Abstract
Bovine brucellosis is an endemic zoonotic disease in South Africa with significant consequences for livestock productivity and public health. Although routine laboratory surveillance data from the Rose Bengal Test (RBT) are widely collected, they are seldom used to investigate temporal and spatial patterns of disease detection. This study aimed to examine temporal, seasonal, and spatial predictors of RBT positivity for bovine brucellosis in Mpumalanga Province, South Africa. A retrospective observational study was conducted using routine laboratory records from the Mpumalanga Provincial Veterinary Laboratory between January 2021 and December 2024. The dataset included all bovine serum samples with complete information on testing date, municipality, and RBT results. Laboratory submissions were recorded as batches, defined as groups of serum samples submitted together to the laboratory as part of a single surveillance or investigation event. The primary outcome was batch-level RBT positivity, defined as the presence of at least one RBT-positive serum sample within a submission batch. Temporal (year of testing), seasonal (season of submission), and spatial (local municipality area) variables were evaluated as predictors of RBT positivity using logistic regression models. Mixed-effects logistic regression accounted for the clustering of submissions within municipalities. A total of 568 submission batches comprising 67,974 serum samples were analysed, of which 6182 tested positive, yielding an overall positivity of 9.1%. RBT positivity increased significantly in 2023 compared with 2021 (AOR = 2.47; 95% CI: 2.27-2.68). Seasonal variation was observed, with higher odds of positivity in spring (AOR = 1.80; 95% CI: 1.65-1.97) and lower odds in autumn and winter relative to summer. Mixed-effects modelling indicated significant residual spatial heterogeneity in RBT positivity across municipalities. Routine laboratory surveillance data can provide valuable epidemiological insights into the temporal, seasonal, and spatial dynamics of bovine brucellosis detection and support risk-based surveillance strategies in endemic livestock systems.