Activity monitoring: noticing interesting changes in behavior
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Time Series Analysis: Forecasting and Control
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A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
Bayesian biosurveillance of disease outbreaks
UAI '04 Proceedings of the 20th conference on Uncertainty in artificial intelligence
Detection of emerging space-time clusters
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
A Bayesian network for outbreak detection and prediction
AAAI'06 proceedings of the 21st national conference on Artificial intelligence - Volume 2
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Data Mining and Knowledge Discovery
Anomaly detection in large-scale data stream networks
Data Mining and Knowledge Discovery
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Reliable and accurate detection of disease outbreaks remains an important research topic in disease outbreak surveillance. A temporal surveillance system bases its analysis on data not only from the most recent time period, but also on data from previous time periods. A non-temporal system only looks at data from the most recent time period. There are two difficulties with a non-temporal system when it is used to monitor real data which often contain noise. First, it is prone to produce false positive signals during non-outbreak time periods. Second, during an outbreak, it tends to release false negative signals early in the outbreak, which can adversely affect the decision making process of the user of the system. We conjecture that by converting a non-temporal system to a temporal one, we may attenuate these difficulties inherent in a non-temporal system. In this paper, we propose a Bayesian network architecture for a class of temporal event surveillance models called BayesNet-T. Using this Bayesian network architecture, we can convert certain non-temporal surveillance systems to temporal ones. We apply this architecture to a previously developed non-temporal multiple-disease outbreak detection system called PC and create a temporal system called PCT. PCT takes Emergency Department (ED) patient chief complaint data as its input. The PCT system was constructed using both data (non-outbreak diseases) and expert assessments (outbreak diseases). We compare PCT to PC using a real influenza outbreak. Furthermore, we compare PCT to both PC and the classic statistical methods CUSUM and EWMA using a total of 240 influenza and Cryptosporidium disease outbreaks created by injecting stochastically simulated outbreak cases into real ED admission data. Our results indicate that PCT has a smaller mean time to detection than PC at low false alarm rates, and that PCT is more stable than PC in that once an outbreak is detected, PCT is better at maintaining the detection signal on future days.