Spread and Impact of COVID-19 in China: A Systematic Review and Synthesis of Predictions From Transmission-Dynamic Models

Background: Coronavirus disease 2019 (COVID-19) was first identified in Wuhan, China, in December 2019 and quickly spread throughout China and the rest of the world. Many mathematical models have been developed to understand and predict the infectiousness of COVID-19. We aim to summarize these model...

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Main Authors: Yi-Fan Lin, Qibin Duan, Yiguo Zhou, Tanwei Yuan, Peiyang Li, Thomas Fitzpatrick, Leiwen Fu, Anping Feng, Ganfeng Luo, Yuewei Zhan, Bowen Liang, Song Fan, Yong Lu, Bingyi Wang, Zhenyu Wang, Heping Zhao, Yanxiao Gao, Meijuan Li, Dahui Chen, Xiaoting Chen, Yunlong Ao, Linghua Li, Weiping Cai, Xiangjun Du, Yuelong Shu, Huachun Zou
Format: Article
Language:English
Published: Frontiers Media S.A. 2020-06-01
Series:Frontiers in Medicine
Subjects:
Online Access:https://www.frontiersin.org/article/10.3389/fmed.2020.00321/full
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language English
format Article
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author Yi-Fan Lin
Qibin Duan
Qibin Duan
Yiguo Zhou
Tanwei Yuan
Peiyang Li
Thomas Fitzpatrick
Leiwen Fu
Anping Feng
Ganfeng Luo
Yuewei Zhan
Bowen Liang
Song Fan
Yong Lu
Bingyi Wang
Bingyi Wang
Bingyi Wang
Zhenyu Wang
Heping Zhao
Yanxiao Gao
Meijuan Li
Dahui Chen
Xiaoting Chen
Yunlong Ao
Linghua Li
Weiping Cai
Xiangjun Du
Yuelong Shu
Huachun Zou
Huachun Zou
Huachun Zou
Huachun Zou
spellingShingle Yi-Fan Lin
Qibin Duan
Qibin Duan
Yiguo Zhou
Tanwei Yuan
Peiyang Li
Thomas Fitzpatrick
Leiwen Fu
Anping Feng
Ganfeng Luo
Yuewei Zhan
Bowen Liang
Song Fan
Yong Lu
Bingyi Wang
Bingyi Wang
Bingyi Wang
Zhenyu Wang
Heping Zhao
Yanxiao Gao
Meijuan Li
Dahui Chen
Xiaoting Chen
Yunlong Ao
Linghua Li
Weiping Cai
Xiangjun Du
Yuelong Shu
Huachun Zou
Huachun Zou
Huachun Zou
Huachun Zou
Spread and Impact of COVID-19 in China: A Systematic Review and Synthesis of Predictions From Transmission-Dynamic Models
Frontiers in Medicine
the reproduction number
incubation
infectious period
fatality
mathematical model
author_facet Yi-Fan Lin
Qibin Duan
Qibin Duan
Yiguo Zhou
Tanwei Yuan
Peiyang Li
Thomas Fitzpatrick
Leiwen Fu
Anping Feng
Ganfeng Luo
Yuewei Zhan
Bowen Liang
Song Fan
Yong Lu
Bingyi Wang
Bingyi Wang
Bingyi Wang
Zhenyu Wang
Heping Zhao
Yanxiao Gao
Meijuan Li
Dahui Chen
Xiaoting Chen
Yunlong Ao
Linghua Li
Weiping Cai
Xiangjun Du
Yuelong Shu
Huachun Zou
Huachun Zou
Huachun Zou
Huachun Zou
author_sort Yi-Fan Lin
title Spread and Impact of COVID-19 in China: A Systematic Review and Synthesis of Predictions From Transmission-Dynamic Models
title_short Spread and Impact of COVID-19 in China: A Systematic Review and Synthesis of Predictions From Transmission-Dynamic Models
title_full Spread and Impact of COVID-19 in China: A Systematic Review and Synthesis of Predictions From Transmission-Dynamic Models
title_fullStr Spread and Impact of COVID-19 in China: A Systematic Review and Synthesis of Predictions From Transmission-Dynamic Models
title_full_unstemmed Spread and Impact of COVID-19 in China: A Systematic Review and Synthesis of Predictions From Transmission-Dynamic Models
title_sort spread and impact of covid-19 in china: a systematic review and synthesis of predictions from transmission-dynamic models
publisher Frontiers Media S.A.
