{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "*This notebook contains course material from [CBE30338](https://jckantor.github.io/CBE30338)\n", "by Jeffrey Kantor (jeff at nd.edu); the content is available [on Github](https://github.com/jckantor/CBE30338.git).\n", "The text is released under the [CC-BY-NC-ND-4.0 license](https://creativecommons.org/licenses/by-nc-nd/4.0/legalcode),\n", "and code is released under the [MIT license](https://opensource.org/licenses/MIT).*" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "< [Manometer Models and Dynamics](http://nbviewer.jupyter.org/github/jckantor/CBE30338/blob/master/notebooks/03.08-Manometer-Models-and-Dynamics.ipynb) | [Contents](toc.ipynb) | [PID Control](http://nbviewer.jupyter.org/github/jckantor/CBE30338/blob/master/notebooks/04.00-PID_Control.ipynb) >
" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Modeling and Control of a Campus Outbreak of Coronavirus COVID-19\n", "\n", "This Jupyter/Python notebook presents models for the outbreak of an infectious disease into a susceptible population using standard epidemiological models. Model parameters are taken from a rapidly evolving scientific literature documenting the global COVID-19 outbreak. A control policy based on 'social distancing' is included in the model.\n", "\n", "The notebook is organized as follows:\n", "\n", "1. Brief Background on the SARS-CoV-2 Coronavirus and COVID-19\n", "2. Model 1. SIR Model for an Infectious Disease\n", "3. Model 2. SEIR Model\n", "4. Model 3. SEIR Model with Control\n", "5. Model 4. An Improved SEIR model.\n", "6. Ideas for student projects.\n", "\n", "The executable Python code in this notebook can be edited and run Google's cloud servers by clicking on the \"Open in Colab\" button located in the header. Use the interactive sliders to adjust model parameters and perform 'what if' simulations." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 1. Brief background on the SARS-CoV-2 Coronavirus and COVID-19\n", "\n", "[COVID-19](https://www.who.int/health-topics/coronavirus) is caused by the human coronavirus SARS-CoV-2. First identified in the 1960's, there are currently four human coronaviruses endemic to populations around the world:\n", "\n", "* 229E (alpha coronavirus)\n", "* NL63 (alpha coronavirus)\n", "* OC43 (beta coronavirus)\n", "* HKU1 (beta coronavirus)\n", "\n", "These four common coronaviruses cause an upper respiratory disease that can progress to pneumonia. These endemic viruses cause about a quarter of all common colds. Most people will suffer from at least one during their lifetimes.\n", "\n", "In recent decades, three additional coronaviruses that normally infect animals have evolved to infect humans. These include:\n", "\n", "* MERS-CoV (the beta coronavirus that causes Middle East Respiratory Syndrome, or MERS)\n", "* SARS-CoV (the beta coronavirus that causes severe acute respiratory syndrome, or SARS)\n", "* SARS-CoV-2 (the novel coronavirus that causes coronavirus disease COVID-19)\n", "\n", "The last of these, now called SARS-CoV-2, first appeared in December, 2019, at a seafood market in Wuhan (Hubei, China). The rapid spread of SARS-CoV-2 in Wuhan, and subsequent appearance in other locations around the globe, has resulted in declaration of a [global health emergency by the World Health Organization (WHO)](https://www.who.int/emergencies/diseases/novel-coronavirus-2019). Most countries are mobilizing to track the virus and control new outbreaks. At this stage, it is too early to know if efforts to contain the mitigate transmission of the virus will be successful in preventing COVID-19 from becoming a pandemic, or later an endemic disease with a global footprint. \n", "\n", "The latest status on the global outbreak of COVID-19 can be found at the following links:\n", "\n", "* [Coronavirus COVID-19 Global Cases by Johns Hopkins CSSE](https://www.arcgis.com/apps/opsdashboard/index.html#/bda7594740fd40299423467b48e9ecf6)\n", "* [WHO Novel Coronavirus (COVID-19) Situation](https://experience.arcgis.com/experience/685d0ace521648f8a5beeeee1b9125cd)\n", "\n", "The purpose of this notebook is to demonstrate the modeling of an infectious epidemic using the latest available data for COVID-19, and to provide a framework for evaluating the performance of 'social distancing' and other mitigation strategies. The models and data used in this notebook have been extracted from a rapidly emerging and changing literature. Recent papers on COVID-19 can be found at the following links.\n", "\n", "* [Cell Press Coronavirus Resource Hub](https://www.cell.com/2019-nCOV)\n", "* [Lancet COVID-19 Resource Centre](https://www.thelancet.com/coronavirus)\n", "* [medRxiv preprint server search on COVID-19](https://www.medrxiv.org/search/COVID-19%20numresults%3A50%20sort%3Apublication-date%20direction%3Adescending)\n", "* [New England Journal of Medicine Coverage on Coronavirus (COVID-19)](https://www.nejm.org/coronavirus)\n", "* [WHO Database of publications on coronavirus disease (COVID-19)](https://www.who.int/emergencies/diseases/novel-coronavirus-2019/global-research-on-novel-coronavirus-2019-ncov)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Model 1. SIR model for an infectious disease" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Readings\n", "\n", "* Keeling, Matt J., and Pejman Rohani. Modeling Infectious Diseases in Humans and Animals. Princeton University Press, 2008. JSTOR, www.jstor.org/stable/j.ctvcm4gk0. Accessed 25 Feb. 2020.