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Stat Med. 2006 Jun 15;25(11):1803-16; discussion 1817-25.

A Bayesian dynamic model for influenza surveillance.

Author information

  • 1Department of Biostatistics, Boston University, Boston, MA, USA. sebas@bu.edu

Abstract

The severe acute respiratory syndrome (SARS) epidemic, the growing fear of an influenza pandemic and the recent shortage of flu vaccine highlight the need for surveillance systems able to provide early, quantitative predictions of epidemic events. We use dynamic Bayesian networks to discover the interplay among four data sources that are monitored for influenza surveillance. By integrating these different data sources into a dynamic model, we identify in children and infants presenting to the pediatric emergency department with respiratory syndromes an early indicator of impending influenza morbidity and mortality. Our findings show the importance of modelling the complex dynamics of data collected for influenza surveillance, and suggest that dynamic Bayesian networks could be suitable modelling tools for developing epidemic surveillance systems.

Copyright 2006 John Wiley & Sons, Ltd.

PMID:
16645996
[PubMed - indexed for MEDLINE]
PMCID:
PMC4128871
Free PMC Article

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