BVD CONSULT

Sample size calculations for Bayesian prediction of bovine viral-diarrhoea-virus infection in beef herds

aDepartment of Statistics, University of Wyoming, Dept. 3332, 1000 E. University Avenue, Laramie, WY 82071, USA bDepartment of Microbiology, Colorado State University, Fort Collins, CO 80523, USA

Abstract

We used a Bayesian classification approach to predict theĀ  bovine viral-diarrhoea -virus infection status of a herd when the prevalence of persistently infected animals in such herds is very small (e.g. <1%). An example of the approach is presented using data on beef herds in Wyoming, USA. The approach uses past covariate information (serum-neutralization titres collected on animals in 16 herds) within a predictive model for classification of a future observable herd. Simulations to estimate misclassification probabilities for different misclassification costs and prevalences of infected herds can be used as a guide to the sample size needed for classification of a future herd.

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This article was published in Preventive Veterinary Medicine, 62, S. Huzurbazara, Hana Van Campenb and Mark B. McLeana, Sample size calculations for Bayesian prediction of bovine viral-diarrhoea-virus infection in beef herds, 217-232, Copyright Elsevier 2004.