Imitation dynamics in the mitigation of the novel coronavirus disease (COVID-19) outbreak in Wuhan, China from 2019 to 2020.
Identifieur interne : 002709 ( Main/Corpus ); précédent : 002708; suivant : 002710Imitation dynamics in the mitigation of the novel coronavirus disease (COVID-19) outbreak in Wuhan, China from 2019 to 2020.
Auteurs : Shi Zhao ; Lewi Stone ; Daozhou Gao ; Salihu S. Musa ; Marc K C. Chong ; Daihai He ; Maggie H. WangSource :
- Annals of translational medicine [ 2305-5839 ] ; 2020.
Abstract
Background
The coronavirus disease 2019 (COVID-19) was first identified in Wuhan, China on December 2019 in patients presenting with atypical pneumonia. Although 'city-lockdown' policy reduced the spatial spreading of the COVID-19, the city-level outbreaks within each city remain a major concern to be addressed. The local or regional level disease control mainly depends on individuals self-administered infection prevention actions. The contradiction between choice of taking infection prevention actions or not makes the elimination difficult under a voluntary acting scheme, and represents a clash between the optimal choice of action for the individual interest and group interests.
Methods
We develop a compartmental epidemic model based on the classic susceptible-exposed-infectious-recovered model and use this to fit the data. Behavioral imitation through a game theoretical decision-making process is incorporated to study and project the dynamics of the COVID-19 outbreak in Wuhan, China. By varying the key model parameters, we explore the probable course of the outbreak in terms of size and timing under several public interventions in improving public awareness and sensitivity to the infection risk as well as their potential impact.
Results
We estimate the basic reproduction number,
Conclusions
Through an imitating social learning process, individual-level behavioral change on taking infection prevention actions have the potentials to significantly reduce the COVID-19 outbreak in terms of size and timing at city-level. Timely and substantially resources and supports for improving the willingness-to-act and conducts of self-administered infection prevention actions are recommended to reduce to the COVID-19 associated risks.
DOI: 10.21037/atm.2020.03.168
PubMed: 32395492
PubMed Central: PMC7210122
Links to Exploration step
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Musa</name><affiliation><nlm:affiliation>Department of Applied Mathematics, Hong Kong Polytechnic University, Hong Kong, China.</nlm:affiliation></affiliation></author><author><name sortKey="Chong, Marc K C" sort="Chong, Marc K C" uniqKey="Chong M" first="Marc K C" last="Chong">Marc K C. Chong</name><affiliation><nlm:affiliation>JC School of Public Health and Primary Care, Chinese University of Hong Kong, Hong Kong, China.</nlm:affiliation></affiliation><affiliation><nlm:affiliation>Shenzhen Research Institute of Chinese University of Hong Kong, Shenzhen 518060, China.</nlm:affiliation></affiliation></author><author><name sortKey="He, Daihai" sort="He, Daihai" uniqKey="He D" first="Daihai" last="He">Daihai He</name><affiliation><nlm:affiliation>Department of Applied Mathematics, Hong Kong Polytechnic University, Hong Kong, China.</nlm:affiliation></affiliation></author><author><name sortKey="Wang, Maggie H" sort="Wang, Maggie H" uniqKey="Wang M" first="Maggie H" last="Wang">Maggie H. Wang</name><affiliation><nlm:affiliation>JC School of Public Health and Primary Care, Chinese University of Hong Kong, Hong Kong, China.</nlm:affiliation></affiliation><affiliation><nlm:affiliation>Shenzhen Research Institute of Chinese University of Hong Kong, Shenzhen 518060, China.</nlm:affiliation></affiliation></author></titleStmt><publicationStmt><idno type="wicri:source">PubMed</idno><date when="2020">2020</date><idno type="RBID">pubmed:32395492</idno><idno type="pmid">32395492</idno><idno type="doi">10.21037/atm.2020.03.168</idno><idno type="pmc">PMC7210122</idno><idno type="wicri:Area/Main/Corpus">002709</idno><idno type="wicri:explorRef" wicri:stream="Main" wicri:step="Corpus" wicri:corpus="PubMed">002709</idno></publicationStmt><sourceDesc><biblStruct><analytic><title xml:lang="en">Imitation dynamics in the mitigation of the novel coronavirus disease (COVID-19) outbreak in Wuhan, China from 2019 to 2020.