Network Analysis of Global Influenza Spread
Identifieur interne : 000E55 ( Main/Merge ); précédent : 000E54; suivant : 000E56Network 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><idno type="wicri:Area/Pmc/Checkpoint">000919</idno><idno type="wicri:explorRef" wicri:stream="Pmc" wicri:step="Checkpoint">000919</idno><idno type="wicri:Area/Ncbi/Merge">000592</idno><idno type="wicri:Area/Ncbi/Curation">000592</idno><idno type="wicri:Area/Ncbi/Checkpoint">000592</idno><idno type="wicri:doubleKey">1553-734X:2010:Chan J:network:analysis:of</idno><idno type="wicri:Area/Main/Merge">000E55</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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