ArticleVeterinary medicine international2026
Bayesian Modeling of Bovine Tuberculosis Prevalence for Enhanced Estimation and Control in Intensive Dairy Farms: A Longitudinal Decision-Support Tools for Veterinarians and Policymakers.
Article in Veterinary medicine international, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Accurate prevalence estimation is crucial for effective bovine tuberculosis (bTB) control, as unreliable data often gives rise to strategic failures, misallocation of resources, and loss of stakeholder trust. In an epidemiological study, obtaining true prevalence (Tp) requires accounting for misclassification and measurement errors inherent in diagnostic testing. Given the imperfect specificity and sensitivity of single intradermal comparative tuberculin tests (SICCTT), Bayesian methods present a natural way to propagate these uncertainties. This study uses a Bayesian approach to improve bTB prevalence estimates from imperfect diagnostic tests. It transforms raw screening data from a dairy farm case study into reliable evidence for better veterinary decisions and targeted disease control policies. Prior sensitivity estimates of SICCTT under standard and severe interpretations were 70.0% (BPI: 52.0%-84.6%) and 78.68% (BPI: 62.1%-90.5%), with corresponding specificities of 96.4% (BPI: 92.2%-98.8%) and 94.7% (BPI: 91.4%-97.1%). Posterior estimates showed a slight reduction in sensitivity under standard interpretation (68.3%, BPI: 50.4-83.7), while other estimates remained comparable to prior distributions. Bayesian modeling revealed notable variation in Tp across testing rounds. Rounds 1 and 3 had the highest prevalence, 27.6% (BPI: 18.5-40.6) and 30.9% (BPI: 23.3-47.5) under standard interpretation, respectively. In contrast, test round 4 yielded the lowest prevalence estimate at 4.1% (BPI: 0.6-8.6). The posterior probability of Tp being below 1%, the threshold for herd freedom from
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