title: Data normalization in biosurveillance: an information-theoretic approach.
abstract: An approach to identifying public health threats by characterizing syndromic surveillance data in terms of its surprisability is discussed. Surprisability in our model is measured by assigning a probability distribution to a time series, and then calculating its entropy, leading to a straightforward designation of an alert. Initial application of our method is to investigate the applicability of using suitably-normalized syndromic counts (i.e., proportions) to improve early event detection.
authors.affiliation: Johns Hopkins University Applied Physics Laboratory, Laurel, MD 20723, USA.
authors.forename: William
authors.initials: W
authors.lastname: Peter
authors.forename: Amir H
authors.initials: AH
authors.lastname: Najmi
authors.forename: Howard
authors.initials: H
authors.lastname: Burkom
mesh_terms: D000465:Algorithms; D023482:Bioterrorism / Q000517:prevention & control; D004196:Disease Outbreaks; D015991:Epidemiologic Measurements; D006801:Humans; D011159:Population Surveillance / Q000379:methods*; D040442:Public Health Informatics; D013223:Statistics as Topic