A Wavelet-based Anomaly Detector for Early Detection of Disease Outbreaks

TitleA Wavelet-based Anomaly Detector for Early Detection of Disease Outbreaks
Publication TypeConference Proceedings
Year of Publication2006
AuthorsLotze, T., G. Shmueli, S. Murphy, and H. S. Burkom
Conference NameProceedings of the 23rd International Conference on Machine Learning (ICML), Workshop on Machine Learning Algorithms for Surveillance and Event Detection
Conference LocationPittsburgh, PA
Abstract

We describe a wavelet-based automated algorithm for
detecting disease outbreaks in temporal syndromic data.
We describe the method, which improves upon the
Goldenberg et al. (2002) algorithm and its implementation
on a diverse set of real syndromic data from multiple data
sources and multiple geographical locations. Our results
show a robust performance which is comparable to a few
recently suggested methods.

URLhttp://web.engr.oregonstate.edu/~wong/workshops/icml2006/papers/lotze.pdf
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