A Bayesian biosurveillance method that models unknown outbreak diseases

Authors:
Yanna Shen;Gregory F. Cooper
Affiliations:
Department of Biomedical Informatics and the Intelligent Systems Program, University of Pittsburgh, Pittsburgh, PA;Department of Biomedical Informatics and the Intelligent Systems Program, University of Pittsburgh, Pittsburgh, PA
Venue:
BioSurveillance'07 Proceedings of the 2nd NSF conference on Intelligence and security informatics: BioSurveillance
Year:
2007

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Abstract

Algorithms for detecting anomalous events can be divided into those that are designed to detect specific diseases and those that are non-specific in what they detect. Specific detection methods determine if patterns in the data are consistent with known outbreak diseases, as for example influenza. These methods are usually Bayesian. Non-specific detection methods attempt broadly to detect deviations from some model of the non-outbreak situation, regardless of which disease might be causing the deviation. Many frequentist outbreak detection methods are non-specific. In this paper, we introduce a Bayesian approach for detecting both specific and non-specific disease outbreaks, and we report a preliminary study of the approach.