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.

Background: Caprine arthritis encephalitis (CAE) is a major viral disease of goats, caused by small ruminant lentivirus (SRLV), associated with chronic arthritis, mastitis, pneumonia, and encephalitis, leading to economic losses and reduced animal welfare. This study aimed to estimate the true prevalence of CAE in Hungarian goat herds, based on nationwide sampling and statistical modeling. Methods: Blood samples from 1218 goats in 53 herds were tested using ELISA, and true prevalence was estimated by Bayesian analysis. Results: The mean herd true prevalence (HTP) was 29.1% (95% CrI: 20.8–38.5%), while within the infected herds, the conditional within herd prevalence (CWHP) reached 58% ± 27.1%. Medium- and large-sized herds (>50 animals) showed the highest mean HTP (77.8% and 74.9%, respectively). No significant regional differences were observed, indicating that CAE is uniformly distributed across the country. Conclusions: Our findings place Hungary among moderately to highly affected European countries and highlight the need for a nationwide control strategy integrating routine serological surveillance, biosecurity improvements, farmer education, and long-term tools such as selective breeding.

A Bayes-i módszertan széles körben elterjedt fertőző betegségek prevalenciájának modellezésére az állatorvosi szakirodalomban, hiszen ezzel a megközelítéssel a korábbi ismeretek és az új adatok egyszerre építhetőek be a becslésekbe. A szerzők áttekintik a Bayes-i modellezés alapvető fogalmait és működési elveit, továbbá bemutatják, hogyan alkalmazható ez a módszer a gyakorlatban a paratuberkulózis valódi prevalenciájának telepi szintű becslésére tejelő szarvasmarhák esetében (https://github.com/VeresKatalin/PTBC). A modell eredményei alapján egyszer ellett tehenek esetében a valódi prevalencia a látszólagos prevalencia 1,6-szorosaként, többször ellett teheneknél pedig 1,5-szöröseként becsülhető.