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Toward unbiased assessment of treatment and prevention: modeling household transmission of pandemic influenza

Gerardo Chowell12* and Hiroshi Nishiura34

Author Affiliations

1 School of Human Evolution and Social Change, Arizona State University, Tempe, AZ, USA

2 Division of Epidemiology and Population Studies, Fogarty International Center, National Institutes of Health, Bethesda, MD, USA

3 School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China

4 PRESTO, Japan Science and Technology Agency, Saitama, Japan

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BMC Medicine 2012, 10:118  doi:10.1186/1741-7015-10-118

Published: 9 October 2012


Providing valid and reliable estimates of the transmissibility and severity of pandemic influenza in real time is key to guide public health policymaking. In particular, early estimates of the transmissibility are indispensable for determining the type and intensity of interventions. A recent study by House and colleagues in BMC Medicine devised a stochastic transmission model to estimate the unbiased risk of transmission within households, applying the method to datasets of the 2009 A/H1N1 influenza pandemic. Here, we discuss future challenges in household transmission studies and underscore the need to systematically collect epidemiological data to decipher the household transmission dynamics. We emphasize the need to consider three critical issues for future improvements: (i) capturing age-dependent heterogeneity within households calls for intensive modeling efforts, (ii) the timeline of observation during the course of an epidemic and the length of follow-up should be aligned with study objectives, and (iii) the use of laboratory methods, especially molecular techniques, is encouraged to distinguish household transmissions from those arising in the community.

See related article: webcite

epidemic; estimation; household transmissibility; household transmission studies; mathematical model; outbreaks; pandemic; reproduction number; secondary attack rate; serial interval