Using weekly influenza surveillance data from the US CDC, Edward Goldstein and colleagues develop a statistical method to predict the sizes of epidemics caused by seasonal influenza strains. This method could inform decisions about the most appropriate vaccines or drugs needed early in the influenza season. The analyses, based on data collected by the CDC from 1997 through to 2009, suggests that early circulation of one influenza strain is associated with a reduced total incidence of other strains.
It is likely that routine early-season surveillance data could be used to predict the relative size of the epidemics caused by each influenza strain in the USA and in other countries where sufficient surveillance data are available.
Funding: This work was supported in part by the US National Institutes of Health Models of Infectious Disease Agent Study program through cooperative agreement 1 U54 GM088558 (ML, EG, SC); by the RAPIDD program of the Science & Technology Directorate, Department of Homeland Security, and the Fogarty International Center, US National Institutes of Health (JCM); and by the Harvard College Research Program (ST). The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institute of General Medical Sciences or the National Institutes of Health. No funding bodies had any role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing Interests: Marc Lipsitch is on the PLoS Medicine Editorial Board. ML discloses consulting income from the Avian/Pandemic Flu Registry (Outcome Sciences, funded in part by Roche) and from Pfizer/Wyeth and from Novartis Vaccines and Diagnostics. The other authors declare no competing interests.
Citation: Goldstein E, Cobey S, Takahashi S, Miller JC, Lipsitch M (2011) Predicting the Epidemic Sizes of Influenza A/H1N1, A/H3N2, and B: A Statistical Method. PLoS Med 8(7): e1001051. doi:10.1371/journal.pmed.1001051
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Source:
Clare Weaver
Public Library of Science