Journal
STATISTICAL METHODS IN MEDICAL RESEARCH
Volume 27, Issue 4, Pages 1076-1087Publisher
SAGE PUBLICATIONS LTD
DOI: 10.1177/0962280216652156
Keywords
Bayesian; Kullback-Leibler; SCPO; spatial; prospective surveillance; temporal
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Funding
- NCI NIH HHS [R03 CA179665] Funding Source: Medline
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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.
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