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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.

Original publication

DOI

10.1177/0962280216652156

Type

Journal article

Journal

Statistical methods in medical research

Publication Date

04/2018

Volume

27

Pages

1076 - 1087

Addresses

Department of Public Health sciences, Medical University of South Carolina, Charleston, SC, USA.

Keywords

Humans, Population Surveillance, Small-Area Analysis, Bayes Theorem, Prospective Studies, Disease Outbreaks, Algorithms, South Carolina