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An epidemiological forecast model and software assessing interventions on the COVID-19 epidemic in China
Volume 18, Issue 3 (2020): Special issue: Data Science in Action in Response to the Outbreak of COVID-19, pp. 409–432
Lili Wang   Yiwang Zhou   Jie He     All authors (10)

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https://doi.org/10.6339/JDS.202007_18(3).0003
Pub. online: 4 August 2022      Type: Research Article      Open accessOpen Access

Published
4 August 2022

Abstract

We develop a health informatics toolbox that enables timely analysis and evaluation of the timecourse dynamics of a range of infectious disease epidemics. As a case study, we examine the novel coronavirus (COVID-19) epidemic using the publicly available data from the China CDC. This toolbox is built upon a hierarchical epidemiological model in which two observed time series of daily proportions of infected and removed cases are generated from the underlying infection dynamics governed by a Markov Susceptible-Infectious-Removed (SIR) infectious disease process. We extend the SIR model to incorporate various types of time-varying quarantine protocols, including government-level ‘macro’ isolation policies and community-level ‘micro’ social distancing (e.g. self-isolation and self-quarantine) measures. We develop a calibration procedure for underreported infected cases. This toolbox provides forecasts, in both online and offline forms, as well as simulating the overall dynamics of the epidemic. An R software package is made available for the public, and examples on the use of this software are illustrated. Some possible extensions of our novel epidemiological models are discussed.

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Keywords
coronavirus Infectious disease MCMC prediction Runga–Kutta approximation SIR model turning point under-reporting

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