title: A Bayesian dynamic model for influenza surveillance.
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.
authors.affiliation: Department of Biostatistics, Boston University, Boston, MA, USA. sebas@bu.edu
authors.forename: Paola
authors.initials: P
authors.lastname: Sebastiani
authors.forename: Kenneth D
authors.initials: KD
authors.lastname: Mandl
authors.forename: Peter
authors.initials: P
authors.lastname: Szolovits
authors.forename: Isaac S
authors.initials: IS
authors.lastname: Kohane
authors.forename: Marco F
authors.initials: MF
authors.lastname: Ramoni
mesh_terms: D001499:Bayes Theorem; D002675:Child, Preschool; D004196:Disease Outbreaks; D006801:Humans; D007223:Infant; D053124:Influenza A Virus, H5N1 Subtype / Q000254:growth & development*; D007251:Influenza, Human / Q000453:epidemiology* / Q000517:prevention & control / Q000821:virology; D008954:Models, Biological; D015233:Models, Statistical; D011159:Population Surveillance / Q000379:methods*; D014481:United States