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Utilize State Transition Matrix Model to Predict the Novel Corona Virus Infection Peak and Patient Distribution

17 Pages Posted: 19 Feb 2020

See all articles by Ke Wu

Ke Wu

Shanghai Jiao Tong University (SJTU) - Department of Evidence-Based Medicine; Shanghai Jiao Tong University (SJTU) - Department of Urology

Junhua Zheng

Shanghai Jiao Tong University (SJTU) - Department of Evidence-Based Medicine; Shanghai Jiao Tong University (SJTU) - Department of Urology

Jian Chen

IFE Group; CreditWise Technology Company Limited; Caixin Insight Group; MSCI Inc.

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Abstract

Background: Since December 2019, a pneumonia caused by the 2019 novel coronavirus (2019-nCoV) has broken out in Wuhan, Hubei province, China. The continuous rising of infected cases has imposed overwhelming pressure on public health decision and medical resource allocation in China.

Methods: We used data resource according to cases reported by the National Health Commission of the People’s Republic of China (Jan 25, 2019, to Feb 28, 2020) as the training set to deduce the arrival of the peak infection time and the number of severe and critical cases in Wuhan on subsequent days.

Findings: In very optimistic scenarios (daily NCC decay rate of -10%), the peak time of open inflection cases will arrive around February 23-February 26. At the same time, there will be a peak in the numbers of severely ill and critically ill patients, between 6800-7200 and 1800-2000, respectively. In a relative optimistic scenario (daily NCC decay rate of -5%), the inflection case peak time will arrive around February 28-March 2. The numbers of critically ill and critically ill patients will lie between 7100-7800 and 1900-2200, respectively. In a relatively pessimistic scenario (daily NCC decay rate of -1%), the inflection peak time does not arrive around the end of March. Estimated time is April 6-April 14. The numbers of critically ill and critically ill patients will lie between 8300-9800 and 2200-2700, respectively. We are using the diagnosis rate, mortality rate, cure rate as the 2/8 data. There should be room for improvement, if these metrics continue to improve. In that case, the peak time will arrive earlier than our estimation. Also, the severe and critical case ratios are likely to decline as the virus becomes less toxic and medical conditions improve. If that happens, the peak numbers will be lower than predicted above.

Interpretation: We can infer that we are still not close to the end of this outbreak and the number of critically ill patients is still climbing. Assisting critical care resources in Hubei province requires the government to consider further tilt, and it is vital to make reasonable management of doctors and medical assistance systems to curb the transmission trend.

Funding Statement: This work is funded by the Natural Science Foundation of China (no. 81972393, 81772705, 31570775).

Declaration of Interests: All authors declare no competing interests.

Keywords: State Transition Matrix Model; 2019 Novel Corona Virus; pneumonia; transmission trend

Suggested Citation

Wu, Ke and Zheng, Junhua and Chen, Jian, Utilize State Transition Matrix Model to Predict the Novel Corona Virus Infection Peak and Patient Distribution (2/15/2020). Available at SSRN: https://ssrn.com/abstract=3539658 or http://dx.doi.org/10.2139/ssrn.3539658

Ke Wu (Contact Author)

Shanghai Jiao Tong University (SJTU) - Department of Evidence-Based Medicine

Shanghai
China

Shanghai Jiao Tong University (SJTU) - Department of Urology ( email )

China

Junhua Zheng

Shanghai Jiao Tong University (SJTU) - Department of Evidence-Based Medicine ( email )

Shanghai
China

Shanghai Jiao Tong University (SJTU) - Department of Urology

China

Jian Chen

IFE Group ( email )

51 Monroe St., Suite 1100
Rockville, MD Maryland 20850
United States
3013096560 (Phone)
3013096562 (Fax)

CreditWise Technology Company Limited

Chengdu
China

Caixin Insight Group

Beijing
China

MSCI Inc.

Shanghai
China

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