Characterizing the dynamics underlying global spread of epidemics
Identifieur interne : 000269 ( Pmc/Curation ); précédent : 000268; suivant : 000270Characterizing the dynamics underlying global spread of epidemics
Auteurs : Lin Wang ; Joseph T. WuSource :
- Nature Communications [ 2041-1723 ] ; 2018.
Abstract
Over the past few decades, global metapopulation epidemic simulations built with worldwide air-transportation data have been the main tool for studying how epidemics spread from the origin to other parts of the world (e.g., for pandemic influenza, SARS, and Ebola). However, it remains unclear how disease epidemiology and the air-transportation network structure determine epidemic arrivals for different populations around the globe. Here, we fill this knowledge gap by developing and validating an analytical framework that requires only basic analytics from stochastic processes. We apply this framework retrospectively to the 2009 influenza pandemic and 2014 Ebola epidemic to show that key epidemic parameters could be robustly estimated in real-time from public data on local and global spread at very low computational cost. Our framework not only elucidates the dynamics underlying global spread of epidemics but also advances our capability in nowcasting and forecasting epidemics.
Url:
DOI: 10.1038/s41467-017-02344-z
PubMed: 29335536
PubMed Central: 5768765
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<record><TEI><teiHeader><fileDesc><titleStmt><title xml:lang="en">Characterizing the dynamics underlying global spread of epidemics</title><author><name sortKey="Wang, Lin" sort="Wang, Lin" uniqKey="Wang L" first="Lin" last="Wang">Lin Wang</name><affiliation><nlm:aff id="Aff1"></nlm:aff></affiliation></author><author><name sortKey="Wu, Joseph T" sort="Wu, Joseph T" uniqKey="Wu J" first="Joseph T." last="Wu">Joseph T. Wu</name><affiliation><nlm:aff id="Aff1"></nlm:aff></affiliation></author></titleStmt><publicationStmt><idno type="wicri:source">PMC</idno><idno type="pmid">29335536</idno><idno type="pmc">5768765</idno><idno type="url">http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5768765</idno><idno type="RBID">PMC:5768765</idno><idno type="doi">10.1038/s41467-017-02344-z</idno><date when="2018">2018</date><idno type="wicri:Area/Pmc/Corpus">000269</idno><idno type="wicri:explorRef" wicri:stream="Pmc" wicri:step="Corpus" wicri:corpus="PMC">000269</idno><idno type="wicri:Area/Pmc/Curation">000269</idno><idno type="wicri:explorRef" wicri:stream="Pmc" wicri:step="Curation">000269</idno></publicationStmt><sourceDesc><biblStruct><analytic><title xml:lang="en" level="a" type="main">Characterizing the dynamics underlying global spread of epidemics</title><author><name sortKey="Wang, Lin" sort="Wang, Lin" uniqKey="Wang L" first="Lin" last="Wang">Lin Wang</name><affiliation><nlm:aff id="Aff1"></nlm:aff></affiliation></author><author><name sortKey="Wu, Joseph T" sort="Wu, Joseph T" uniqKey="Wu J" first="Joseph T." last="Wu">Joseph T. Wu</name><affiliation><nlm:aff id="Aff1"></nlm:aff></affiliation></author></analytic><series><title level="j">Nature Communications</title><idno type="eISSN">2041-1723</idno><imprint><date when="2018">2018</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en"><p id="Par1">Over the past few decades, global metapopulation epidemic simulations built with worldwide air-transportation data have been the main tool for studying how epidemics spread from the origin to other parts of the world (e.g., for pandemic influenza, SARS, and Ebola). However, it remains unclear how disease epidemiology and the air-transportation network structure determine epidemic arrivals for different populations around the globe. Here, we fill this knowledge gap by developing and validating an analytical framework that requires only basic analytics from stochastic processes. We apply this framework retrospectively to the 2009 influenza pandemic and 2014 Ebola epidemic to show that key epidemic parameters could be robustly estimated in real-time from public data on local and global spread at very low computational cost. 