Bayesian modeling to unmask and predict influenza A/H1N1pdm dynamics in London

Proceedings of the National Academy of Sciences of the United States of America

Volumn 108, Issue 45, 2011, Pages 18238-18243

  Birrell, Paul J ^a   Ketsetzis, Georgios ^b   Gay, Nigel J ^c   Cooper, Ben S ^d   Presanis, Anne M ^a   Harris, Ross J ^b   Charlett, André ^b   Zhang, Xu Sheng ^b   White, Peter J ^b,e   Pebody, Richard G ^b   De Angelis, Daniela ^a,b  

^a MRC BIOSTATISTICS UNIT   (United Kingdom)

^b PUBLIC HEALTH ENGLAND   (United Kingdom)

^c Fu Consulting   (United Kingdom)

^d MAHIDOL UNIVERSITY   (Thailand)

^e IMPERIAL COLLEGE LONDON   (United Kingdom)

Author keywords

Bayesian statistics; General practice consultation data; Infectious disease; Real time modeling; Seroepidemiology

Indexed keywords

ADOLESCENT; ADULT; AGED; ARTICLE; BAYES THEOREM; CHILD; DYNAMICS; EPIDEMIC; HEALTH BEHAVIOR; HUMAN; INFLUENZA A (H1N1); MODEL; PANDEMIC; PRESCHOOL CHILD; PRIORITY JOURNAL; SCHOOL CHILD; SUMMER; UNITED KINGDOM;

BAYES THEOREM; HUMANS; INFLUENZA A VIRUS, H1N1 SUBTYPE; INFLUENZA, HUMAN; LONDON;

EID: 81055130178     PISSN: 00278424     EISSN: 10916490     Source Type: Journal    
DOI: 10.1073/pnas.1103002108     Document Type: Article

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