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Comparison of Aberration Detection Algorithms for Syndromic Surveillance


This paper describes a study of various aberration detection algorithms currently used in syndromic surveillance and one based on artificial neural networks developed at Guelph. The goal of the research is not to select one ìwinningî algorithm but to instead understand the characteristics of the algorithms so that a systems designer can successfully use all of these algorithms in an outbreak detection system.

Submitted by elamb on