A Bayesian approach to estimating the performance of a bovine virus diarrhoea virus (BVDV) antibody ELISA bulk-tank milk test

Abstract

We investigated the operational performance (sensitivity and specificity) of a bovine virus diarrhoea virus (BVDV) antibody ELISA bulk-tank milk test for predicting the herd BVDV antibody status in young stock (as a relatively precise indicator of active BVDV infection). The study was based on results from the annual screenings under the Norwegian bovine virus diarrhoea (BVD) control and eradication program, lasting from 1993 to 1997. Empirical information from these annual screenings was the basis for prior assumptions about the true prevalence of young-stock-positive herds. Assumptions about prior distributions for sensitivity and specificity were based on the literature. Improved posterior test performance estimates were achieved applying a Bayesian approach using Gibbs sampling simulation. The simulations were run separately for each year, and yielded median values for sensitivity of 87% at the cut-off used in the BVD program. The posterior distributions were wide indicating much uncertainty in these estimates. The specificity estimates ranged from 79 to 92% and had narrower posterior estimates. The estimates differed by year. When running the same simulation procedures at a lower cut-off — after altering the sensitivity and specificity priors — the median sensitivity estimates increased to about 95%; the median specificity ranged from 71 to 83%. Due to low prevalence, the Bayesian method lacked power to assess the test sensitivity. A technically simpler descriptive graphing procedure (based on empirical information) provided equally useful insight into the bulk-tank milk test performance.