Spread and Impact of COVID-19 in China: A Systematic Review and Synthesis of Predictions From Transmission-Dynamic Models.
Identifieur interne : 002135 ( Main/Corpus ); précédent : 002134; suivant : 002136Spread and Impact of COVID-19 in China: A Systematic Review and Synthesis of Predictions From Transmission-Dynamic Models.
Auteurs : 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 ZouSource :
- Frontiers in medicine [ 2296-858X ] ; 2020.
Abstract
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.
DOI: 10.3389/fmed.2020.00321
PubMed: 32626719
PubMed Central: PMC7314927
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<author><name sortKey="Lin, Yi Fan" sort="Lin, Yi Fan" uniqKey="Lin Y" first="Yi-Fan" last="Lin">Yi-Fan Lin</name>
<affiliation><nlm:affiliation>School of Public Health (Shenzhen), Sun Yat-sen University, Shenzhen, China.</nlm:affiliation>
</affiliation>
</author>
<author><name sortKey="Duan, Qibin" sort="Duan, Qibin" uniqKey="Duan Q" first="Qibin" last="Duan">Qibin Duan</name>
<affiliation><nlm:affiliation>School of Mathematical Sciences, Queensland University of Technology, Brisbane, QLD, Australia.</nlm:affiliation>
</affiliation>
<affiliation><nlm:affiliation>Kirby Institute, University of New South Wales, Sydney, NSW, Australia.</nlm:affiliation>
</affiliation>
</author>
<author><name sortKey="Zhou, Yiguo" sort="Zhou, Yiguo" uniqKey="Zhou Y" first="Yiguo" last="Zhou">Yiguo Zhou</name>
<affiliation><nlm:affiliation>School of Public Health (Shenzhen), Sun Yat-sen University, Shenzhen, China.</nlm:affiliation>
</affiliation>
</author>
<author><name sortKey="Yuan, Tanwei" sort="Yuan, Tanwei" uniqKey="Yuan T" first="Tanwei" last="Yuan">Tanwei Yuan</name>
<affiliation><nlm:affiliation>School of Public Health (Shenzhen), Sun Yat-sen University, Shenzhen, China.</nlm:affiliation>
</affiliation>
</author>
<author><name sortKey="Li, Peiyang" sort="Li, Peiyang" uniqKey="Li P" first="Peiyang" last="Li">Peiyang Li</name>
<affiliation><nlm:affiliation>School of Public Health, Sun Yat-sen University, Guangzhou, China.</nlm:affiliation>
</affiliation>
</author>
<author><name sortKey="Fitzpatrick, Thomas" sort="Fitzpatrick, Thomas" uniqKey="Fitzpatrick T" first="Thomas" last="Fitzpatrick">Thomas Fitzpatrick</name>
<affiliation><nlm:affiliation>Department of Internal Medicine, University of Washington, Seattle, WA, United States.</nlm:affiliation>
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</author>
<author><name sortKey="Fu, Leiwen" sort="Fu, Leiwen" uniqKey="Fu L" first="Leiwen" last="Fu">Leiwen Fu</name>
<affiliation><nlm:affiliation>School of Public Health (Shenzhen), Sun Yat-sen University, Shenzhen, China.</nlm:affiliation>
</affiliation>
</author>
<author><name sortKey="Feng, Anping" sort="Feng, Anping" uniqKey="Feng A" first="Anping" last="Feng">Anping Feng</name>
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</affiliation>
</author>
<author><name sortKey="Luo, Ganfeng" sort="Luo, Ganfeng" uniqKey="Luo G" first="Ganfeng" last="Luo">Ganfeng Luo</name>
<affiliation><nlm:affiliation>School of Public Health (Shenzhen), Sun Yat-sen University, Shenzhen, China.</nlm:affiliation>
</affiliation>
</author>
<author><name sortKey="Zhan, Yuewei" sort="Zhan, Yuewei" uniqKey="Zhan Y" first="Yuewei" last="Zhan">Yuewei Zhan</name>
