Revisiting classical SIR modelling in light of the COVID-19 pandemic

作     者：Leonid Kalachev Erin L.Landguth Jon Graham 

作者机构：Mathematical SciencesUniversity of MontanaMissoulaUSA Center for Population Health ResearchSchool of Public and Community Health SciencesUniversity of MontanaMissoulaUSA 

出 版 物：《Infectious Disease Modelling》 (传染病建模（英文）)

年 卷 期：2023年第8卷第1期

页      面：72-83页

核心收录：

学科分类：0710[理学-生物学] 1004[医学-公共卫生与预防医学(可授医学、理学学位)] 1002[医学-临床医学] 100401[医学-流行病与卫生统计学] 10[医学] 

基　　金：supported by National Institute of General Medical Sciences of the National Institutes of Health United States(Award Numbers P20GM130418 U54GM104944). 

主　　题：Basic disease reproduction number Communicable disease control Coronavirus COVID-19 Disease transmission Epidemics Epidemiology Influenza data Mathematical models Montana SIR models 

摘      要：Background:Classical infectious disease models during epidemics have widespread usage,from predicting the probability of new infections to developing vaccination plans for informing policy decisions and public health responses.However,it is important to correctly classify reported data and understand how this impacts estimation of model parameters.The COVID-19 pandemic has provided an abundant amount of data that allow for thorough testing of disease modelling assumptions,as well as how we think about classical infectious disease modelling paradigms.Objective:We aim to assess the appropriateness of model parameter estimates and preiction results in classical infectious disease compartmental modelling frameworks given available data types(infected,active,quarantined,and recovered cases)for situations where just one data type is available to fit the model.Our main focus is on how model prediction results are dependent on data being assigned to the right model compartment.Methods:We first use simulated data to explore parameter reliability and prediction capability with three formulations of the classical Susceptible-Infected-Removed(SIR)modelling framework.We then explore two applications with reported data to assess which data and models are sufficient for reliable model parameter estimation and prediction accuracy:a classical influenza outbreak in a boarding school in England and COVID-19 data from the fall of 2020 in Missoula County,Montana,USA.Results:We demonstrated the magnitude of parameter estimation errors and subsequent prediction errors resulting from data misclassification to model compartments with simulated data.We showed that prediction accuracy in each formulation of the classical disease modelling framework was largely determined by correct data classification versus misclassification.Using a classical example of influenza epidemics in an England boarding school,we argue that the Susceptible-Infected-Quarantined-Recovered(SIQR)model is more appropriate than the commonly employed SIR model given the data collected(number of active cases).Similarly,we show in the COVID-19 disease model example that reported active cases could be used inappropriately in the SIR modelling framework if treated as infected.Conclusions:We demonstrate the role of misclassification of disease data and thus the importance of correctly classifying reported data to the proper compartment using both simulated and real data.For both a classical influenza data set and a COVID-19 case data set,we demonstrate the implications of using the“rightdata in the“wrongmodel.The importance of correctly classifying reported data will have downstream impacts on predictions of number of infections,as well as minimal vaccination requirements.