Activity monitoring: noticing interesting changes in behavior
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Data mining for early disease outbreak detection
Data mining for early disease outbreak detection
Inference in multiply sectioned Bayesian networks with extended Shafer-Shenoy and lazy propagation
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
Object-oriented Bayesian networks
UAI'97 Proceedings of the Thirteenth conference on Uncertainty in artificial intelligence
The Journal of Machine Learning Research
Learning to Detect Adverse Traffic Events from Noisily Labeled Data
PKDD 2007 Proceedings of the 11th European conference on Principles and Practice of Knowledge Discovery in Databases
Integrating a Commuting Model with the Bayesian Aerosol Release Detector
BioSecure '08 Proceedings of the 2008 International Workshop on Biosurveillance and Biosecurity
Bayesian prediction of an epidemic curve
Journal of Biomedical Informatics
Proceedings of the 2008 conference on ECAI 2008: 18th European Conference on Artificial Intelligence
When gossip is good: distributed probabilistic inference for detection of slow network intrusions
AAAI'06 proceedings of the 21st national conference on Artificial intelligence - Volume 2
A Bayesian network for outbreak detection and prediction
AAAI'06 proceedings of the 21st national conference on Artificial intelligence - Volume 2
COD: online temporal clustering for outbreak detection
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 1
Data Mining and Knowledge Discovery
A review of public health syndromic surveillance systems
ISI'06 Proceedings of the 4th IEEE international conference on Intelligence and Security Informatics
Computer Methods and Programs in Biomedicine
Sensing Technologies for Societal Well-Being: A Needs Analysis
International Journal of Interdisciplinary Telecommunications and Networking
The picture of health: map-based, collaborative spatio-temporal disease tracking
Proceedings of the First ACM SIGSPATIAL International Workshop on Use of GIS in Public Health
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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.