title: Using hierarchical mixture of experts model for fusion of outbreak detection methods.
abstract: A wide variety of disease outbreak detection methods has been developed in automated public health surveillance systems. The choice of outbreak detection method results in large changes in performance under different circumstances. In this paper, we investigate how outbreak detection methods can be combined in order to improve the overall detection performance. We used Hierarchical Mixture of Experts, which is a probabilistic model for combining classification methods, for fusion of detection methods. Simulated surveillance data for waterborne disease outbreaks are used in this research to train and evaluate a Hierarchical Mixture of Experts model. Performance evaluation of our approach with respect to sensitivity-specificity trade-off and detection timeliness is provided in comparison with several other detection methods.
authors.affiliation: Ecole Polytechnique de Montreal;
authors.forename: Nastaran
authors.initials: N
authors.lastname: Jafarpour
authors.affiliation: McGill University, Montreal, Canada.
authors.forename: Doina
authors.initials: D
authors.lastname: Precup
authors.affiliation: McGill University, Montreal, Canada.
authors.forename: Masoumeh
authors.initials: M
authors.lastname: Izadi
authors.affiliation: McGill University, Montreal, Canada.
authors.forename: David
authors.initials: D
authors.lastname: Buckeridge
mesh_terms: D000465:Algorithms; D004196:Disease Outbreaks; D006801:Humans; D015233:Models, Statistical; D011159:Population Surveillance / Q000379:methods*; D012680:Sensitivity and Specificity