JSM 2004 - Toronto

Abstract #301754

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Activity Number: 332
Type: Topic Contributed
Date/Time: Wednesday, August 11, 2004 : 10:30 AM to 12:20 PM
Sponsor: Section on Bayesian Statistical Science
Abstract - #301754
Title: A Bayesian Model for Livestock Disease Infection Prevalence Rates
Author(s): Eric A. Suess*+ and Ian A. Gardner and Wesley O. Johnson
Companies: California State University, Hayward and University of California, Davis and University of California, Davis
Address: Dept. of Statistics, Hayward, CA, 94542,
Keywords: disease surveillance ; prevalence estimation ; Bayesian ; MCMC
Abstract:

For livestock disease infection surveillance, we develop a Bayesian model for estimating infection rates. By incorporating expert prior opinion related to the animal disease along with test results from animals sampled within herds we produce posterior estimates of infection rates. The model is used to estimate the within-herd prevalence and the average within-herd prevalence, the prevalence of infected herds, and the regional prevalences. Correlation between the infection status of animals within herds part of the model. We use data from a national survey of Johne's Disease. The model is implemented using WinBugs and the MCMC output is analyzed in R.

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