Elements of information theory
Elements of information theory
Rule-based anomaly pattern detection for detecting disease outbreaks
Eighteenth national conference on Artificial intelligence
The Journal of Machine Learning Research
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Detecting the high-risk zones as well as potential infected geographical neighbor is necessary and important to reduce the loss caused by infectious disease. However, it is a challenging work, since the outbreak of infectious disease is uncertain and unclear. Moreover, the detection should be efficient otherwise the best control and prevention time may be missed. To deal with this problem, we propose a geography high-risk zones detection method by capturing the significant change in the infectious disease monitoring data. The main contribution of this paper includes: (1) Analyzing the challenges of the early warning and detection of infectious disease outbreak; (2) Proposing a method to detect the zone that the number of monitoring cases changes significantly; (3) Defining the infection perturbation to describe the infection probability between two zones; (4) Designing an algorithm to measure the infection perturbation of infectious disease between adjacent zones; (5) Performing extensive experiments on both real-world data and synthetic data to demonstrate the effectiveness and efficiency of the proposed methods.