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...
Main Authors: | , , , , , , , , , , , , , , , , , , , , , , , , , |
---|---|
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 |
id |
doaj-eef3279586f345abb24984539430cf79 |
---|---|
record_format |
Article |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
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 |
work_keys_str_mv |
AT yifanlin spreadandimpactofcovid19inchinaasystematicreviewandsynthesisofpredictionsfromtransmissiondynamicmodels AT qibinduan spreadandimpactofcovid19inchinaasystematicreviewandsynthesisofpredictionsfromtransmissiondynamicmodels AT qibinduan spreadandimpactofcovid19inchinaasystematicreviewandsynthesisofpredictionsfromtransmissiondynamicmodels AT yiguozhou spreadandimpactofcovid19inchinaasystematicreviewandsynthesisofpredictionsfromtransmissiondynamicmodels AT tanweiyuan spreadandimpactofcovid19inchinaasystematicreviewandsynthesisofpredictionsfromtransmissiondynamicmodels AT peiyangli spreadandimpactofcovid19inchinaasystematicreviewandsynthesisofpredictionsfromtransmissiondynamicmodels AT thomasfitzpatrick spreadandimpactofcovid19inchinaasystematicreviewandsynthesisofpredictionsfromtransmissiondynamicmodels AT leiwenfu spreadandimpactofcovid19inchinaasystematicreviewandsynthesisofpredictionsfromtransmissiondynamicmodels AT anpingfeng spreadandimpactofcovid19inchinaasystematicreviewandsynthesisofpredictionsfromtransmissiondynamicmodels AT ganfengluo spreadandimpactofcovid19inchinaasystematicreviewandsynthesisofpredictionsfromtransmissiondynamicmodels AT yueweizhan spreadandimpactofcovid19inchinaasystematicreviewandsynthesisofpredictionsfromtransmissiondynamicmodels AT bowenliang spreadandimpactofcovid19inchinaasystematicreviewandsynthesisofpredictionsfromtransmissiondynamicmodels AT songfan spreadandimpactofcovid19inchinaasystematicreviewandsynthesisofpredictionsfromtransmissiondynamicmodels AT yonglu spreadandimpactofcovid19inchinaasystematicreviewandsynthesisofpredictionsfromtransmissiondynamicmodels AT bingyiwang spreadandimpactofcovid19inchinaasystematicreviewandsynthesisofpredictionsfromtransmissiondynamicmodels AT bingyiwang spreadandimpactofcovid19inchinaasystematicreviewandsynthesisofpredictionsfromtransmissiondynamicmodels AT bingyiwang spreadandimpactofcovid19inchinaasystematicreviewandsynthesisofpredictionsfromtransmissiondynamicmodels AT zhenyuwang spreadandimpactofcovid19inchinaasystematicreviewandsynthesisofpredictionsfromtransmissiondynamicmodels AT hepingzhao spreadandimpactofcovid19inchinaasystematicreviewandsynthesisofpredictionsfromtransmissiondynamicmodels AT yanxiaogao spreadandimpactofcovid19inchinaasystematicreviewandsynthesisofpredictionsfromtransmissiondynamicmodels AT meijuanli spreadandimpactofcovid19inchinaasystematicreviewandsynthesisofpredictionsfromtransmissiondynamicmodels AT dahuichen spreadandimpactofcovid19inchinaasystematicreviewandsynthesisofpredictionsfromtransmissiondynamicmodels AT xiaotingchen spreadandimpactofcovid19inchinaasystematicreviewandsynthesisofpredictionsfromtransmissiondynamicmodels AT yunlongao spreadandimpactofcovid19inchinaasystematicreviewandsynthesisofpredictionsfromtransmissiondynamicmodels AT linghuali spreadandimpactofcovid19inchinaasystematicreviewandsynthesisofpredictionsfromtransmissiondynamicmodels AT weipingcai spreadandimpactofcovid19inchinaasystematicreviewandsynthesisofpredictionsfromtransmissiondynamicmodels AT xiangjundu spreadandimpactofcovid19inchinaasystematicreviewandsynthesisofpredictionsfromtransmissiondynamicmodels AT yuelongshu spreadandimpactofcovid19inchinaasystematicreviewandsynthesisofpredictionsfromtransmissiondynamicmodels AT huachunzou spreadandimpactofcovid19inchinaasystematicreviewandsynthesisofpredictionsfromtransmissiondynamicmodels AT huachunzou spreadandimpactofcovid19inchinaasystematicreviewandsynthesisofpredictionsfromtransmissiondynamicmodels AT huachunzou spreadandimpactofcovid19inchinaasystematicreviewandsynthesisofpredictionsfromtransmissiondynamicmodels AT huachunzou spreadandimpactofcovid19inchinaasystematicreviewandsynthesisofpredictionsfromtransmissiondynamicmodels |
_version_ |
1724675885570719744 |
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 |