title: Bayesian prospective detection of small area health anomalies using Kullback-Leibler divergence.
abstract: Early detection of unusual health events depends on the ability to rapidly detect any substantial changes in disease, thus facilitating timely public health interventions. To assist public health practitioners to make decisions, statistical methods are adopted to assess unusual events in real time. We introduce a surveillance Kullback-Leibler measure for timely detection of disease outbreaks for small area health data. The detection methods are compared with the surveillance conditional predictive ordinate within the framework of Bayesian hierarchical Poisson modeling and applied to a case study of a group of respiratory system diseases observed weekly in South Carolina counties. Properties of the proposed surveillance techniques including timeliness and detection precision are investigated using a simulation study.
authors.affiliation: Department of Public Health sciences, Medical University of South Carolina, Charleston, SC, USA.
authors.forename: Chawarat
authors.initials: C
authors.lastname: Rotejanaprasert
authors.affiliation: Department of Public Health sciences, Medical University of South Carolina, Charleston, SC, USA.
authors.forename: Andrew
authors.initials: A
authors.lastname: Lawson
mesh_terms: D000465:Algorithms; D001499:Bayes Theorem; D004196:Disease Outbreaks; D006801:Humans; D011159:Population Surveillance / Q000379:methods*; D011446:Prospective Studies; D017062:Small-Area Analysis; D013022:South Carolina
keywords: Bayesian; Kullback–Leibler; SCPO; prospective surveillance; spatial; temporal