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<front><journal-meta><journal-id journal-id-type="nlm-ta">Nat Commun</journal-id><journal-id journal-id-type="iso-abbrev">Nat Commun</journal-id><journal-title-group><journal-title>Nature Communications</journal-title></journal-title-group><issn pub-type="epub">2041-1723</issn><publisher><publisher-name>Nature Publishing Group UK</publisher-name><publisher-loc>London</publisher-loc></publisher></journal-meta><article-meta><article-id pub-id-type="pmid">29335536</article-id><article-id pub-id-type="pmc">5768765</article-id><article-id pub-id-type="publisher-id">2344</article-id><article-id pub-id-type="doi">10.1038/s41467-017-02344-z</article-id><article-categories><subj-group subj-group-type="heading"><subject>Article</subject></subj-group></article-categories><title-group><article-title>Characterizing the dynamics underlying global spread of epidemics</article-title></title-group><contrib-group><contrib contrib-type="author" equal-contrib="yes"><name><surname>Wang</surname><given-names>Lin</given-names></name><xref ref-type="aff" rid="Aff1"></xref></contrib><contrib contrib-type="author" corresp="yes" equal-contrib="yes"><contrib-id contrib-id-type="orcid">http://orcid.org/0000-0002-3155-5987</contrib-id><name><surname>Wu</surname><given-names>Joseph T.</given-names></name><address><email>joewu@hku.hk</email></address><xref ref-type="aff" rid="Aff1"></xref></contrib><aff id="Aff1"><institution-wrap><institution-id institution-id-type="GRID">grid.194645.b</institution-id><institution-id institution-id-type="ISNI">0000000121742757</institution-id><institution>WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine,</institution><institution>The University of Hong Kong,</institution></institution-wrap>7 Sassoon Road, Hong Kong Special Administrative Region, 999077 Pokfulam, China</aff></contrib-group><pub-date pub-type="epub"><day>15</day><month>1</month><year>2018</year></pub-date><pub-date pub-type="pmc-release"><day>15</day><month>1</month><year>2018</year></pub-date><pub-date pub-type="collection"><year>2018</year></pub-date><volume>9</volume><elocation-id>218</elocation-id><history><date date-type="received"><day>13</day><month>4</month><year>2017</year></date><date date-type="accepted"><day>20</day><month>11</month><year>2017</year></date></history><permissions><copyright-statement>© The Author(s) 2018</copyright-statement><license license-type="OpenAccess"><license-p><bold>Open Access</bold> This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit <ext-link ext-link-type="uri" xlink:href="http://creativecommons.org/licenses/by/4.0/">http://creativecommons.org/licenses/by/4.0/</ext-link>.</license-p></license></permissions><abstract id="Abs1"><p id="Par1">Over the past few decades, global metapopulation epidemic simulations built with worldwide air-transportation data have been the main tool for studying how epidemics spread from the origin to other parts of the world (e.g., for pandemic influenza, SARS, and Ebola). However, it remains unclear how disease epidemiology and the air-transportation network structure determine epidemic arrivals for different populations around the globe. Here, we fill this knowledge gap by developing and validating an analytical framework that requires only basic analytics from stochastic processes. We apply this framework retrospectively to the 2009 influenza pandemic and 2014 Ebola epidemic to show that key epidemic parameters could be robustly estimated in real-time from public data on local and global spread at very low computational cost. Our framework not only elucidates the dynamics underlying global spread of epidemics but also advances our capability in nowcasting and forecasting epidemics.</p></abstract><abstract id="Abs2" abstract-type="web-summary"><p id="Par2">Understanding global epidemics spread is crucial for preparedness and response. Here the authors introduce an analytical framework to study epidemic spread on air transport networks, and demonstrate its power to estimate key epidemic parameters by application to the recent influenza pandemic and Ebola outbreak.</p></abstract><kwd-group kwd-group-type="npg-subject"><title>Subject terms</title><kwd>Infectious diseases</kwd><kwd>Epidemiology</kwd><kwd>Complex networks</kwd></kwd-group><custom-meta-group><custom-meta><meta-name>issue-copyright-statement</meta-name><meta-value>© The Author(s) 2018</meta-value></custom-meta></custom-meta-group></article-meta></front></pmc></record>Pour manipuler ce document sous Unix (Dilib)
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