Bayesian estimation of the true prevalence of paratuberculosis in Hungarian dairy cattle herds

Abstract

Paratuberculosis is a chronic incurable disease caused by Mycobacterium avium subsp. paratuberculosis (MAP), which leads to extensive economic losses on dairy farms, and may also pose serious public health risk to the consumers. The aim of our study was to estimate the true prevalence of paratuberculosis in commercial dairy cattle herds participating in a voluntary MAP testing programme that started in February 2018 in Hungary. Milk samples collected during official milk recording were used for MAP ELISA testing. A Bayesian two-stage hierarchical (herd and animal level) model was fitted to the data. Altogether, 26,437 cows from 51 herds were sampled, which represents 14.4 % of the Hungarian dairy cow population. The median herd size was 477 cows (interquartile range: 331–709). Each studied farm had at least one ELISA positive cow, resulting in a herd-level apparent prevalence of 100 %. The overall within herd apparent prevalence was 5.5 %. Herd-level true prevalence was estimated at 89.1 % [95 % credible interval (CrI): 80.3–95.6%]. Within the infected herds, the median animal-level true prevalence was 4.4 % (3.2–5.8%) for primiparous and 10.3 % (7.9–12.9%) for multiparous cows, respectively. The probability of having an animal-level true prevalence of at least 5% among primiparous cows, within infected herds, was 17.8 %. Similarly, the probability of having an animal-level true prevalence of at least 5% or 10 % among multiparous cows was 100 % and 56 %, respectively. Simulations assuming herd-level true prevalence varying from 50 to 100 % revealed high accuracy of our Bayesian model. Our study showed that a large percentage of the studied Hungarian dairy cattle herds was infected with MAP. © 2020 Elsevier B.V.