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
We used a Bayesian classification approach to predict the 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.
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.