Bayesian latent class modelling of true prevalence in animal subgroups with application to bovine paratuberculosis infection.

Abstract

The prevalence of an infectious disease of animals living in separate groups (e.g. herds) is naturally analyzed using a Bayesian hierarchical latent class model. We propose an extension to this methodology by including subgroup level prevalence measures within the groups of animals. As an application illustrating the merits of our methodology, we reassessed the prevalence of bovine paratuberculosis (PTBC) infection in Hungarian commercial dairy farms. Our aim was to consolidate previous findings using a large amount of recent data and priors based on historical data. To model the subgroup level infection prevalence within animal groups, we considered correlated prevalences following beta distributions derived from independent normally distributed random herd effects. In the application, infection status of herds was handled as latent classes, multiparous and primiparous cows as within-herd subgroups. The novel methodology allows us to estimate both the mean and median conditional within-herd true prevalence (CWHP) related to each animal subgroup as well as other measures characterizing the interrelation of subgroups. The results of the application aligned with the findings of the former PTBC study, while the more recent and considerably larger dataset and the use of historical priors increased the reliability of the results. The STAN and JAGS codes of the application are available in Supplementary material.