<affiliation><nlm:affiliation>School of Public Health (Shenzhen), Sun Yat-sen University, Shenzhen, China.</nlm:affiliation>
</affiliation>
</author>
<author><name sortKey="Liang, Bowen" sort="Liang, Bowen" uniqKey="Liang B" first="Bowen" last="Liang">Bowen Liang</name>
<affiliation><nlm:affiliation>School of Public Health (Shenzhen), Sun Yat-sen University, Shenzhen, China.</nlm:affiliation>
</affiliation>
</author>
<author><name sortKey="Fan, Song" sort="Fan, Song" uniqKey="Fan S" first="Song" last="Fan">Song Fan</name>
<affiliation><nlm:affiliation>School of Public Health, Sun Yat-sen University, Guangzhou, China.</nlm:affiliation>
</affiliation>
</author>
<author><name sortKey="Lu, Yong" sort="Lu, Yong" uniqKey="Lu Y" first="Yong" last="Lu">Yong Lu</name>
<affiliation><nlm:affiliation>School of Public Health, Sun Yat-sen University, Guangzhou, China.</nlm:affiliation>
</affiliation>
</author>
<author><name sortKey="Wang, Bingyi" sort="Wang, Bingyi" uniqKey="Wang B" first="Bingyi" last="Wang">Bingyi Wang</name>
<affiliation><nlm:affiliation>School of Public Health (Shenzhen), Sun Yat-sen University, Shenzhen, China.</nlm:affiliation>
</affiliation>
<affiliation><nlm:affiliation>State Key Laboratory of Food Nutrition and Safety, Tianjin University of Science and Technology, Tianjin, China.</nlm:affiliation>
</affiliation>
<affiliation><nlm:affiliation>College of Food Science and Technology, Tianjin University of Science and Technology, Tianjin, China.</nlm:affiliation>
</affiliation>
</author>
<author><name sortKey="Wang, Zhenyu" sort="Wang, Zhenyu" uniqKey="Wang Z" first="Zhenyu" last="Wang">Zhenyu Wang</name>
<affiliation><nlm:affiliation>School of Public Health, Sun Yat-sen University, Guangzhou, China.</nlm:affiliation>
</affiliation>
</author>
<author><name sortKey="Zhao, Heping" sort="Zhao, Heping" uniqKey="Zhao H" first="Heping" last="Zhao">Heping Zhao</name>
<affiliation><nlm:affiliation>School of Public Health (Shenzhen), Sun Yat-sen University, Shenzhen, China.</nlm:affiliation>
</affiliation>
</author>
<author><name sortKey="Gao, Yanxiao" sort="Gao, Yanxiao" uniqKey="Gao Y" first="Yanxiao" last="Gao">Yanxiao Gao</name>
<affiliation><nlm:affiliation>School of Public Health (Shenzhen), Sun Yat-sen University, Shenzhen, China.</nlm:affiliation>
</affiliation>
</author>
<author><name sortKey="Li, Meijuan" sort="Li, Meijuan" uniqKey="Li M" first="Meijuan" last="Li">Meijuan Li</name>
<affiliation><nlm:affiliation>School of Public Health (Shenzhen), Sun Yat-sen University, Shenzhen, China.</nlm:affiliation>
</affiliation>
</author>
<author><name sortKey="Chen, Dahui" sort="Chen, Dahui" uniqKey="Chen D" first="Dahui" last="Chen">Dahui Chen</name>
<affiliation><nlm:affiliation>School of Public Health (Shenzhen), Sun Yat-sen University, Shenzhen, China.</nlm:affiliation>
</affiliation>
</author>
<author><name sortKey="Chen, Xiaoting" sort="Chen, Xiaoting" uniqKey="Chen X" first="Xiaoting" last="Chen">Xiaoting Chen</name>
<affiliation><nlm:affiliation>Guangzhou Eighth People's Hospital, Guangzhou Medical University, Guangzhou, China.</nlm:affiliation>
</affiliation>
</author>
<author><name sortKey="Ao, Yunlong" sort="Ao, Yunlong" uniqKey="Ao Y" first="Yunlong" last="Ao">Yunlong Ao</name>