series Frontiers in Medicine
issn 2296-858X
publishDate 2020-06-01
description Background: Coronavirus disease 2019 (COVID-19) was first identified in Wuhan, China, in December 2019 and quickly spread throughout China and the rest of the world. Many mathematical models have been developed to understand and predict the infectiousness of COVID-19. We aim to summarize these models to inform efforts to manage the current outbreak.Methods: We searched PubMed, Web of science, EMBASE, bioRxiv, medRxiv, arXiv, Preprints, and National Knowledge Infrastructure (Chinese database) for relevant studies published between 1 December 2019 and 21 February 2020. References were screened for additional publications. Crucial indicators were extracted and analysed. We also built a mathematical model for the evolution of the epidemic in Wuhan that synthesised extracted indicators.Results: Fifty-two articles involving 75 mathematical or statistical models were included in our systematic review. The overall median basic reproduction number (R0) was 3.77 [interquartile range (IQR) 2.78–5.13], which dropped to a controlled reproduction number (Rc) of 1.88 (IQR 1.41–2.24) after city lockdown. The median incubation and infectious periods were 5.90 (IQR 4.78–6.25) and 9.94 (IQR 3.93–13.50) days, respectively. The median case-fatality rate (CFR) was 2.9% (IQR 2.3–5.4%). Our mathematical model showed that, in Wuhan, the peak time of infection is likely to be March 2020 with a median size of 98,333 infected cases (range 55,225–188,284). The earliest elimination of ongoing transmission is likely to be achieved around 7 May 2020.Conclusions: Our analysis found a sustained Rc and prolonged incubation/ infectious periods, suggesting COVID-19 is highly infectious. Although interventions in China have been effective in controlling secondary transmission, sustained global efforts are needed to contain an emerging pandemic. Alternative interventions can be explored using modelling studies to better inform policymaking as the outbreak continues.
topic the reproduction number
incubation
infectious period
fatality
mathematical model
url https://www.frontiersin.org/article/10.3389/fmed.2020.00321/full
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spelling doaj-eef3279586f345abb24984539430cf792020-11-25T03:06:02ZengFrontiers Media S.A.Frontiers in Medicine2296-858X2020-06-01710.3389/fmed.2020.00321544224Spread and Impact of COVID-19 in China: A Systematic Review and Synthesis of Predictions From Transmission-Dynamic ModelsYi-Fan Lin0Qibin Duan1Qibin Duan2Yiguo Zhou3Tanwei Yuan4Peiyang Li5Thomas Fitzpatrick6Leiwen Fu7Anping Feng8Ganfeng Luo9Yuewei Zhan10Bowen Liang11Song Fan12Yong Lu13Bingyi Wang14Bingyi Wang15Bingyi Wang16Zhenyu Wang17Heping Zhao18Yanxiao Gao19Meijuan Li20Dahui Chen21Xiaoting Chen22Yunlong Ao23Linghua Li24Weiping Cai25Xiangjun Du26Yuelong Shu27Huachun Zou28Huachun Zou29Huachun Zou30Huachun Zou31School of Public Health (Shenzhen), Sun Yat-sen University, Shenzhen, ChinaSchool of Mathematical Sciences, Queensland University of Technology, Brisbane, QLD, AustraliaKirby Institute, University of New South Wales, Sydney, NSW, AustraliaSchool of Public Health (Shenzhen), Sun Yat-sen University, Shenzhen, ChinaSchool of Public Health (Shenzhen), Sun Yat-sen University, Shenzhen, ChinaSchool of Public Health, Sun Yat-sen University, Guangzhou, ChinaDepartment of Internal Medicine, University of Washington, Seattle, WA, United StatesSchool of Public Health (Shenzhen), Sun Yat-sen University, Shenzhen, ChinaSchool of Public Health (Shenzhen), Sun Yat-sen University, Shenzhen, ChinaSchool of Public Health (Shenzhen), Sun Yat-sen University, Shenzhen, ChinaSchool of Public Health (Shenzhen), Sun Yat-sen