</title><author><name sortKey="Zhao, Shi" sort="Zhao, Shi" uniqKey="Zhao S" first="Shi" last="Zhao">Shi Zhao</name><affiliation><nlm:affiliation>JC School of Public Health and Primary Care, Chinese University of Hong Kong, Hong Kong, China.</nlm:affiliation></affiliation><affiliation><nlm:affiliation>Shenzhen Research Institute of Chinese University of Hong Kong, Shenzhen 518060, China.</nlm:affiliation></affiliation></author><author><name sortKey="Stone, Lewi" sort="Stone, Lewi" uniqKey="Stone L" first="Lewi" last="Stone">Lewi Stone</name><affiliation><nlm:affiliation>School of Mathematical and Geospatial Sciences, RMIT University, Melbourne, Australia.</nlm:affiliation></affiliation><affiliation><nlm:affiliation>Biomathematics Unit, Department of Zoology, Tel Aviv University, Ramat Aviv, Israel.</nlm:affiliation></affiliation></author><author><name sortKey="Gao, Daozhou" sort="Gao, Daozhou" uniqKey="Gao D" first="Daozhou" last="Gao">Daozhou Gao</name><affiliation><nlm:affiliation>Department of Mathematics, Shanghai Normal University, Shanghai 200234, China.</nlm:affiliation></affiliation></author><author><name sortKey="Musa, Salihu S" sort="Musa, Salihu S" uniqKey="Musa S" first="Salihu S" last="Musa">Salihu S. Musa</name><affiliation><nlm:affiliation>Department of Applied Mathematics, Hong Kong Polytechnic University, Hong Kong, China.</nlm:affiliation></affiliation></author><author><name sortKey="Chong, Marc K C" sort="Chong, Marc K C" uniqKey="Chong M" first="Marc K C" last="Chong">Marc K C. Chong</name><affiliation><nlm:affiliation>JC School of Public Health and Primary Care, Chinese University of Hong Kong, Hong Kong, China.</nlm:affiliation></affiliation><affiliation><nlm:affiliation>Shenzhen Research Institute of Chinese University of Hong Kong, Shenzhen 518060, China.</nlm:affiliation></affiliation></author><author><name sortKey="He, Daihai" sort="He, Daihai" uniqKey="He D" first="Daihai" last="He">Daihai He</name><affiliation><nlm:affiliation>Department of Applied Mathematics, Hong Kong Polytechnic University, Hong Kong, China.</nlm:affiliation></affiliation></author><author><name sortKey="Wang, Maggie H" sort="Wang, Maggie H" uniqKey="Wang M" first="Maggie H" last="Wang">Maggie H. Wang</name><affiliation><nlm:affiliation>JC School of Public Health and Primary Care, Chinese University of Hong Kong, Hong Kong, China.</nlm:affiliation></affiliation><affiliation><nlm:affiliation>Shenzhen Research Institute of Chinese University of Hong Kong, Shenzhen 518060, China.</nlm:affiliation></affiliation></author></analytic><series><title level="j">Annals of translational medicine</title><idno type="ISSN">2305-5839</idno><imprint><date when="2020" type="published">2020</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en"><p><b>Background</b></p><p>The coronavirus disease 2019 (COVID-19) was first identified in Wuhan, China on December 2019 in patients presenting with atypical pneumonia. Although 'city-lockdown' policy reduced the spatial spreading of the COVID-19, the city-level outbreaks within each city remain a major concern to be addressed. The local or regional level disease control mainly depends on individuals self-administered infection prevention actions. The contradiction between choice of taking infection prevention actions or not makes the elimination difficult under a voluntary acting scheme, and represents a clash between the optimal choice of action for the individual interest and group interests.</p></div><div type="abstract" xml:lang="en"><p><b>Methods</b></p><p>We develop a compartmental epidemic model based on the classic susceptible-exposed-infectious-recovered model and use this to fit the data. Behavioral imitation through a game theoretical decision-making process is incorporated to study and project the dynamics of the COVID-19 outbreak in Wuhan, China. By varying the key model parameters, we explore the probable course of the outbreak in terms of size and timing under several public interventions in improving public awareness and sensitivity to the infection risk as well as their potential impact.