Bayesian Biosurveillance of Disease Outbreaks
This addresses the critical need for reliable biosurveillance in public health, though it is incremental as it builds on existing Bayesian network methods with specific scaling techniques.
The paper tackles the problem of early detection of disease outbreaks by using causal Bayesian networks to model spatio-temporal patterns of respiratory anthrax infection, demonstrating a proof-of-concept that scales to millions of nodes for real-time surveillance.
Early, reliable detection of disease outbreaks is a critical problem today. This paper reports an investigation of the use of causal Bayesian networks to model spatio-temporal patterns of a non-contagious disease (respiratory anthrax infection) in a population of people. The number of parameters in such a network can become enormous, if not carefully managed. Also, inference needs to be performed in real time as population data stream in. We describe techniques we have applied to address both the modeling and inference challenges. A key contribution of this paper is the explication of assumptions and techniques that are sufficient to allow the scaling of Bayesian network modeling and inference to millions of nodes for real-time surveillance applications. The results reported here provide a proof-of-concept that Bayesian networks can serve as the foundation of a system that effectively performs Bayesian biosurveillance of disease outbreaks.