Network Analysis of Global Influenza Spread
Identifieur interne : 000A43 ( Pmc/Curation ); précédent : 000A42; suivant : 000A44Network Analysis of Global Influenza Spread
Auteurs : Joseph Chan ; Antony Holmes ; Raul RabadanSource :
- PLoS Computational Biology [ 1553-734X ] ; 2010.
Abstract
Although vaccines pose the best means of preventing influenza infection, strain selection and optimal implementation remain difficult due to antigenic drift and a lack of understanding global spread. Detecting viral movement by sequence analysis is complicated by skewed geographic and seasonal distributions in viral isolates. We propose a probabilistic method that accounts for sampling bias through spatiotemporal clustering and modeling regional and seasonal transmission as a binomial process. Analysis of H3N2 not only confirmed East-Southeast Asia as a source of new seasonal variants, but also increased the resolution of observed transmission to a country level. H1N1 data revealed similar viral spread from the tropics. Network analysis suggested China and Hong Kong as the origins of new seasonal H3N2 strains and the United States as a region where increased vaccination would maximally disrupt global spread of the virus. These techniques provide a promising methodology for the analysis of any seasonal virus, as well as for the continued surveillance of influenza.
Url:
DOI: 10.1371/journal.pcbi.1001005
PubMed: 21124942
PubMed Central: 2987833
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<record><TEI><teiHeader><fileDesc><titleStmt><title xml:lang="en">Network Analysis of Global Influenza Spread</title><author><name sortKey="Chan, Joseph" sort="Chan, Joseph" uniqKey="Chan J" first="Joseph" last="Chan">Joseph Chan</name><affiliation><nlm:aff id="aff1"></nlm:aff></affiliation></author><author><name sortKey="Holmes, Antony" sort="Holmes, Antony" uniqKey="Holmes A" first="Antony" last="Holmes">Antony Holmes</name><affiliation><nlm:aff id="aff1"></nlm:aff></affiliation></author><author><name sortKey="Rabadan, Raul" sort="Rabadan, Raul" uniqKey="Rabadan R" first="Raul" last="Rabadan">Raul Rabadan</name><affiliation><nlm:aff id="aff1"></nlm:aff></affiliation></author></titleStmt><publicationStmt><idno type="wicri:source">PMC</idno><idno type="pmid">21124942</idno><idno type="pmc">2987833</idno><idno type="url">http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2987833</idno><idno type="RBID">PMC:2987833</idno><idno type="doi">10.1371/journal.pcbi.1001005</idno><date when="2010">2010</date><idno type="wicri:Area/Pmc/Corpus">000A43</idno><idno type="wicri:explorRef" wicri:stream="Pmc" wicri:step="Corpus" wicri:corpus="PMC">000A43</idno><idno type="wicri:Area/Pmc/Curation">000A43</idno><idno type="wicri:explorRef" wicri:stream="Pmc" wicri:step="Curation">000A43</idno></publicationStmt><sourceDesc><biblStruct><analytic><title xml:lang="en" level="a" type="main">Network Analysis of Global Influenza Spread</title><author><name sortKey="Chan, Joseph" sort="Chan, Joseph" uniqKey="Chan J" first="Joseph" last="Chan">Joseph Chan</name><affiliation><nlm:aff id="aff1"></nlm:aff></affiliation></author><author><name sortKey="Holmes, Antony" sort="Holmes, Antony" uniqKey="Holmes A" first="Antony" last="Holmes">Antony Holmes</name><affiliation><nlm:aff id="aff1"></nlm:aff></affiliation></author><author><name sortKey="Rabadan, Raul" sort="Rabadan, Raul" uniqKey="Rabadan R" first="Raul" last="Rabadan">Raul Rabadan</name><affiliation><nlm:aff id="aff1"></nlm:aff></affiliation></author></analytic><series><title level="j">PLoS Computational Biology</title><idno type="ISSN">1553-734X</idno><idno type="eISSN">1553-7358</idno><imprint><date when="2010">2010</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en"><p>Although vaccines pose the best means of preventing influenza infection, strain selection and optimal implementation remain difficult due to antigenic drift and a lack of understanding global spread. Detecting viral movement by sequence analysis is complicated by skewed geographic and seasonal distributions in viral isolates. We propose a probabilistic method that accounts for sampling bias through spatiotemporal clustering and modeling regional and seasonal transmission as a binomial process. Analysis of H3N2 not only confirmed East-Southeast Asia as a source of new seasonal variants, but also increased the resolution of observed transmission to a country level. H1N1 data revealed similar viral spread from the tropics. Network analysis suggested China and Hong Kong as the origins of new seasonal H3N2 strains and the United States as a region where increased vaccination would maximally disrupt global spread of the virus. 