A Bayesian Biosurveillance Method that Models Unknown Outbreak Diseases

Shen Y, Cooper GF. A Bayesian Biosurveillance Method that Models Unknown Outbreak Diseases. Proceedings of the Conference on Intelligence and Security Informatics: Biosurveillance, (2007) 209-215.

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.
Keywords: anomaly detection, biosurveillance, Bayesian methods

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Shen Y, Cooper GF.
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