University, Shenzhen, ChinaSchool of Public Health (Shenzhen), Sun Yat-sen University, Shenzhen, ChinaSchool of Public Health, Sun Yat-sen University, Guangzhou, ChinaSchool of Public Health, Sun Yat-sen University, Guangzhou, ChinaSchool of Public Health (Shenzhen), Sun Yat-sen University, Shenzhen, ChinaState Key Laboratory of Food Nutrition and Safety, Tianjin University of Science and Technology, Tianjin, ChinaCollege of Food Science and Technology, Tianjin University of Science and Technology, Tianjin, ChinaSchool of Public Health, Sun Yat-sen University, Guangzhou, ChinaSchool of Public Health (Shenzhen), Sun Yat-sen University, Shenzhen, ChinaSchool of Public Health (Shenzhen), Sun Yat-sen University, Shenzhen, ChinaSchool of Public Health (Shenzhen), Sun Yat-sen University, Shenzhen, ChinaSchool of Public Health (Shenzhen), Sun Yat-sen University, Shenzhen, ChinaGuangzhou Eighth People's Hospital, Guangzhou Medical University, Guangzhou, ChinaGuangzhou Eighth People's Hospital, Guangzhou Medical University, Guangzhou, ChinaGuangzhou Eighth People's Hospital, Guangzhou Medical University, Guangzhou, ChinaGuangzhou Eighth People's Hospital, Guangzhou Medical University, Guangzhou, ChinaSchool of Public Health (Shenzhen), Sun Yat-sen University, Shenzhen, ChinaSchool of Public Health (Shenzhen), Sun Yat-sen University, Shenzhen, ChinaSchool of Public Health (Shenzhen), Sun Yat-sen University, Shenzhen, ChinaKirby Institute, University of New South Wales, Sydney, NSW, AustraliaShenzhen Center for Disease Control and Prevention, Shenzhen, China0School of Public Health, Shanghai Jiao Tong University, Shanghai, ChinaBackground: Coronavirus disease 2019 (COVID-19) was first identified in Wuhan, China, in December 2019 and quickly spread throughout China and the rest of the world. Many mathematical models have been developed to understand and predict the infectiousness of COVID-19. We aim to summarize these models to inform efforts to manage the current outbreak.Methods: We searched PubMed, Web of science, EMBASE, bioRxiv, medRxiv, arXiv, Preprints, and National Knowledge Infrastructure (Chinese database) for relevant studies published between 1 December 2019 and 21 February 2020. References were screened for additional publications. Crucial indicators were extracted and analysed. We also built a mathematical model for the evolution of the epidemic in Wuhan that synthesised extracted indicators.Results: Fifty-two articles involving 75 mathematical or statistical models were included in our systematic review. The overall median basic reproduction number (R0) was 3.77 [interquartile range (IQR) 2.78–5.13], which dropped to a controlled reproduction number (Rc) of 1.88 (IQR 1.41–2.24) after city lockdown. The median incubation and infectious periods were 5.90 (IQR 4.78–6.25) and 9.94 (IQR 3.93–13.50) days, respectively. The median case-fatality rate (CFR) was 2.9% (IQR 2.3–5.4%). Our mathematical model showed that, in Wuhan, the peak time of infection is likely to be March 2020 with a median size of 98,333 infected cases (range 55,225–188,284). The earliest elimination of ongoing transmission is likely to be achieved around 7 May 2020.Conclusions: Our analysis found a sustained Rc and prolonged incubation/ infectious periods, suggesting COVID-19 is highly infectious. Although interventions in China have been effective in controlling secondary transmission, sustained global efforts are needed to contain an emerging pandemic. Alternative interventions can be explored using modelling studies to better inform policymaking as the outbreak continues.https://www.frontiersin.org/article/10.3389/fmed.2020.00321/fullthe reproduction numberincubationinfectious periodfatalitymathematical model