Online Program

WITHDRAWN: Bayesian Detection for Online Spatial Surveillance of Small Area Count Data Using Kullback-Leibler Divergence

Chawarat Rotejanaprasert, Medical University of South Carolina 
Andrew Lawson, Medical University of South Carolina 

Keywords: public health surveillance, Bayesian, Kullback-Leibler, small area data, spatial

The importance of early detection of unusual health events depends on the ability to rapidly detect any substantial changes in disease, thus facilitating timely public health policy and interventions. To assist public health practitioners make decisions, statistical methods are adopted to assess unusual events in real time. We introduce a surveillance Kullback-Leibler (SKL) measure for timely detection of disease outbreaks for small area data. We investigate the performance of the proposed surveillance technique and compare with the surveillance conditional predictive ordinate (SCPO) within the framework of Bayesian hierarchical Poisson modeling using a simulation study. Finally, the detection methods are applied to a case study of a group of respiratory system diseases observed weekly in South Carolina counties.