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Extended SIR Prediction of the Epidemics Trend of COVID-19 in Italy and Compared with Hunan, China

17 Pages Posted: 31 Mar 2020

See all articles by Jia Wangping

Jia Wangping

Government of the People's Republic of China - Institute of Geriatrics; Government of the People's Republic of China - Department of Military Medical Technology Support

Han Ke

Government of the People's Republic of China - Institute of Geriatrics

Song Yang

Government of the People's Republic of China - Institute of Geriatrics

Cao Wenzhe

Government of the People's Republic of China - Institute of Geriatrics

Shengshu Wang

Government of the People's Republic of China - Institute of Geriatrics

Shanshan Yang

Government of the People's Republic of China - Institute of Geriatrics

Wang Jianwei

Government of the People's Republic of China - Institute of Geriatrics

Kou Fuyin

Government of the People's Republic of China - Institute of Geriatrics

Tai Penggang

Government of the People's Republic of China - Institute of Geriatrics

Li Jing

Government of the People's Republic of China - Institute of Geriatrics

Miao Liu

Beijing Key Laboratory of Aging and Geriatrics - Institute of Geriatrics; National Clinical Research Center for Geriatrics Diseases - State Key Laboratory of Kidney Diseases

He Yao

Government of the People's Republic of China - Institute of Geriatrics

More...

Abstract

Background: Coronavirus Disease 2019 (COVID-19) infection spreads rapidly in Italy. It is important to predict the epidemics trend of COVID-19 epidemic in Italy to help develop public health strategies.

Methods: An infectious disease dynamic extended susceptible-infected-removed (eSIR) model was applied to estimate the epidemic trend in Italy, and Hunan, Province of China was used as a comparative item.

Findings: In the eSIR model, we estimated that there would be 20909 infected cases (95%CI 6164-45384) in Italy under the current preventive situation and the endpoint of the epidemic trend would be Apr 03 (95%CI: Mar 19 to Jun 02). Assuming current preventive measures have been taken on Mar 05 or Mar 10, the number of infected cases would be respectively 10636(95%CI: 2357-23326) and 7373(95%CI: 967-18915), and the endpoint of the epidemic trend would be respectively Mar 16 (95%CI: Mar 9 to Apr 22) and Mar 08 (95%CI: Mar 03 to Apr 03).

Interpretation: It’s suggested that Italy's current strict measures continue to be implemented, and necessary strict public health measures be implemented as soon as possible in other European countries with a high number of COVID-19 cases.

Funding Statement: Army Logistics Emergency Scientific Research Project; Emergency scientific research of the army and the emergency scientific research of Chinese PLA General Hospital (20EP008).

Declaration of Interests: The authors declare no competing interests.

Ethics Approval Statement: These data are collected through public health authorities' announcements and are directly reported public and unidentified patient data, so ethical approval is not required.

Keywords: Coronavirus; infectious disease; prediction; eSIR model

Suggested Citation

Wangping, Jia and Ke, Han and Yang, Song and Wenzhe, Cao and Wang, Shengshu and Yang, Shanshan and Jianwei, Wang and Fuyin, Kou and Penggang, Tai and Jing, Li and Liu, Miao and Yao, He, Extended SIR Prediction of the Epidemics Trend of COVID-19 in Italy and Compared with Hunan, China (3/13/2020). Available at SSRN: https://ssrn.com/abstract=3556691 or http://dx.doi.org/10.2139/ssrn.3556691

Jia Wangping

Government of the People's Republic of China - Institute of Geriatrics

28 Fuxing Road
Beijing, 100853
China

Government of the People's Republic of China - Department of Military Medical Technology Support

Shijiazhuang, Hebei
China

Han Ke

Government of the People's Republic of China - Institute of Geriatrics

28 Fuxing Road
Beijing, 100853
China

Song Yang

Government of the People's Republic of China - Institute of Geriatrics

28 Fuxing Road
Beijing, 100853
China

Cao Wenzhe

Government of the People's Republic of China - Institute of Geriatrics ( email )

28 Fuxing Road
Beijing, 100853
China

Shengshu Wang

Government of the People's Republic of China - Institute of Geriatrics

28 Fuxing Road
Beijing, 100853
China

Shanshan Yang

Government of the People's Republic of China - Institute of Geriatrics

28 Fuxing Road
Beijing, 100853
China

Wang Jianwei

Government of the People's Republic of China - Institute of Geriatrics

28 Fuxing Road
Beijing, 100853
China

Kou Fuyin

Government of the People's Republic of China - Institute of Geriatrics

28 Fuxing Road
Beijing, 100853
China

Tai Penggang

Government of the People's Republic of China - Institute of Geriatrics

28 Fuxing Road
Beijing, 100853
China

Li Jing

Government of the People's Republic of China - Institute of Geriatrics

28 Fuxing Road
Beijing, 100853
China

Miao Liu

Beijing Key Laboratory of Aging and Geriatrics - Institute of Geriatrics ( email )

China

National Clinical Research Center for Geriatrics Diseases - State Key Laboratory of Kidney Diseases ( email )

China

He Yao (Contact Author)

Government of the People's Republic of China - Institute of Geriatrics ( email )

28 Fuxing Road
Beijing, 100853
China

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