Predictive Modeling for Epidemic Outbreaks: A New Approach and COVID-19 Case Study

Jian Chen, Michael C. Fu (), Wenhong Zhang and Junhua Zheng
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Jian Chen: CreditWise Technology Co., Ltd., Lujiazui Road No. 166, Mirae Asset Tower Building, 16 Block D, Shanghai 200120, China
Michael C. Fu: Smith Chair of Management Science Department of Decision, Operations and Information Technologies, Robert H. Smith School of Business & Institute for Systems, Research Van Munching Hall University of Maryland, College Park, Maryland 20742, USA
Wenhong Zhang: Department of Infectious Diseases, Huashan Hospital, Fudan University, 12 Wulumuqi Zhong Road, Shanghai 200040, China
Junhua Zheng: Shanghai General Hospital, School of Medicine in Shanghai, Jiao Tong University, 86 Wujing Road, Shanghai 200080, China

Asia-Pacific Journal of Operational Research (APJOR), 2020, vol. 37, issue 03, 1-21

Abstract: Since the onset of the COVID-19 outbreak in Wuhan, China, numerous forecasting models have been proposed to project the trajectory of coronavirus infection cases. Most of these forecasts are based on epidemiology models that utilize deterministic differential equations and have resulted in widely varying predictions. We propose a new discrete-time Markov chain model that directly incorporates stochastic behavior and for which parameter estimation is straightforward from available data. Using such data from China’s Hubei province (for which Wuhan is the provincial capital city and which accounted for approximately 82% of the total reported COVID-19 cases in the entire country), the model is shown to be flexible, robust, and accurate. As a result, it has been adopted by the first Shanghai assistance medical team in Wuhan’s Jinyintan Hospital, which was the first designated hospital to take COVID-19 patients in the world. The forecast has been used for preparing medical staff, intensive care unit (ICU) beds, ventilators, and other critical care medical resources and for supporting real-time medical management decisions.

Keywords: COVID-19; coronavirus; epidemics; pandemic; Markov chain; stochastic models; simulation; predictive modeling; analytics; predictive analytics; transition matrix model (search for similar items in EconPapers)
Date: 2020
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DOI: 10.1142/S0217595920500281

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