Substantial undocumented infection facilitates the rapid dissemination of novel coronavirus (SARS-CoV2)
Identifieur interne : 000060 ( Pmc/Checkpoint ); précédent : 000059; suivant : 000061Substantial 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.) [ 0036-8075 ] ; 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.
Url:
DOI: 10.1126/science.abb3221
PubMed: 32179701
PubMed Central: 7164387
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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<front><div type="abstract" xml:lang="en"><p>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.</p>
</div>
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<biblStruct><analytic><author><name sortKey="Diekmann, O" uniqKey="Diekmann O">O. Diekmann</name>
</author>
<author><name sortKey="Heesterbeek, J A X0a P" uniqKey="Heesterbeek J">J. A.
P. Heesterbeek</name>
</author>
<author><name sortKey="Metz, J X0a A" uniqKey="Metz J">J.
A. Metz</name>
</author>
</analytic>
</biblStruct>
<biblStruct><analytic><author><name sortKey="Diekmann, O" uniqKey="Diekmann O">O. Diekmann</name>
</author>
<author><name sortKey="Heesterbeek, J A X0a P" uniqKey="Heesterbeek J">J. A.
P. Heesterbeek</name>
</author>
<author><name sortKey="Roberts, M X0a G" uniqKey="Roberts M">M.
G. Roberts</name>
</author>
</analytic>
</biblStruct>
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</TEI>
<pmc article-type="research-article"><pmc-dir>properties open_access</pmc-dir>
<front><journal-meta><journal-id journal-id-type="nlm-ta">Science</journal-id>
<journal-id journal-id-type="iso-abbrev">Science</journal-id>
<journal-id journal-id-type="publisher-id">SCIENCE</journal-id>
<journal-title-group><journal-title>Science (New York, N.y.)</journal-title>
</journal-title-group>
<issn pub-type="ppub">0036-8075</issn>
<issn pub-type="epub">1095-9203</issn>
<publisher><publisher-name>American Association for the Advancement of Science</publisher-name>
</publisher>
</journal-meta>
<article-meta><article-id pub-id-type="pmid">32179701</article-id>
<article-id pub-id-type="pmc">7164387</article-id>
<article-id pub-id-type="publisher-id">abb3221</article-id>
<article-id pub-id-type="doi">10.1126/science.abb3221</article-id>
<article-categories><subj-group subj-group-type="article-type"><subject>Research Article</subject>
</subj-group>
<subj-group subj-group-type="heading"><subject>Research Articles</subject>
</subj-group>
<subj-group subj-group-type="legacy-article-type"><subject>R-Articles</subject>
</subj-group>
<subj-group subj-group-type="field"><subject>Epidemiology</subject>
</subj-group>
</article-categories>
<title-group><article-title>Substantial undocumented infection facilitates the rapid dissemination of
novel coronavirus (SARS-CoV2)</article-title>
</title-group>
<contrib-group><contrib contrib-type="author"><contrib-id contrib-id-type="orcid" authenticated="true">https://orcid.org/0000-0001-8927-9965</contrib-id>
<name><surname>Li</surname>
<given-names>Ruiyun</given-names>
</name>
<xref ref-type="aff" rid="aff1"><sup>1</sup>
</xref>
<xref ref-type="author-notes" rid="afn1">*</xref>
</contrib>
<contrib contrib-type="author"><contrib-id contrib-id-type="orcid" authenticated="true">https://orcid.org/0000-0002-7072-2995</contrib-id>
<name><surname>Pei</surname>
<given-names>Sen</given-names>
</name>
<xref ref-type="aff" rid="aff2"><sup>2</sup>
</xref>
<xref ref-type="author-notes" rid="afn1">*</xref>
<xref ref-type="corresp" rid="cor1">†</xref>
</contrib>
<contrib contrib-type="author"><contrib-id contrib-id-type="orcid" authenticated="true">https://orcid.org/0000-0003-3496-2876</contrib-id>
<name><surname>Chen</surname>
<given-names>Bin</given-names>
</name>
<xref ref-type="aff" rid="aff3"><sup>3</sup>
</xref>
<xref ref-type="author-notes" rid="afn1">*</xref>
</contrib>
<contrib contrib-type="author"><contrib-id contrib-id-type="orcid" authenticated="true">https://orcid.org/0000-0001-9558-1220</contrib-id>
<name><surname>Song</surname>
<given-names>Yimeng</given-names>
</name>
<xref ref-type="aff" rid="aff4"><sup>4</sup>
</xref>
</contrib>
<contrib contrib-type="author"><name><surname>Zhang</surname>
<given-names>Tao</given-names>
</name>
<xref ref-type="aff" rid="aff5"><sup>5</sup>