<affiliation><nlm:affiliation>Guangzhou Eighth People's Hospital, Guangzhou Medical University, Guangzhou, China.</nlm:affiliation>
</affiliation>
</author>
<author><name sortKey="Li, Linghua" sort="Li, Linghua" uniqKey="Li L" first="Linghua" last="Li">Linghua Li</name>
<affiliation><nlm:affiliation>Guangzhou Eighth People's Hospital, Guangzhou Medical University, Guangzhou, China.</nlm:affiliation>
</affiliation>
</author>
<author><name sortKey="Cai, Weiping" sort="Cai, Weiping" uniqKey="Cai W" first="Weiping" last="Cai">Weiping Cai</name>
<affiliation><nlm:affiliation>Guangzhou Eighth People's Hospital, Guangzhou Medical University, Guangzhou, China.</nlm:affiliation>
</affiliation>
</author>
<author><name sortKey="Du, Xiangjun" sort="Du, Xiangjun" uniqKey="Du X" first="Xiangjun" last="Du">Xiangjun Du</name>
<affiliation><nlm:affiliation>School of Public Health (Shenzhen), Sun Yat-sen University, Shenzhen, China.</nlm:affiliation>
</affiliation>
</author>
<author><name sortKey="Shu, Yuelong" sort="Shu, Yuelong" uniqKey="Shu Y" first="Yuelong" last="Shu">Yuelong Shu</name>
<affiliation><nlm:affiliation>School of Public Health (Shenzhen), Sun Yat-sen University, Shenzhen, China.</nlm:affiliation>
</affiliation>
</author>
<author><name sortKey="Zou, Huachun" sort="Zou, Huachun" uniqKey="Zou H" first="Huachun" last="Zou">Huachun Zou</name>
<affiliation><nlm:affiliation>School of Public Health (Shenzhen), Sun Yat-sen University, Shenzhen, China.</nlm:affiliation>
</affiliation>
<affiliation><nlm:affiliation>Kirby Institute, University of New South Wales, Sydney, NSW, Australia.</nlm:affiliation>
</affiliation>
<affiliation><nlm:affiliation>Shenzhen Center for Disease Control and Prevention, Shenzhen, China.</nlm:affiliation>
</affiliation>
<affiliation><nlm:affiliation>School of Public Health, Shanghai Jiao Tong University, Shanghai, China.</nlm:affiliation>
</affiliation>
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<sourceDesc><biblStruct><analytic><title xml:lang="en">Spread and Impact of COVID-19 in China: A Systematic Review and Synthesis of Predictions From Transmission-Dynamic Models.</title>
<author><name sortKey="Lin, Yi Fan" sort="Lin, Yi Fan" uniqKey="Lin Y" first="Yi-Fan" last="Lin">Yi-Fan Lin</name>
<affiliation><nlm:affiliation>School of Public Health (Shenzhen), Sun Yat-sen University, Shenzhen, China.</nlm:affiliation>
</affiliation>
</author>
<author><name sortKey="Duan, Qibin" sort="Duan, Qibin" uniqKey="Duan Q" first="Qibin" last="Duan">Qibin Duan</name>
<affiliation><nlm:affiliation>School of Mathematical Sciences, Queensland University of Technology, Brisbane, QLD, Australia.</nlm:affiliation>
</affiliation>
<affiliation><nlm:affiliation>Kirby Institute, University of New South Wales, Sydney, NSW, Australia.</nlm:affiliation>
</affiliation>
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<author><name sortKey="Zhou, Yiguo" sort="Zhou, Yiguo" uniqKey="Zhou Y" first="Yiguo" last="Zhou">Yiguo Zhou</name>
<affiliation><nlm:affiliation>School of Public Health (Shenzhen), Sun Yat-sen University, Shenzhen, China.</nlm:affiliation>
</affiliation>
</author>
<author><name sortKey="Yuan, Tanwei" sort="Yuan, Tanwei" uniqKey="Yuan T" first="Tanwei" last="Yuan">Tanwei Yuan</name>
<affiliation><nlm:affiliation>School of Public Health (Shenzhen), Sun Yat-sen University, Shenzhen, China.</nlm:affiliation>
