Uncertainty in Artificial Intelligence
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Bayesian Biosurveillance of Disease Outbreaks
Gregory Cooper, Denver Dash, John Levander, Weng-Keen Wong, William Hogan, Michael Wagner
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.
Keywords: null
Pages: 94-103
PS Link:
PDF Link: /papers/04/p94-cooper.pdf
AUTHOR = "Gregory Cooper and Denver Dash and John Levander and Weng-Keen Wong and William Hogan and Michael Wagner",
TITLE = "Bayesian Biosurveillance of Disease Outbreaks",
BOOKTITLE = "Proceedings of the Twentieth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-04)",
ADDRESS = "Arlington, Virginia",
YEAR = "2004",
PAGES = "94--103"

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