\n", "* Boldog, Péter, et al. \"Risk Assessment of Novel Coronavirus COVID-19 Outbreaks Outside China.\" Journal of Clinical Medicine 9.2 (2020): 571. https://www.mdpi.com/2077-0383/9/2/571\n", "* Bedford, Trevor. Cryptic transmission of novel coronavirus revealed by genomic epidemiology. Accessed 4 Mar 2020. https://bedford.io/blog/ncov-cryptic-transmission/" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Introduction to SIR models\n", "\n", "The SIR model is deterministic compartment model for the spread of an infectious disease that describes key phenomena encountered in epidemiology. In the SIR model, a population is broken into three non-overlapping groups corresponding to stages of the disease:\n", "\n", "* **Susceptible.** The subpopulation susceptible to acquire the disease. For SARS-CoV-2, the assumption is that everyone who has not previously acquired the disease is susceptible to infection.\n", "* **Infectious.** The subpopulation that has become infective.\n", "* **Recovered.** The subpopulation that has recovered from infection, and presumed to be no longer susceiptible to the disease.\n", "\n", "Neglecting demographic processes of birth and death from other causes, and assuming a negligible death rate due to infectious disease at issue, the progression of an epidemic can be modeled by rate processesl\n", "\n", "$$\\text{Susceptible}\n", "\\xrightarrow{\\frac{\\beta S I}{N}} \n", "\\text{Infectious} \n", "\\xrightarrow{\\gamma I} \n", "\\text{Recovered} $$\n", "\n", "The rate processes are modeled as follows.\n", "\n", "* $\\frac{\\beta S I}{N}$ is the rate at which susecptible population encounters the infected population resulting in trasmission of the disease. $S$ is the size of the susceptible population. $\\beta$ is a the model parameters with units of 1/day. \n", "* $\\gamma I$ is the rate at which infected population recovers and becomes resistent to further infection. $I$ is the size of the infective population.\n", "\n", "A model for the spread of an infectious disease in a uniform population is given by the deterministic SIR equations\n", "\n", "\\begin{align*}\n", "\\frac{dS}{dt} & = -\\frac{\\beta S I}{N} \\\\\n", "\\frac{dI}{dt} & = \\frac{\\beta S I}{N} - \\gamma I \\\\\n", "\\frac{dR}{dt} & = \\gamma I\n", "\\end{align*} \n", "\n", "The model becomes more generic by working with population fractions rather than raw population counts. To this end, define\n", "\n", "\\begin{align}\n", "s = \\frac{S}{N} \\qquad\n", "i = \\frac{I}{N} \\qquad\n", "r = \\frac{R}{N}\n", "\\end{align} \n", "\n", "After substitution, this results in a system of four equations.\n", "\n", "\\begin{align*}\n", "\\frac{ds}{dt} & = -\\beta s i \\\\\n", "\\frac{di}{dt} & = \\beta s i - \\gamma i \\\\\n", "\\frac{dr}{dt} & = \\gamma i\n", "\\end{align*} \n", "\n", "where $s + i + r = 1$ is an invariant." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Key Properties of the SIR Model\n", "\n", "The SIR model describes key epidemiological phenemena. Here is a brief synposis of the relevant results.\n", "\n", "* The parameters $\\beta$ and $\\gamma$ have units of inverse time. \n", "* $\\beta$ is rate constant associated with transmission of the infection. The corresponding time constant $\\tau_{infect} = \\frac{1}{\\beta}$ corresponds to exponential growth of new infections in an initially susceptible population where $s=1$. \n", "* $\\gamma$ is the rate of recovery from infections. The associated time constant $\\tau_{recovery}=\\frac{1}{\\gamma}$ is average time to recover from an infection.\n", "* The infectious population can grown only if $\\beta s > \\gamma$, that is the rate of infection is greater than the rate of recovery.\n", "* The ratio $R_0 = \\frac{\\beta}{\\gamma}$ is the \"Basic Reproduction Number\" that describes the transmissability or contagiousness of an infectious disease. \n", "* $R_0$ is the average number of people infected by an index 0 case in an otherwise completely susceptible population. \n", "* The infectious population can grow only if $R_0 s > 1$. If $s=1$, then $R_0 > 1$ is sufficient for growth of the infectious population.\n", "* The infectious population decreases if $s R_0 < 1$ or, equivalently, $s < \\frac{1}{R_0}$.\n", "* The population has 'herd immunity' when the fraction of susceptibles is less than $\\frac{1}{R_0}$." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Simulation\n", "\n", "The following Python code implements a simulation of the SIR model. The parameter values were selected from the recent survey by Boldog, et al. (2020).\n", "\n", "* $\\tau_{infectious} = \\frac{1}{\\gamma} = 8.4$ days.\n", "* $R_0 = \\frac{\\beta}{\\gamma} = 2.4$ " ] }, { "cell_type": "code", "execution_count": 123, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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