From tests to truth: A misclassification-aware machine learning framework for estimating brucellosis seroprevalence in wild canids
Abstract
Brucellosis is an important zoonotic disease affecting humans, livestock, and wildlife, yet prevalence estimates in wild species are often underestimated due to limited attention to surveillance, as well as insufficient and biased sampling. To clarify exposure patterns in wild canids, we searched PubMed, Scopus, Web of Science, SciELO, and Google Scholar (1962-2025) for primary prevalence studies of Brucella species (spp.) in free-ranging canids. Serological data were analyzed using a misclassification-aware, multi-assay model that corrects for imperfect test sensitivity and specificity. Confirmatory polymerase chain reaction (PCR) and culture results were considered separately from serological data, as each offers a different perspective on disease status. Across 48 wild serology populations (n = 3,925 animals), the global misclassification-adjusted true seroprevalence was 8.2% (95% confidence interval [CI]: 5.1-11.3%). Confirmed active infection, based on PCR or culture, was uncommon (3.9%; 95% CI: 3.0-5.1%). Exposure levels varied across continents, with higher estimates in South America (approximately 18%) and lower levels in Europe (approximately 0.8%) and North America (approximately 4.1%). Data from Africa were limited, and Asian estimates were based on sparse wild samples, leading to wide uncertainty. Seroprevalence was consistently influenced by assay type, host species, and region. Overall, wild canids exhibit modest but widespread serological exposure to Brucella…
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