</affiliation>
</author>
<author><name sortKey="Li, Peiyang" sort="Li, Peiyang" uniqKey="Li P" first="Peiyang" last="Li">Peiyang Li</name>
<affiliation><nlm:affiliation>School of Public Health, Sun Yat-sen University, Guangzhou, China.</nlm:affiliation>
</affiliation>
</author>
<author><name sortKey="Fitzpatrick, Thomas" sort="Fitzpatrick, Thomas" uniqKey="Fitzpatrick T" first="Thomas" last="Fitzpatrick">Thomas Fitzpatrick</name>
<affiliation><nlm:affiliation>Department of Internal Medicine, University of Washington, Seattle, WA, United States.</nlm:affiliation>
</affiliation>
</author>
<author><name sortKey="Fu, Leiwen" sort="Fu, Leiwen" uniqKey="Fu L" first="Leiwen" last="Fu">Leiwen Fu</name>
<affiliation><nlm:affiliation>School of Public Health (Shenzhen), Sun Yat-sen University, Shenzhen, China.</nlm:affiliation>
</affiliation>
</author>
<author><name sortKey="Feng, Anping" sort="Feng, Anping" uniqKey="Feng A" first="Anping" last="Feng">Anping Feng</name>
<affiliation><nlm:affiliation>School of Public Health (Shenzhen), Sun Yat-sen University, Shenzhen, China.</nlm:affiliation>
</affiliation>
</author>
<author><name sortKey="Luo, Ganfeng" sort="Luo, Ganfeng" uniqKey="Luo G" first="Ganfeng" last="Luo">Ganfeng Luo</name>
<affiliation><nlm:affiliation>School of Public Health (Shenzhen), Sun Yat-sen University, Shenzhen, China.</nlm:affiliation>
</affiliation>
</author>
<author><name sortKey="Zhan, Yuewei" sort="Zhan, Yuewei" uniqKey="Zhan Y" first="Yuewei" last="Zhan">Yuewei Zhan</name>
<affiliation><nlm:affiliation>School of Public Health (Shenzhen), Sun Yat-sen University, Shenzhen, China.</nlm:affiliation>
</affiliation>
</author>
<author><name sortKey="Liang, Bowen" sort="Liang, Bowen" uniqKey="Liang B" first="Bowen" last="Liang">Bowen Liang</name>
<affiliation><nlm:affiliation>School of Public Health (Shenzhen), Sun Yat-sen University, Shenzhen, China.</nlm:affiliation>
</affiliation>
</author>
<author><name sortKey="Fan, Song" sort="Fan, Song" uniqKey="Fan S" first="Song" last="Fan">Song Fan</name>
<affiliation><nlm:affiliation>School of Public Health, Sun Yat-sen University, Guangzhou, China.</nlm:affiliation>
</affiliation>
</author>
<author><name sortKey="Lu, Yong" sort="Lu, Yong" uniqKey="Lu Y" first="Yong" last="Lu">Yong Lu</name>
<affiliation><nlm:affiliation>School of Public Health, Sun Yat-sen University, Guangzhou, China.</nlm:affiliation>
</affiliation>
</author>
<author><name sortKey="Wang, Bingyi" sort="Wang, Bingyi" uniqKey="Wang B" first="Bingyi" last="Wang">Bingyi Wang</name>
<affiliation><nlm:affiliation>School of Public Health (Shenzhen), Sun Yat-sen University, Shenzhen, China.</nlm:affiliation>
</affiliation>
<affiliation><nlm:affiliation>State Key Laboratory of Food Nutrition and Safety, Tianjin University of Science and Technology, Tianjin, China.</nlm:affiliation>
</affiliation>
<affiliation><nlm:affiliation>College of Food Science and Technology, Tianjin University of Science and Technology, Tianjin, China.</nlm:affiliation>
</affiliation>
</author>
<author><name sortKey="Wang, Zhenyu" sort="Wang, Zhenyu" uniqKey="Wang Z" first="Zhenyu" last="Wang">Zhenyu Wang</name>
<affiliation><nlm:affiliation>School of Public Health, Sun Yat-sen University, Guangzhou, China.</nlm:affiliation>
