\n", "\n", "Number of new cases per unit time (usually a single day).\n", "\n", "### incubation period\n", "\n", "Time between exposure to a infectious desease and when first clinical symtoms show.\n", "\n", "### infection rate\n", "\n", "See [contact rate](#contact-rate)\n", "\n", "### infectious period\n", "\n", "The time period during which an infected person can **transmit** the desease to other susceptiple individuels.\n", "\n", "### latent period\n", "\n", "The latent period or _pre-infectious period_ is the period starting with the infection until the individuel becomes infectious (see [infectious period](#infectious-period)).\n", "\n", "### ordinary differential equation\n", "\n", "An ordinary [differential equation](https://en.wikipedia.org/wiki/Differential_calculus#Differential_equations) (ODE) is an equation containing a function of one independent variable and their derivatives. \n", "\n", "$$\n", "y'(t) = f(t,y),\\quad y(t_0) = y_0\\text{ (initial condition)}\n", "$$\n", "\n", "For examle the above defined SIR-model is an (system of) ODE(s). Many if not most physical processes can be described by a set of differential equations. They play an very important role in the mathematical modelling of natural phenomena. We can numerically, approximately solve an ODE using Euler's method for solving ODEs.\n", "\n", "### pre-infectious period\n", "\n", "See [latent period](#latent-period).\n", "\n", "### recovery rate\n", "\n", "$\\gamma$ (gamma), the recovery rate is the average rate at which an infected person recovers (and is immune to a new infection). IN the SIR model it is responsible for a change from compartment $I$ into compartment $R$.\n", "\n", "### replacement number\n", "\n", "The replacement number $R$ is defined as the average number of secondary infections produced by an infective individual during the infectious period. Note that at the start of the epidemic $R_0=R$. See also [basic reproduction number](basic-reproduction-number)." ] } ], "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": "3.7.3" } }, "nbformat": 4, "nbformat_minor": 4 }