  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.
