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dc.contributor.authorPaul J. Birrellen_US
dc.contributor.authorGeorgios Ketsetzisen_US
dc.contributor.authorNigel J. Gayen_US
dc.contributor.authorBen S. Cooperen_US
dc.contributor.authorAnne M. Presanisen_US
dc.contributor.authorRoss J. Harrisen_US
dc.contributor.authorAndré Charletten_US
dc.contributor.authorXu Sheng Zhangen_US
dc.contributor.authorPeter J. Whiteen_US
dc.contributor.authorRichard G. Pebodyen_US
dc.contributor.authorDaniela De Angelisen_US
dc.contributor.otherUniversity of Cambridgeen_US
dc.contributor.otherHealth Protection Agencyen_US
dc.contributor.otherFu Consultingen_US
dc.contributor.otherMahidol Universityen_US
dc.contributor.otherImperial College Faculty of Medicineen_US
dc.date.accessioned2018-05-03T08:46:51Z-
dc.date.available2018-05-03T08:46:51Z-
dc.date.issued2011-11-08en_US
dc.identifier.citationProceedings of the National Academy of Sciences of the United States of America. Vol.108, No.45 (2011), 18238-18243en_US
dc.identifier.issn10916490en_US
dc.identifier.issn00278424en_US
dc.identifier.other2-s2.0-81055130178en_US
dc.identifier.urihttps://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=81055130178&origin=inwarden_US
dc.identifier.urihttp://repository.li.mahidol.ac.th/dspace/handle/123456789/12902-
dc.description.abstractThe tracking and projection of emerging epidemics is hindered by the disconnect between apparent epidemic dynamics, discernible from noisy and incomplete surveillance data, and the underlying, imperfectly observed, system. Behavior changes compound this, altering both true dynamics and reporting patterns, particularly for diseases with nonspecific symptoms, such as influenza. We disentangle these effects to unravel the hidden dynamics of the 2009 influenza A/H1N1pdm pandemic in London, where surveillance suggests an unusual dominant peak in the summer. We embed an age-structured model into a Bayesian synthesis of multiple evidence sources to reveal substantial changes in contact patterns and health-seeking behavior throughout the epidemic, uncovering two similar infection waves, despite large differences in the reported levels of disease. We show how this approach, which allows for real-time learning about model parameters as the epidemic progresses, is also able to provide a sequence of nested projections that are capable of accurately reflecting the epidemic evolution.en_US
dc.rightsMahidol Universityen_US
dc.source.urihttps://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=81055130178&origin=inwarden_US
dc.subjectMultidisciplinaryen_US
dc.titleBayesian modeling to unmask and predict influenza A/H1N1pdm dynamics in Londonen_US
dc.typeArticleen_US
dc.rights.holderSCOPUSen_US
dc.identifier.doi10.1073/pnas.1103002108en_US
Appears in Collections:Scopus 2011-2015

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