Journal of Data Science ›› 2020, Vol. 18 ›› Issue (3): 455-472.doi: 10.6339/JDS.202007_18(3).0010

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Tracking Reproductivity of COVID-19 Epidemic in China with Varying Coefficient SIR Model

Haoxuan Sun1, Yumou Qiu2, Han Yan3, Yaxuan Huang4, Yuru Zhu5, Jia Gu5, and Song Xi Chen5,6

  1. 1 Center for Data Science, Peking University, Beijing, China 
    2 Department of Statistics, Iowa State University, Ames, Iowa, USA 
    3 School of Mathematical Sciences, Sichuan University, Chengdu, Sichuan, China 
    4 Yuanpei College, Peking University, Beijing, China
    5 Center for Statistical Science, Peking University, Beijing, China 
    6 Guanghua School of Management, Peking University, Beijing China

  • Online:2020-07-21 Published:2020-07-22

Abstract: We propose a varying coefficient Susceptible-Infected-Removal (vSIR) model that allows changing infection and removal rates for the latest corona virus (COVID-19) outbreak in China. The vSIR model together with proposed estimation procedures allow one to track the reproductivity of the COVID-19 through time and to assess the effectiveness of the control measures implemented since Jan 23 2020 when the city of Wuhan was lockdown followed by an extremely high level of self-isolation in the population. Our study finds that the reproductivity of COVID-19 had been significantly slowed down in the three weeks from January 27th to February 17th with 96.3% and 95.1% reductions in the effective reproduction numbers R among the 30 provinces and 15 Hubei cities, respectively. Predictions to the ending times and the total numbers of infected are made under three scenarios of the removal rates. The paper provides a timely model and associated estimation and prediction methods which may be applied in other countries to track, assess and predict the epidemic of the COVID-19 or other infectious diseases.

Key words: epidemic assessment, estimation of basic reproductive number