</affiliation>
</author>
<author><name sortKey="Zhao, Heping" sort="Zhao, Heping" uniqKey="Zhao H" first="Heping" last="Zhao">Heping Zhao</name>
<affiliation><nlm:affiliation>School of Public Health (Shenzhen), Sun Yat-sen University, Shenzhen, China.</nlm:affiliation>
</affiliation>
</author>
<author><name sortKey="Gao, Yanxiao" sort="Gao, Yanxiao" uniqKey="Gao Y" first="Yanxiao" last="Gao">Yanxiao Gao</name>
<affiliation><nlm:affiliation>School of Public Health (Shenzhen), Sun Yat-sen University, Shenzhen, China.</nlm:affiliation>
</affiliation>
</author>
<author><name sortKey="Li, Meijuan" sort="Li, Meijuan" uniqKey="Li M" first="Meijuan" last="Li">Meijuan Li</name>
<affiliation><nlm:affiliation>School of Public Health (Shenzhen), Sun Yat-sen University, Shenzhen, China.</nlm:affiliation>
</affiliation>
</author>
<author><name sortKey="Chen, Dahui" sort="Chen, Dahui" uniqKey="Chen D" first="Dahui" last="Chen">Dahui Chen</name>
<affiliation><nlm:affiliation>School of Public Health (Shenzhen), Sun Yat-sen University, Shenzhen, China.</nlm:affiliation>
</affiliation>
</author>
<author><name sortKey="Chen, Xiaoting" sort="Chen, Xiaoting" uniqKey="Chen X" first="Xiaoting" last="Chen">Xiaoting Chen</name>
<affiliation><nlm:affiliation>Guangzhou Eighth People's Hospital, Guangzhou Medical University, Guangzhou, China.</nlm:affiliation>
</affiliation>
</author>
<author><name sortKey="Ao, Yunlong" sort="Ao, Yunlong" uniqKey="Ao Y" first="Yunlong" last="Ao">Yunlong Ao</name>
<affiliation><nlm:affiliation>Guangzhou Eighth People's Hospital, Guangzhou Medical University, Guangzhou, China.</nlm:affiliation>
</affiliation>
</author>
<author><name sortKey="Li, Linghua" sort="Li, Linghua" uniqKey="Li L" first="Linghua" last="Li">Linghua Li</name>
<affiliation><nlm:affiliation>Guangzhou Eighth People's Hospital, Guangzhou Medical University, Guangzhou, China.</nlm:affiliation>
</affiliation>
</author>
<author><name sortKey="Cai, Weiping" sort="Cai, Weiping" uniqKey="Cai W" first="Weiping" last="Cai">Weiping Cai</name>
<affiliation><nlm:affiliation>Guangzhou Eighth People's Hospital, Guangzhou Medical University, Guangzhou, China.</nlm:affiliation>
</affiliation>
</author>
<author><name sortKey="Du, Xiangjun" sort="Du, Xiangjun" uniqKey="Du X" first="Xiangjun" last="Du">Xiangjun Du</name>
<affiliation><nlm:affiliation>School of Public Health (Shenzhen), Sun Yat-sen University, Shenzhen, China.</nlm:affiliation>
</affiliation>
</author>
<author><name sortKey="Shu, Yuelong" sort="Shu, Yuelong" uniqKey="Shu Y" first="Yuelong" last="Shu">Yuelong Shu</name>
<affiliation><nlm:affiliation>School of Public Health (Shenzhen), Sun Yat-sen University, Shenzhen, China.</nlm:affiliation>
</affiliation>
</author>
<author><name sortKey="Zou, Huachun" sort="Zou, Huachun" uniqKey="Zou H" first="Huachun" last="Zou">Huachun Zou</name>
<affiliation><nlm:affiliation>School of Public Health (Shenzhen), Sun Yat-sen University, Shenzhen, China.</nlm:affiliation>
</affiliation>
<affiliation><nlm:affiliation>Kirby Institute, University of New South Wales, Sydney, NSW, Australia.</nlm:affiliation>
</affiliation>
<affiliation><nlm:affiliation>Shenzhen Center for Disease Control and Prevention, Shenzhen, China.</nlm:affiliation>
</affiliation>
