An epidemiological forecast model and software assessing interventions on the COVID-19 epidemic in China

• Published in: Journal of Data Science Vol. 18; no. 3; pp. 409 - 432
• Main Authors: Wang, Lili, Zhou, Yiwang, He, Jie, Zhu, Bin, Wang, Fei, Tang, Lu, Kleinsasser, Michael, Barker, Daniel, Eisenberg, Marisa C., Song, Peter X.K.
• Format: Journal Article
• Language: English
• Published: 中華資料採礦協會 17.01.2021
• Subjects: Infectious disease; under-reporting; SIR model; turning point; MCMC; prediction; Runga-Kutta approximation; coronavirus
• ISSN: 1683-8602; 1680-743X; 1683-8602
• DOI: 10.6339/JDS.202007_18(3).0003

We develop a health informatics toolbox that enables timely analysis and evaluation of the time-course 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.