Substantial undocumented infection facilitates the rapid dissemination of novel coronavirus (SARS-CoV2).
Identifieur interne : 000162 ( PubMed/Checkpoint ); précédent : 000161; suivant : 000163Substantial undocumented infection facilitates the rapid dissemination of novel coronavirus (SARS-CoV2).
Auteurs : Ruiyun Li [Royaume-Uni] ; Sen Pei [États-Unis] ; Bin Chen [États-Unis] ; Yimeng Song [Hong Kong] ; Tao Zhang [République populaire de Chine] ; Wan Yang [États-Unis] ; Jeffrey Shaman [États-Unis]Source :
- Science (New York, N.Y.) [ 1095-9203 ] ; 2020.
Abstract
Estimation of the prevalence and contagiousness of undocumented novel coronavirus (SARS-CoV2) infections is critical for understanding the overall prevalence and pandemic potential of this disease. Here we use observations of reported infection within China, in conjunction with mobility data, a networked dynamic metapopulation model and Bayesian inference, to infer critical epidemiological characteristics associated with SARS-CoV2, including the fraction of undocumented infections and their contagiousness. We estimate 86% of all infections were undocumented (95% CI: [82%-90%]) prior to 23 January 2020 travel restrictions. Per person, the transmission rate of undocumented infections was 55% of documented infections ([46%-62%]), yet, due to their greater numbers, undocumented infections were the infection source for 79% of documented cases. These findings explain the rapid geographic spread of SARS-CoV2 and indicate containment of this virus will be particularly challenging.
DOI: 10.1126/science.abb3221
PubMed: 32179701
Affiliations:
- Hong Kong, Royaume-Uni, République populaire de Chine, États-Unis
- Californie, État de New York
- New York, Pékin
- Université Columbia
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Here we use observations of reported infection within China, in conjunction with mobility data, a networked dynamic metapopulation model and Bayesian inference, to infer critical epidemiological characteristics associated with SARS-CoV2, including the fraction of undocumented infections and their contagiousness. We estimate 86% of all infections were undocumented (95% CI: [82%-90%]) prior to 23 January 2020 travel restrictions. Per person, the transmission rate of undocumented infections was 55% of documented infections ([46%-62%]), yet, due to their greater numbers, undocumented infections were the infection source for 79% of documented cases. These findings explain the rapid geographic spread of SARS-CoV2 and indicate containment of this virus will be particularly challenging.</div></front></TEI><pubmed><MedlineCitation Status="Publisher" Owner="NLM"><PMID Version="1">32179701</PMID><DateRevised><Year>2020</Year><Month>03</Month><Day>27</Day></DateRevised><Article PubModel="Print-Electronic"><Journal><ISSN IssnType="Electronic">1095-9203</ISSN><JournalIssue CitedMedium="Internet"><PubDate><Year>2020</Year><Month>Mar</Month><Day>16</Day></PubDate></JournalIssue><Title>Science (New York, N.Y.)</Title><ISOAbbreviation>Science</ISOAbbreviation></Journal><ArticleTitle>Substantial undocumented infection facilitates the rapid dissemination of novel coronavirus (SARS-CoV2).</ArticleTitle><ELocationID EIdType="pii" ValidYN="Y">eabb3221</ELocationID><ELocationID EIdType="doi" ValidYN="Y">10.1126/science.abb3221</ELocationID><Abstract><AbstractText>Estimation of the prevalence and contagiousness of undocumented novel coronavirus (SARS-CoV2) infections is critical for understanding the overall prevalence and pandemic potential of this disease. Here we use observations of reported infection within China, in conjunction with mobility data, a networked dynamic metapopulation model and Bayesian inference, to infer critical epidemiological characteristics associated with SARS-CoV2, including the fraction of undocumented infections and their contagiousness. We estimate 86% of all infections were undocumented (95% CI: [82%-90%]) prior to 23 January 2020 travel restrictions. Per person, the transmission rate of undocumented infections was 55% of documented infections ([46%-62%]), yet, due to their greater numbers, undocumented infections were the infection source for 79% of documented cases. 