</p></div><div type="abstract" xml:lang="en"><p><b>Results</b></p><p>We estimate the basic reproduction number, </p></div><div type="abstract" xml:lang="en"><p><b>Conclusions</b></p><p>Through an imitating social learning process, individual-level behavioral change on taking infection prevention actions have the potentials to significantly reduce the COVID-19 outbreak in terms of size and timing at city-level. Timely and substantially resources and supports for improving the willingness-to-act and conducts of self-administered infection prevention actions are recommended to reduce to the COVID-19 associated risks.</p></div></front></TEI><pubmed><MedlineCitation Status="PubMed-not-MEDLINE" Owner="NLM"><PMID Version="1">32395492</PMID><DateRevised><Year>2020</Year><Month>09</Month><Day>28</Day></DateRevised><Article PubModel="Print"><Journal><ISSN IssnType="Print">2305-5839</ISSN><JournalIssue CitedMedium="Print"><Volume>8</Volume><Issue>7</Issue><PubDate><Year>2020</Year><Month>Apr</Month></PubDate></JournalIssue><Title>Annals of translational medicine</Title><ISOAbbreviation>Ann Transl Med</ISOAbbreviation></Journal><ArticleTitle>Imitation dynamics in the mitigation of the novel coronavirus disease (COVID-19) outbreak in Wuhan, China from 2019 to 2020.</ArticleTitle><Pagination><MedlinePgn>448</MedlinePgn></Pagination><ELocationID EIdType="doi" ValidYN="Y">10.21037/atm.2020.03.168</ELocationID><Abstract><AbstractText Label="Background" NlmCategory="UNASSIGNED">The coronavirus disease 2019 (COVID-19) was first identified in Wuhan, China on December 2019 in patients presenting with atypical pneumonia. Although 'city-lockdown' policy reduced the spatial spreading of the COVID-19, the city-level outbreaks within each city remain a major concern to be addressed. The local or regional level disease control mainly depends on individuals self-administered infection prevention actions. The contradiction between choice of taking infection prevention actions or not makes the elimination difficult under a voluntary acting scheme, and represents a clash between the optimal choice of action for the individual interest and group interests.</AbstractText><AbstractText Label="Methods" NlmCategory="UNASSIGNED">We develop a compartmental epidemic model based on the classic susceptible-exposed-infectious-recovered model and use this to fit the data. Behavioral imitation through a game theoretical decision-making process is incorporated to study and project the dynamics of the COVID-19 outbreak in Wuhan, China. By varying the key model parameters, we explore the probable course of the outbreak in terms of size and timing under several public interventions in improving public awareness and sensitivity to the infection risk as well as their potential impact.</AbstractText><AbstractText Label="Results" NlmCategory="UNASSIGNED">We estimate the basic reproduction number, <i>R</i><sub>0</sub>, to be 2.5 (95% CI: 2.4-2.7). Under the current most realistic setting, we estimate the peak size at 0.28 (95% CI: 0.24-0.32) infections per 1,000 population. In Wuhan, the final size of the outbreak is likely to infect 1.35% (95% CI: 1.00-2.12%) of the population. The outbreak will be most likely to peak in the first half of February and drop to daily incidences lower than 10 in June 2020. Increasing sensitivity to take infection prevention actions and the effectiveness of infection prevention measures are likely to mitigate the COVID-19 outbreak in Wuhan.</AbstractText><AbstractText Label="Conclusions" NlmCategory="UNASSIGNED">Through an imitating social learning process, individual-level behavioral change on taking infection prevention actions have the potentials to significantly reduce the COVID-19 outbreak in terms of size and timing at city-level. Timely and substantially resources and supports for improving the willingness-to-act and conducts of self-administered infection prevention actions are recommended to reduce to the COVID-19 associated risks.</AbstractText><CopyrightInformation>2020 Annals of Translational Medicine. All rights reserved.</CopyrightInformation></Abstract><AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>Zhao</LastName><ForeName>Shi</ForeName><Initials>S</Initials><AffiliationInfo><Affiliation>JC School of Public Health and Primary Care, Chinese University of Hong Kong, Hong Kong, China.</Affiliation></AffiliationInfo><AffiliationInfo><Affiliation>Shenzhen Research Institute of Chinese University of Hong Kong, Shenzhen 518060, China.