Bayesian spatiotemporal evaluation of bovine anaplasmosis seroprevalence in Missouri (2010-2021)

Bovine anaplasmosis, caused by the rickettsia Anaplasma marginale, is an economically important and globally distributed tick- and blood-borne disease of cattle. Although cases are known to be widespread in Missouri, current spatiotemporal trends, presence of high-risk areas, and any potential drivers for disease trends in Missouri are poorly documented. To address these knowledge gaps, this study analyzed spatiotemporal patterns of annual, county-level anaplasmosis case counts using a Bayesian hierarchical framework. Seropositive cases of anaplasmosis detected at the University of Missouri Veterinary Medical Diagnostic Laboratory (n = 1,944) between the years 2010-2021 were used to construct data-driven Bayesian hierarchical models. All the models consisted of imputation sub-models to alleviate issues related to missing observations from spatiotemporal units (114 counties and 1 independent city, 12 years). Three progressively complex models with different assumptions for capturing the spatial, temporal, and spatiotemporal interactions that explained the variability in case counts were prepared. Model-1 included linear predictors decomposed into structured and unstructured terms for the temporal and spatial processes. Model-2 included separate temporal terms for smoothing each spatial entity and spatial smoothing terms for each temporal entity. This model was extended in Model-3, which included space-time interaction effect using first-order conditional autoregressive (CAR) priors. Based on the Deviance Information Criterion (DIC), Model 3 was superior at explaining space/time variability in the detected seropositive cases of bovine anaplasmosis. These findings indicate that distribution and risk of bovine anaplasmosis seroprevalence in Missouri are non-uniform, and potentially driven by environmental and/or management factors, operating at local and regional scales, that when identified could inform mitigation strategies.