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Modeling Epidemics With Compartmental Models

Educational Objective
To understand how to use compartmental models to predict an epidemic’s infection rate and pattern
1 Credit CME

During epidemics, there is a critical need to understand both the likely number of infections and their time course to inform both public health and health care system responses. Approaches to forecasting the course of an epidemic vary and can include simulating the dynamics of disease transmission and recovery1,2 or empirical fitting of data trends.3 A common approach is to use epidemic compartmental models, such as the susceptible-infected-recovered (SIR) model.1,2

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Article Information

Corresponding Author: Juliana Tolles, MD, MHS, Department of Emergency Medicine, Harbor-UCLA Medical Center, 1000 W Carson St, Bldg D9, Torrance, CA 90502 (jtolles@emedharbor.edu).

Published Online: May 27, 2020. doi:10.1001/jama.2020.8420

Conflict of Interest Disclosures: None reported.

References
1.
Lourenco  J , Paton  R , Ghafari  M ,  et al. Fundamental principles of epidemic spread highlight the immediate need for large-scale serological surveys to assess the stage of the SARS-CoV-2. medRxiv. Preprint posted March 26, 2020. doi:10.1101/2020.03.24.20042291
2.
Weissman  GE , Crane-Droesch  A , Chivers  C ,  et al.  Locally informed simulation to predict hospital capacity needs during the COVID-19 pandemic.   Ann Intern Med. Published online April 7, 2020. doi:10.7326/M20-1260PubMedGoogle Scholar
3.
Murray  CJL ; IHME COVID-19 health service utilization forecasting team. Forecasting COVID-19 impact on hospital bed-days, ICU-days, ventilator days and deaths by US state in the next 4 months. medRxiv. Preprint posted March 30, 2020. doi:10.1101/2020.03.27.20043752
4.
Kermack  WO , McKendrick  AG .  A contribution to the mathematical theory of epidemics.   Proc R Soc Lond A. 1927;115:700-721. doi:10.1098/rspa.1927.0118Google ScholarCrossref
5.
Clancy  D , O’Neill  PD .  Bayesian estimation of the basic reproduction number in stochastic epidemic models.   Bayesian Anal. 2008;3(4):737-757.Google ScholarCrossref
6.
Newell  NP , Lewnard  JA , Jewell  BL .  Predictive mathematical models of the COVID-19 pandemic.   JAMA. Published online April 16, 2020. doi:10.1001/jama.2020.6585PubMedGoogle Scholar
7.
Zlojutro  A , Rey  D , Gardner  L .  A decision-support framework to optimize border control for global outbreak mitigation.   Sci Rep. 2019;9(1):2216.PubMedGoogle ScholarCrossref
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