</xref>
</contrib>
<contrib contrib-type="author"><contrib-id contrib-id-type="orcid" authenticated="true">https://orcid.org/0000-0002-7555-9728</contrib-id>
<name><surname>Yang</surname>
<given-names>Wan</given-names>
</name>
<xref ref-type="award" rid="award587412"></xref>
<xref ref-type="aff" rid="aff6"><sup>6</sup>
</xref>
</contrib>
<contrib contrib-type="author"><contrib-id contrib-id-type="orcid" authenticated="true">https://orcid.org/0000-0002-7216-7809</contrib-id>
<name><surname>Shaman</surname>
<given-names>Jeffrey</given-names>
</name>
<xref ref-type="award" rid="award587411"></xref>
<xref ref-type="aff" rid="aff2"><sup>2</sup>
</xref>
<xref ref-type="corresp" rid="cor1">†</xref>
</contrib>
<aff id="aff1"><label>1</label>
MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, School of Public Health, Faculty of Medicine, Imperial College London, London W2 1PG, UK.</aff>
<aff id="aff2"><label>2</label>
Department of Environmental Health Sciences, Mailman School of Public Health, Columbia University, New York, NY 10032, USA.</aff>
<aff id="aff3"><label>3</label>
Department of Land, Air and Water Resources, University of California, Davis, Davis, CA 95616, USA.</aff>
<aff id="aff4"><label>4</label>
Department of Urban Planning and Design, The University of Hong Kong, Hong Kong.</aff>
<aff id="aff5"><label>5</label>
Ministry of Education Key Laboratory for Earth System Modeling, Department of Earth System Science, Tsinghua University, Beijing 10084, P. R. China.</aff>
<aff id="aff6"><label>6</label>
Department of Epidemiology, Mailman School of Public Health, Columbia University, New York, NY 10032, USA.</aff>
</contrib-group>
<author-notes><fn id="afn1" fn-type="equal"><label>*</label>
<p>These authors contributed equally to this work.</p>
</fn>
<corresp id="cor1"><label>†</label>
Corresponding author. Email: <email xlink:href="sp3449@cumc.columbia.edu">sp3449@cumc.columbia.edu</email>
(S.P.); <email xlink:href="jls106@cumc.columbia.edu">jls106@cumc.columbia.edu</email>
(J.S.)</corresp>
</author-notes>
<pub-date pub-type="epub"><day>16</day>
<month>3</month>
<year>2020</year>
</pub-date>
<elocation-id>eabb3221</elocation-id>
<history><date date-type="received"><day>15</day>
<month>2</month>
<year>2020</year>
</date>
<date date-type="accepted"><day>12</day>
<month>3</month>
<year>2020</year>
</date>
</history>
<permissions><copyright-statement>Copyright © 2020, American Association for the Advancement of
Science</copyright-statement>
<copyright-year>2020</copyright-year>
<copyright-holder>American Association for the Advancement of Science</copyright-holder>
<license license-type="open-access" xlink:href="http://creativecommons.org/licenses/by/4.0/"><ali:license_ref specific-use="vor" start_date="2020-03-04">https://creativecommons.org/licenses/by/4.0/</ali:license_ref>
<license-p>This open access article is distributed under <ext-link ext-link-type="uri" xlink:href="http://creativecommons.org/licenses/by/4.0/">Creative Commons Attribution License 4.0 (CC BY)</ext-link>
.</license-p>
</license>
</permissions>
<abstract><p>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.</p>
</abstract>
<funding-group><award-group id="award587411"><funding-source><institution-wrap><institution-id institution-id-type="doi">http://dx.doi.org/10.13039/100000057</institution-id>
<institution>National Institute of General Medical Sciences</institution>
</institution-wrap>
</funding-source>
<award-id>GM110748</award-id>
</award-group>
<award-group id="award587412"><funding-source><institution-wrap><institution-id institution-id-type="doi">http://dx.doi.org/10.13039/100000060</institution-id>
<institution>National Institute of Allergy and Infectious Diseases</institution>
</institution-wrap>
</funding-source>
<award-id>AI145883</award-id>
</award-group>
</funding-group>
<custom-meta-group><custom-meta><meta-name>Number-color-figs</meta-name>
<meta-value>2</meta-value>
</custom-meta>
<custom-meta><meta-name>Number-figs</meta-name>
<meta-value>2</meta-value>
</custom-meta>
</custom-meta-group>
</article-meta>
</front>
</pmc>
<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>
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