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Stone</LastName><ForeName>Lewi</ForeName><Initials>L</Initials><AffiliationInfo><Affiliation>School of Mathematical and Geospatial Sciences, RMIT University, Melbourne, Australia.</Affiliation></AffiliationInfo><AffiliationInfo><Affiliation>Biomathematics Unit, Department of Zoology, Tel Aviv University, Ramat Aviv, Israel.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Gao</LastName><ForeName>Daozhou</ForeName><Initials>D</Initials><AffiliationInfo><Affiliation>Department of Mathematics, Shanghai Normal University, Shanghai 200234, China.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Musa</LastName><ForeName>Salihu S</ForeName><Initials>SS</Initials><AffiliationInfo><Affiliation>Department of Applied Mathematics, Hong Kong Polytechnic University, Hong Kong, China.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Chong</LastName><ForeName>Marc K C</ForeName><Initials>MKC</Initials><AffiliationInfo><Affiliation>JC School of Public Health and Primary Care, Chinese University of Hong Kong, Hong Kong, China.</Affiliation></AffiliationInfo><AffiliationInfo><Affiliation>Shenzhen Research Institute of Chinese University of Hong Kong, Shenzhen 518060, China.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>He</LastName><ForeName>Daihai</ForeName><Initials>D</Initials><AffiliationInfo><Affiliation>Department of Applied Mathematics, Hong Kong Polytechnic University, Hong Kong, China.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Wang</LastName><ForeName>Maggie H</ForeName><Initials>MH</Initials><AffiliationInfo><Affiliation>JC School of Public Health and Primary Care, Chinese University of Hong Kong, Hong Kong, China.</Affiliation></AffiliationInfo><AffiliationInfo><Affiliation>Shenzhen Research Institute of Chinese University of Hong Kong, Shenzhen 518060, China.</Affiliation></AffiliationInfo></Author></AuthorList><Language>eng</Language><PublicationTypeList><PublicationType UI="D016428">Journal Article</PublicationType></PublicationTypeList></Article><MedlineJournalInfo><Country>China</Country><MedlineTA>Ann Transl Med</MedlineTA><NlmUniqueID>101617978</NlmUniqueID><ISSNLinking>2305-5839</ISSNLinking></MedlineJournalInfo><KeywordList Owner="NOTNLM"><Keyword MajorTopicYN="N">Coronavirus disease 2019 (COVID-19)</Keyword><Keyword MajorTopicYN="N">final epidemic size</Keyword><Keyword MajorTopicYN="N">imitation game</Keyword><Keyword MajorTopicYN="N">mathematical modelling</Keyword><Keyword MajorTopicYN="N">reproduction number</Keyword></KeywordList><CoiStatement>Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at http://dx.doi.org/10.21037/atm.2020.03.168). Dr. He reports grants from Alibaba (China), during the conduct of the study. The other authors have no conflicts of interest to declare.</CoiStatement></MedlineCitation><PubmedData><History><PubMedPubDate PubStatus="entrez"><Year>2020</Year><Month>5</Month><Day>13</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="pubmed"><Year>2020</Year><Month>5</Month><Day>13</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="medline"><Year>2020</Year><Month>5</Month><Day>13</Day><Hour>6</Hour><Minute>1</Minute></PubMedPubDate></History><PublicationStatus>ppublish</PublicationStatus><ArticleIdList><ArticleId IdType="pubmed">32395492</ArticleId><ArticleId IdType="doi">10.21037/atm.2020.03.168</ArticleId><ArticleId IdType="pii">atm-08-07-448</ArticleId><ArticleId IdType="pmc">PMC7210122</ArticleId></ArticleIdList><ReferenceList><Reference><Citation>Lancet. 2020 Feb 15;395(10223):514-523</Citation><ArticleIdList><ArticleId IdType="pubmed">31986261</ArticleId></ArticleIdList></Reference><Reference><Citation>N Engl J Med. 2020 Mar 26;382(13):1199-1207</Citation><ArticleIdList><ArticleId IdType="pubmed">31995857</ArticleId></ArticleIdList></Reference><Reference><Citation>Emerg Infect Dis. 2005 Jul;11(7):1142-5</Citation><ArticleIdList><ArticleId IdType="pubmed">16022801</ArticleId></ArticleIdList></Reference><Reference><Citation>Travel Med Infect Dis. 2019 Jan - Feb;27:27-32</Citation><ArticleIdList><ArticleId IdType="pubmed">30550839</ArticleId></ArticleIdList></Reference><Reference><Citation>Sci Rep. 2017 Mar 21;7(1):273</Citation><ArticleIdList><ArticleId IdType="pubmed">28325935</ArticleId></ArticleIdList></Reference><Reference><Citation>Euro Surveill. 