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Activity Number: 650
Type: Contributed
Date/Time: Thursday, August 8, 2013 : 8:30 AM to 10:20 AM
Sponsor: Section on Statistics in Epidemiology
Abstract - #308963
Title: Statistical Prediction for Virginia Lyme Disease Emergence Based on Spatial-Temporal Count Data
Author(s): Yuanyuan Duan*+ and Jie Li and Yili Hong and Korine N. Kolivras and Stephen P. Prisley and James B. Campbell and David N. Gaines
Companies: Virginia Tech and Virginia Tech and Virginia Polytechnic Institute & State University and Virginia Tech and Virginia Tech and Virginia Tech and Virginia Department of Health
Keywords: Spatial-temporal Conditional Auto-Regressive ; Markov chain Monte Carlo ; Lyme disease ; Areal count data ; Poisson regression ; Random effects
Abstract:

The emergence of infectious diseases over the past several decades has highlighted the need to better understand and prepare for epidemics as endemic infectious diseases. These diseases are usually expanding their geographic range and are recorded over multiple time periods, making the analysis and prediction more complicated. In this study, based on areal (census tract level) counts data of Lyme disease cases in Virginia from 2003 to 2010; we develop a predictive geographic model of human incidence to identify areas of high risk in Virginia. Our preliminary study use SaTScan with the Discrete Poisson model to visualize the spatial-temporal emergence pattern of Lyme disease. The results indicate the presence of spatial clusters and temporal-spatial clusters. We build a spatial Poisson regression model with random effects to incorporate spatial-temporal correlations. The random effects are modeled by Spatial Temporal Conditional Auto-Regressive (STCAR) model. We use Markov chain Monte Carlo (MCMC) algorithm to fit the model in a Bayesian framework.


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