title: Prediction of Peaks of Seasonal Influenza in Military Health-Care Data.
abstract: Influenza is a highly contagious disease that causes seasonal epidemics with significant morbidity and mortality. The ability to predict influenza peak several weeks in advance would allow for timely preventive public health planning and interventions to be used to mitigate these outbreaks. Because influenza may also impact the operational readiness of active duty personnel, the US military places a high priority on surveillance and preparedness for seasonal outbreaks. A method for creating models for predicting peak influenza visits per total health-care visits (ie, activity) weeks in advance has been developed using advanced data mining techniques on disparate epidemiological and environmental data. The model results are presented and compared with those of other popular data mining classifiers. By rigorously testing the model on data not used in its development, it is shown that this technique can predict the week of highest influenza activity for a specific region with overall better accuracy than other methods examined in this article.
authors.affiliation: Johns Hopkins University Applied Physics Laboratory, Laurel, MD, USA.
authors.forename: Anna L
authors.initials: AL
authors.lastname: Buczak
authors.affiliation: Johns Hopkins University Applied Physics Laboratory, Laurel, MD, USA.
authors.forename: Benjamin
authors.initials: B
authors.lastname: Baugher
authors.affiliation: Johns Hopkins University Applied Physics Laboratory, Laurel, MD, USA.
authors.forename: Erhan
authors.initials: E
authors.lastname: Guven
authors.affiliation: Johns Hopkins University Applied Physics Laboratory, Laurel, MD, USA.
authors.forename: Linda
authors.initials: L
authors.lastname: Moniz
authors.affiliation: Johns Hopkins University Applied Physics Laboratory, Laurel, MD, USA.
authors.forename: Steven M
authors.initials: SM
authors.lastname: Babin
authors.affiliation: Armed Forces Health Surveillance Branch, Defense Health Agency, Silver Spring, MD, USA.
authors.forename: Jean-Paul
authors.initials: JP
authors.lastname: Chretien
keywords: association rule mining; fuzzy logic; influenza; prediction; predictor variables