Abstract
Paratuberculosis is a widespread infectious disease in ruminants that leads to significant economic losses in livestock production. In this study, we developed a practical method for predicting the likelihood of the herd-level presence of the infection and estimating its prevalence in subgroups of a dairy herd-specifically, first-time calving cows (primiparous) and those that have calved more than once (multiparous). We fit a Bayesian hierarchical model to cow-level data, incorporating prior knowledge about regional prevalence of infection to improve the accuracy and reliability of the estimates. The model was tested using synthetic data representing six regional scenarios in four countries (Chile, Denmark, Italy, and Hungary). The likelihood that a herd is infected is evaluated using Bayes factors and posterior probability of infection. Both the Bayes factor and the posterior probability of infection classified the simulated herds in accordance with the proportions of infected herds. Summary measures obtained for within-herd true prevalence estimates demonstrated acceptable accuracy. The R and STAN codes of the model are available as an open-access tool. The model can be customized for any region using real local data and prior information. The relationship between true and apparent prevalence is linear and stable and therefore can be estimated well. We found that, in Hungary, the TP/AP ratios were 1.6 and 1.5 for primi- and multiparous cows, respectively.