JSM 2005 Online Program

Abstract #304319

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Activity Number:	455
Type:	Contributed
Date/Time:	Wednesday, August 10, 2005 : 2:00 PM to 3:50 PM
Sponsor:	Section on Statistics in Epidemiology
Abstract - #304319
Title:	A Composite Clustering Model for Space-time Disease Counts
Author(s):	Ping Yan*+ and Murray K. Clayton
Companies:	University of Wisconsin, Madison and University of Wisconsin, Madison
Address:	932 A Eagle Heights, Madison, WI, 53705, United States
Keywords:	space-time disease clustering ; hierarchical model ; RJMCMC
Abstract:	Modeling clustered disease rates over space and time can be helpful in providing indications of possible exposures and planning corresponding public health responses. Though a considerable number of studies discuss the modeling of spatio-temporal patterns of disease, most do not directly model a spatiotemporal clustering structure and therefore could be less effective for detecting clustering. We recently developed a clustering model for detecting space-time disease clusters. In this paper, we investigate a composite model that combines this model with a hierarchical model for disease mapping (Waller et al. 1997), thus combining the best features of these two methods. Inference is performed in a Bayesian framework using reversible jump Markov chain Monte Carlo. This idea will be illustrated through a simulation study.

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