Bayesian biosurveillance using multiple data streams

Wong WK, Cooper GF, Dash DH, Levander JD, Dowling J, Hogan WR, Wagner MM. Bayesian biosurveillance using multiple data streams. Morbidity and Mortality Weekly Report 54 (2005) 63-69.  PMID: 16177695

Introduction: Emergency Department (ED) records and over-the-counter (OTC) sales data are
two of the most commonly used data sources for syndromic surveillance.  Most current detection
algorithms monitor these data sources separately, and either do not combine them, or combine
them in an ad hoc fashion.  This paper introduces a causal model that coherently combines the
two data sources in order to perform outbreak detection.
Objectives: This paper presents a Bayesian biosurveillance algorithm called PANDA that
combines information from multiple data streams.  We describe the model, along with an
explication of assumptions and techniques used to make this approach scalable for real-time
surveillance of a large population.
Methods: We extend the causal Bayesian network model used in (1) to incorporate evidence
from daily OTC sales data.  We model, at the level of individual people, the actions that result in
the purchase of OTC products, as well as admission to an ED.
Results: The aim of this paper is to describe a detection model for monitoring both ED and OTC
data.  This paper provides preliminary support that despite the complexities of this model, the
running time is tractable.
Conclusion: This paper introduces a new Bayesian biosurveillance algorithm that models the
interaction between ED and OTC data.  It also provides preliminary results that are positive
regarding the run time of the algorithm.

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Wong WK, Cooper GF, Dash DH, Levander JD, Dowling J, Hogan WR, Wagner MM.
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