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R. China.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Yang</LastName><ForeName>Wan</ForeName><Initials>W</Initials><Identifier Source="ORCID">https://orcid.org/0000-0002-7555-9728</Identifier><AffiliationInfo><Affiliation>Department of Epidemiology, Mailman School of Public Health, Columbia University, New York, NY 10032, USA.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Shaman</LastName><ForeName>Jeffrey</ForeName><Initials>J</Initials><Identifier Source="ORCID">https://orcid.org/0000-0002-7216-7809</Identifier><AffiliationInfo><Affiliation>Department of Environmental Health Sciences, Mailman School of Public Health, Columbia University, New York, NY 10032, USA. sp3449@cumc.columbia.edu jls106@cumc.columbia.edu.</Affiliation></AffiliationInfo></Author></AuthorList><Language>eng</Language><PublicationTypeList><PublicationType UI="D016428">Journal Article</PublicationType></PublicationTypeList><ArticleDate DateType="Electronic"><Year>2020</Year><Month>03</Month><Day>16</Day></ArticleDate></Article><MedlineJournalInfo><Country>United States</Country><MedlineTA>Science</MedlineTA><NlmUniqueID>0404511</NlmUniqueID><ISSNLinking>0036-8075</ISSNLinking></MedlineJournalInfo><CitationSubset>IM</CitationSubset></MedlineCitation><PubmedData><History><PubMedPubDate PubStatus="received"><Year>2020</Year><Month>02</Month><Day>15</Day></PubMedPubDate><PubMedPubDate PubStatus="accepted"><Year>2020</Year><Month>03</Month><Day>12</Day></PubMedPubDate><PubMedPubDate PubStatus="pubmed"><Year>2020</Year><Month>3</Month><Day>18</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="medline"><Year>2020</Year><Month>3</Month><Day>18</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="entrez"><Year>2020</Year><Month>3</Month><Day>18</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate></History><PublicationStatus>aheadofprint</PublicationStatus><ArticleIdList><ArticleId IdType="pubmed">32179701</ArticleId><ArticleId IdType="pii">science.abb3221</ArticleId><ArticleId IdType="doi">10.1126/science.abb3221</ArticleId></ArticleIdList></PubmedData></pubmed><affiliations><list><country><li>Hong Kong</li><li>Royaume-Uni</li><li>République populaire de Chine</li><li>États-Unis</li></country><region><li>Californie</li><li>État de New York</li></region><settlement><li>New York</li><li>Pékin</li></settlement><orgName><li>Université Columbia</li></orgName></list><tree><country name="Royaume-Uni"><noRegion><name sortKey="Li, Ruiyun" sort="Li, Ruiyun" uniqKey="Li R" first="Ruiyun" last="Li">Ruiyun Li</name></noRegion></country><country name="États-Unis"><region name="État de New York"><name sortKey="Pei, Sen" sort="Pei, Sen" uniqKey="Pei S" first="Sen" last="Pei">Sen Pei</name></region><name sortKey="Chen, Bin" sort="Chen, Bin" uniqKey="Chen B" first="Bin" last="Chen">Bin Chen</name><name sortKey="Shaman, Jeffrey" sort="Shaman, Jeffrey" uniqKey="Shaman J" first="Jeffrey" last="Shaman">Jeffrey Shaman</name><name sortKey="Yang, Wan" sort="Yang, Wan" uniqKey="Yang W" first="Wan" last="Yang">Wan Yang</name></country><country name="Hong Kong"><noRegion><name sortKey="Song, Yimeng" sort="Song, Yimeng" uniqKey="Song Y" first="Yimeng" last="Song">Yimeng Song</name></noRegion></country><country name="République populaire de Chine"><noRegion><name sortKey="Zhang, Tao" sort="Zhang, Tao" uniqKey="Zhang T" first="Tao" last="Zhang">Tao Zhang</name></noRegion></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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