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<front><journal-meta><journal-id journal-id-type="nlm-ta">PLoS Comput Biol</journal-id><journal-id journal-id-type="iso-abbrev">PLoS Comput. Biol</journal-id><journal-id journal-id-type="publisher-id">plos</journal-id><journal-id journal-id-type="pmc">ploscomp</journal-id><journal-title-group><journal-title>PLoS Computational Biology</journal-title></journal-title-group><issn pub-type="ppub">1553-734X</issn><issn pub-type="epub">1553-7358</issn><publisher><publisher-name>Public Library of Science</publisher-name><publisher-loc>San Francisco, USA</publisher-loc></publisher></journal-meta><article-meta><article-id pub-id-type="pmid">21124942</article-id><article-id pub-id-type="pmc">2987833</article-id><article-id pub-id-type="publisher-id">10-PLCB-RA-2430R2</article-id><article-id pub-id-type="doi">10.1371/journal.pcbi.1001005</article-id><article-categories><subj-group subj-group-type="heading"><subject>Research Article</subject></subj-group><subj-group subj-group-type="Discipline"><subject>Computational Biology/Genomics</subject><subject>Microbiology/Microbial Evolution and Genomics</subject></subj-group></article-categories><title-group><article-title>Network Analysis of Global Influenza Spread</article-title><alt-title alt-title-type="running-head">Network Analysis of Global Influenza Spread</alt-title></title-group><contrib-group><contrib contrib-type="author"><name><surname>Chan</surname><given-names>Joseph</given-names></name><xref ref-type="aff" rid="aff1"></xref><xref ref-type="corresp" rid="cor1"><sup>*</sup></xref></contrib><contrib contrib-type="author"><name><surname>Holmes</surname><given-names>Antony</given-names></name><xref ref-type="aff" rid="aff1"></xref></contrib><contrib contrib-type="author"><name><surname>Rabadan</surname><given-names>Raul</given-names></name><xref ref-type="aff" rid="aff1"></xref></contrib></contrib-group><aff id="aff1"><addr-line>Department of Biomedical Informatics and Center for Computational Biology and Bioinformatics, Columbia University College of Physicians and Surgeons, New York, New York, United States of America</addr-line></aff><contrib-group><contrib contrib-type="editor"><name><surname>Kosakovsky Pond</surname><given-names>Sergei L.</given-names></name><role>Editor</role><xref ref-type="aff" rid="edit1"></xref></contrib></contrib-group><aff id="edit1">University of California San Diego, United States of America</aff><author-notes><corresp id="cor1">* E-mail: <email>jmc2213@columbia.edu</email></corresp><fn fn-type="con"><p>Conceived and designed the experiments: JC AH RR. Performed the experiments: JC AH. Analyzed the data: JC AH. Contributed reagents/materials/analysis tools: JC AH. Wrote the paper: JC AH.</p></fn></author-notes><pub-date pub-type="collection"><month>11</month><year>2010</year></pub-date><pub-date pub-type="epub"><day>18</day><month>11</month><year>2010</year></pub-date><volume>6</volume><issue>11</issue><elocation-id>e1001005</elocation-id><history><date date-type="received"><day>22</day><month>6</month><year>2010</year></date><date date-type="accepted"><day>21</day><month>10</month><year>2010</year></date></history><permissions><copyright-statement>Chan et al.</copyright-statement><copyright-year>2010</copyright-year><license xlink:href="http://creativecommons.org/licenses/by/4.0/"><license-p>This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.</license-p></license></permissions><abstract><p>Although vaccines pose the best means of preventing influenza infection, strain selection and optimal implementation remain difficult due to antigenic drift and a lack of understanding global spread. Detecting viral movement by sequence analysis is complicated by skewed geographic and seasonal distributions in viral isolates. We propose a probabilistic method that accounts for sampling bias through spatiotemporal clustering and modeling regional and seasonal transmission as a binomial process. Analysis of H3N2 not only confirmed East-Southeast Asia as a source of new seasonal variants, but also increased the resolution of observed transmission to a country level. H1N1 data revealed similar viral spread from the tropics. Network analysis suggested China and Hong Kong as the origins of new seasonal H3N2 strains and the United States as a region where increased vaccination would maximally disrupt global spread of the virus. These techniques provide a promising methodology for the analysis of any seasonal virus, as well as for the continued surveillance of influenza.</p></abstract><abstract abstract-type="summary"><title>Author Summary</title><p>As evidenced by several historic vaccine failures, the design and implementation of the influenza vaccine remains an imperfect science. The virus's rapid rate of evolution makes the selection of representative strains for vaccine composition a difficult process. From a global health viewpoint, how to optimally implement a limited stockpile of vaccines is another fundamental question that remains unanswered. An understanding of how influenza spreads around the world would greatly aid the design and implementation process, but regional and seasonal bias in collected virus samples hampers epidemiologic analysis. Here, we show that it is possible to counter this data bias through probabilistic modeling and represent the global viral spread as a network of seeding events between different regions of the world. On a local scale, our technique can output the most likely origins of a virus circulating in a given location. On a global scale, we can pinpoint regions of the world that would maximally disrupt viral transmission with an increase in vaccine implementation. We demonstrate our method on seasonal H3N2 and H1N1 and foresee similar application to other seasonal viruses, including swine-origin H1N1, once more seasonal data is collected.</p></abstract><counts><page-count count="10"></page-count></counts></article-meta></front></pmc></record>Pour manipuler ce document sous Unix (Dilib)
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