Sample size calculations for Bayesian prediction of bovine viral-diarrhoea-virus infection in beef herds.
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.
Huzurbazar
S
S
Department of Statistics, University of Wyoming, Dept. 3332, 1000 E. University Avenue, Laramie, WY 82071, USA. lata@uwyo.edu
Van Campen
Hana
H
McLean
Mark B
MB
D000818:Animals; D001499:Bayes Theorem; D001912:Bovine Virus Diarrhea-Mucosal Disease / Q000453:epidemiology* / Q000517:prevention & control*; D002417:Cattle; D001908:Diarrhea Viruses, Bovine Viral; D015995:Prevalence; D014959:Wyoming / Q000453:epidemiology