Predicting the Cumulative Number of Cases for the COVID-19 Epidemic in China from Early Data

8 Pages Posted: 24 Jun 2020

See all articles by Zhihua Liu

Zhihua Liu

Beijing Normal University (BNU) - School of Mathematical Sciences

Pierre Magal

Institut de Mathematique de Bordeaux

Ousmane Seydi

Ecole Polytechnique de Thies - Departement Tronc Commun

Glenn Webb

Vanderbilt University - Department of Mathematics

Date Written: February 23, 2020

Abstract

We model the COVID-19 coronavirus epidemic in China. We use early reported case data to predict the cumulative number of reported cases to a final size. The key features of our model are the timing of implementation of major public policies restricting social movement, the identification and isolation of unreported cases, and the impact of asymptomatic infectious cases.

Note: Funding: Research was partially supported by NSFC and CNRS (Grant Nos. 11871007 and 11811530272) and the Fundamental Research Funds for the Central Universities.

Conflict of Interest: We declare no competing interests.

Keywords: corona virus, reported and unreported cases, isolation, quarantine, public closings, epidemic mathematical model

Suggested Citation

Liu, Zhihua and Magal, Pierre and Seydi, Ousmane and Webb, Glenn, Predicting the Cumulative Number of Cases for the COVID-19 Epidemic in China from Early Data (February 23, 2020). Available at SSRN: https://ssrn.com/abstract=3543148 or http://dx.doi.org/10.2139/ssrn.3543148

Zhihua Liu

Beijing Normal University (BNU) - School of Mathematical Sciences ( email )

No. 19, XinJieKouWai St
Beijing, 100875
China

Pierre Magal (Contact Author)

Institut de Mathematique de Bordeaux ( email )

Université de Bordeaux
351 cours de la libération
Talence, 33400
France

Ousmane Seydi

Ecole Polytechnique de Thies - Departement Tronc Commun ( email )

Senegal

Glenn Webb

Vanderbilt University - Department of Mathematics ( email )

Nashville, TN 37240
United States

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