<affiliation><nlm:affiliation>School of Public Health, Shanghai Jiao Tong University, Shanghai, China.</nlm:affiliation>
</affiliation>
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<front><div type="abstract" xml:lang="en"><b>Background:</b>
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. <b>Methods:</b>
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. <b>Results:</b>
Fifty-two articles involving 75 mathematical or statistical models were included in our systematic review. The overall median basic reproduction number (R<sub>0</sub>
) was 3.77 [interquartile range (IQR) 2.78-5.13], which dropped to a controlled reproduction number (R<sub>c</sub>
) 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. <b>Conclusions:</b>
Our analysis found a sustained R<sub>c</sub>
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.</div>
</front>
</TEI>
<pubmed><MedlineCitation Status="PubMed-not-MEDLINE" Owner="NLM"><PMID Version="1">32626719</PMID>
<DateRevised><Year>2020</Year>
<Month>09</Month>
<Day>28</Day>
</DateRevised>
<Article PubModel="Electronic-eCollection"><Journal><ISSN IssnType="Print">2296-858X</ISSN>
<JournalIssue CitedMedium="Print"><Volume>7</Volume>
<PubDate><Year>2020</Year>
</PubDate>
</JournalIssue>
<Title>Frontiers in medicine</Title>
<ISOAbbreviation>Front Med (Lausanne)</ISOAbbreviation>
</Journal>
<ArticleTitle>Spread and Impact of COVID-19 in China: A Systematic Review and Synthesis of Predictions From Transmission-Dynamic Models.</ArticleTitle>
<Pagination><MedlinePgn>321</MedlinePgn>
</Pagination>
<ELocationID EIdType="doi" ValidYN="Y">10.3389/fmed.2020.00321</ELocationID>
<Abstract><AbstractText><b>Background:</b>
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. <b>Methods:</b>
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. <b>Results:</b>
Fifty-two articles involving 75 mathematical or statistical models were included in our systematic review. The overall median basic reproduction number (R<sub>0</sub>
) was 3.77 [interquartile range (IQR) 2.78-5.13], which dropped to a controlled reproduction number (R<sub>c</sub>
) 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. <b>Conclusions:</b>
Our analysis found a sustained R<sub>c</sub>
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.</AbstractText>
<CopyrightInformation>Copyright © 2020 Lin, Duan, Zhou, Yuan, Li, Fitzpatrick, Fu, Feng, Luo, Zhan, Liang, Fan, Lu, Wang, Wang, Zhao, Gao, Li, Chen, Chen, Ao, Li, Cai, Du, Shu and Zou.</CopyrightInformation>
</Abstract>
<AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>Lin</LastName>
<ForeName>Yi-Fan</ForeName>
<Initials>YF</Initials>
<AffiliationInfo><Affiliation>School of Public Health (Shenzhen), Sun Yat-sen University, Shenzhen, China.</Affiliation>
</AffiliationInfo>
</Author>
<Author ValidYN="Y"><LastName>Duan</LastName>
<ForeName>Qibin</ForeName>
<Initials>Q</Initials>
<AffiliationInfo><Affiliation>School of Mathematical Sciences, Queensland University of Technology, Brisbane, QLD, Australia.</Affiliation>
</AffiliationInfo>
<AffiliationInfo><Affiliation>Kirby Institute, University of New South Wales, Sydney, NSW, Australia.</Affiliation>
</AffiliationInfo>
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<Author ValidYN="Y"><LastName>Zhou</LastName>
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