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The Evolution of Quarantined and Suspected Cases Determines the Final Trend of the 2019-nCoV Epidemics Based on Multi-Source Data Analyses

26 Pages Posted: 13 Feb 2020

See all articles by Biao Tang

Biao Tang

Xi'an Jiaotong University (XJTU) - The Interdisplinary Research Center for Mathematics and Life Sciences; York University - Department of Mathematics and Statistics

Fan Xia

Xi'an Jiaotong University (XJTU) - The Interdisplinary Research Center for Mathematics and Life Sciences

Sanyi Tang

Shaanxi Normal University - School of Mathematics and Information Science

Nicola Luigi Bragazzi

York University - Laboratory for Industrial and Applied Mathematics

Qian Li

York University - Laboratory for Industrial and Applied Mathematics

Xiaodan Sun

Xi'an Jiaotong University (XJTU) - The Interdisplinary Research Center for Mathematics and Life Sciences

Juhua Liang

Shaanxi Normal University - School of Mathematics and Information Science

Yanni Xiao

Xi'an Jiaotong University (XJTU) - The Interdisplinary Research Center for Mathematics and Life Sciences; Xi'an Jiaotong University (XJTU) - School of Mathematics and Statistics

Jianhong Wu

York University

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Abstract

Background: Since January 23rd 2020, stringent measures for controlling the novel coronavirus epidemics have been gradually enforced and strengthened in mainland China. The detection and diagnosis have been improved as well. However, the daily reported cases staying in a high level make the epidemics trend prediction difficult.

Methods: Since the traditional SEIR model does not evaluate the effectiveness of control strategies, a novel model in line with the current epidemics process and control measures was proposed, utilizing multisource datasets including cumulative number of reported, death, quarantined and suspected cases. Results show that the trend of the epidemics mainly depends on quarantined and suspected cases.

Findings: The predicted cumulative numbers of quarantined and suspected cases nearly reached static states and their inflection points have already been achieved, with the epidemics peak coming soon. The estimated effective reproduction numbers using model-free and model-based methods are decreasing, as well as new infections, while new reported cases are increasing.

Interpretation: Most infected cases have been quarantined or put in suspected class, which has been ignored in existing models. Further, uncertainty analyses reveal that the epidemics is still uncertain and it is important to continue enhancing the quarantine and isolation strategy and improving the detection rate in mainland China.

Funding: This research was funded by the National Natural Science Foundation of China (grant numbers: 11631012 (YX, ST), 61772017 (ST)), and by the Canada Research Chair Program (grant number: 230720 (JW) and the Natural Sciences and Engineering Research Council of Canada (Grant number:105588-2011 (JW).

Declaration of Interest: The authors declare no conflict of interest.

Ethical Approval: Ethical approval was waived since all data utilized are publicly available.

Keywords: coronavirus; multi-source data; mathematical model; SEIR model

Suggested Citation

Tang, Biao and Xia, Fan and Tang, Sanyi and Bragazzi, Nicola Luigi and Li, Qian and Sun, Xiaodan and Liang, Juhua and Xiao, Yanni and Wu, Jianhong, The Evolution of Quarantined and Suspected Cases Determines the Final Trend of the 2019-nCoV Epidemics Based on Multi-Source Data Analyses (2/9/2020). Available at SSRN: https://ssrn.com/abstract=3537099 or http://dx.doi.org/10.2139/ssrn.3537099

Biao Tang

Xi'an Jiaotong University (XJTU) - The Interdisplinary Research Center for Mathematics and Life Sciences

China

York University - Department of Mathematics and Statistics

4700 Keele Street
Toronto, Ontario M3J 1P3
Canada

Fan Xia

Xi'an Jiaotong University (XJTU) - The Interdisplinary Research Center for Mathematics and Life Sciences

China

Sanyi Tang

Shaanxi Normal University - School of Mathematics and Information Science

China

Nicola Luigi Bragazzi

York University - Laboratory for Industrial and Applied Mathematics

Canada

Qian Li

York University - Laboratory for Industrial and Applied Mathematics

Canada

Xiaodan Sun

Xi'an Jiaotong University (XJTU) - The Interdisplinary Research Center for Mathematics and Life Sciences

China

Juhua Liang

Shaanxi Normal University - School of Mathematics and Information Science

China

Yanni Xiao

Xi'an Jiaotong University (XJTU) - The Interdisplinary Research Center for Mathematics and Life Sciences

China

Xi'an Jiaotong University (XJTU) - School of Mathematics and Statistics ( email )

China

Jianhong Wu (Contact Author)

York University ( email )

Canada

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