Abstract
Algorithms for detecting anomalous events can be divided into those that are designed to detect specific diseases and those that are non-specific in what they detect. Specific detection methods determine if patterns in the data are consistent with known outbreak diseases, as for example influenza. These methods are usually Bayesian. Non-specific detection methods attempt broadly to detect deviations from some model of the non-outbreak situation, regardless of which disease might be causing the deviation. Many frequentist outbreak detection methods are non-specific. In this paper, we introduce a Bayesian approach for detecting both specific and non-specific disease outbreaks, and we report a preliminary study of the approach.
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References
Wong, W.-K.: Data mining for early disease outbreak detection. Doctoral Dissertation, Carnegie Mellon University, Pittsburgh (2004)
Fawcett, T., Provost, F.: Adaptive fraud detection. Data Mining and Knowledge Discovery 1(3), 291–316 (1997)
Denning, D.: An intrusion-detection model. IEEE Transactions on Software Engineering (1987)
Cooper, G.F.: A Bayesian method for learning belief networks that contain hidden variables. Journal of Intelligent Information Systems 4, 71–88 (1995)
Shen, Y., Cooper, G.F.: Bayesian disease outbreak detection that includes a model of unknown disease. Department of Medical Informatics, University of Pittsburgh: Technical Report No. DBMI-07-351 (2007)
Hutwagner, L., Thompson, W., Seeman, G.M.: The bioterrorism preparedness and response early aberration reporting system (EARS). Journal of Urban Health, 80(2), Supplement 1, i89–i96 (2003)
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Shen, Y., Cooper, G.F. (2007). A Bayesian Biosurveillance Method That Models Unknown Outbreak Diseases. In: Zeng, D., et al. Intelligence and Security Informatics: Biosurveillance. BioSurveillance 2007. Lecture Notes in Computer Science, vol 4506. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-72608-1_21
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DOI: https://doi.org/10.1007/978-3-540-72608-1_21
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-72607-4
Online ISBN: 978-3-540-72608-1
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