2020 Jan;25(3):</Citation><ArticleIdList><ArticleId IdType="pubmed">31992388</ArticleId></ArticleIdList></Reference><Reference><Citation>Euro Surveill. 2020 Jan;25(4):</Citation><ArticleIdList><ArticleId IdType="pubmed">32019669</ArticleId></ArticleIdList></Reference><Reference><Citation>Ann Intern Med. 2012 Feb 7;156(3):173-81</Citation><ArticleIdList><ArticleId IdType="pubmed">22312137</ArticleId></ArticleIdList></Reference><Reference><Citation>Epidemiology. 2009 May;20(3):344-7</Citation><ArticleIdList><ArticleId IdType="pubmed">19279492</ArticleId></ArticleIdList></Reference><Reference><Citation>J Clin Med. 2020 Feb 01;9(2):</Citation><ArticleIdList><ArticleId IdType="pubmed">32024089</ArticleId></ArticleIdList></Reference><Reference><Citation>Proc Biol Sci. 2005 Aug 22;272(1573):1669-75</Citation><ArticleIdList><ArticleId IdType="pubmed">16087421</ArticleId></ArticleIdList></Reference><Reference><Citation>Am J Epidemiol. 1986 Dec;124(6):1012-20</Citation><ArticleIdList><ArticleId IdType="pubmed">3096132</ArticleId></ArticleIdList></Reference><Reference><Citation>Ann Intern Med. 2009 Oct 6;151(7):437-46</Citation><ArticleIdList><ArticleId IdType="pubmed">19652172</ArticleId></ArticleIdList></Reference><Reference><Citation>Stat Methods Med Res. 2018 Jul;27(7):1968-1978</Citation><ArticleIdList><ArticleId IdType="pubmed">29846148</ArticleId></ArticleIdList></Reference><Reference><Citation>Science. 2003 Jun 20;300(5627):1966-70</Citation><ArticleIdList><ArticleId IdType="pubmed">12766207</ArticleId></ArticleIdList></Reference><Reference><Citation>Ann Intern Med. 2020 Apr 21;172(8):567-568</Citation><ArticleIdList><ArticleId IdType="pubmed">32023340</ArticleId></ArticleIdList></Reference><Reference><Citation>Lancet. 2020 Feb 29;395(10225):689-697</Citation><ArticleIdList><ArticleId IdType="pubmed">32014114</ArticleId></ArticleIdList></Reference><Reference><Citation>J Travel Med. 2020 Mar 13;27(2):</Citation><ArticleIdList><ArticleId IdType="pubmed">31943059</ArticleId></ArticleIdList></Reference><Reference><Citation>BMJ. 2020 Jan 20;368:m236</Citation><ArticleIdList><ArticleId IdType="pubmed">31959587</ArticleId></ArticleIdList></Reference><Reference><Citation>Int J Infect Dis. 2020 Mar;92:214-217</Citation><ArticleIdList><ArticleId IdType="pubmed">32007643</ArticleId></ArticleIdList></Reference><Reference><Citation>Travel Med Infect Dis. 2020 Jan - Feb;33:101568</Citation><ArticleIdList><ArticleId IdType="pubmed">32006656</ArticleId></ArticleIdList></Reference><Reference><Citation>J Clin Med. 2020 Feb 17;9(2):</Citation><ArticleIdList><ArticleId IdType="pubmed">32079150</ArticleId></ArticleIdList></Reference><Reference><Citation>Epidemiology. 2005 Nov;16(6):791-801</Citation><ArticleIdList><ArticleId IdType="pubmed">16222170</ArticleId></ArticleIdList></Reference><Reference><Citation>Proc Natl Acad Sci U S A. 2004 Sep 7;101(36):13391-4</Citation><ArticleIdList><ArticleId IdType="pubmed">15329411</ArticleId></ArticleIdList></Reference><Reference><Citation>Math Biosci. 2002 Nov-Dec;180:29-48</Citation><ArticleIdList><ArticleId IdType="pubmed">12387915</ArticleId></ArticleIdList></Reference><Reference><Citation>J Travel Med. 2020 Mar 13;27(2):</Citation><ArticleIdList><ArticleId IdType="pubmed">32080723</ArticleId></ArticleIdList></Reference><Reference><Citation>Lancet. 2020 Feb 15;395(10223):497-506</Citation><ArticleIdList><ArticleId IdType="pubmed">31986264</ArticleId></ArticleIdList></Reference><Reference><Citation>J R Soc Interface. 2010 Feb 6;7(43):271-83</Citation><ArticleIdList><ArticleId IdType="pubmed">19535416</ArticleId></ArticleIdList></Reference><Reference><Citation>J Clin Med. 2020 Feb 04;9(2):</Citation><ArticleIdList><ArticleId IdType="pubmed">32033064</ArticleId></ArticleIdList></Reference><Reference><Citation>Sci Rep. 2017 Mar 14;7:44122</Citation><ArticleIdList><ArticleId IdType="pubmed">28290504</ArticleId></ArticleIdList></Reference><Reference><Citation>J Theor Biol. 2018 Oct 7;454:1-10</Citation><ArticleIdList><ArticleId IdType="pubmed">29792875</ArticleId></ArticleIdList></Reference></ReferenceList></PubmedData></pubmed></record>Pour manipuler ce document sous Unix (Dilib)
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