Spatial Patterns and Epidemiological Drivers of Foot‐and‐Mouth Disease Outbreaks in Uganda

Foot‐and‐mouth disease (FMD) places a heavy economic burden on farmers and animal health authorities due to its clinical effects, the high cost associated with its prevention and control, and trade restrictions on livestock and livestock products in countries without FMD‐free recognition like Uganda. Building on our previous work using cattle movement networks for risk mapping, we demonstrate that Bayesian disease mapping can serve as a complementary epidemiological approach to identify and communicate spatial variation in outbreak risk. A Bayesian Poisson mixed‐effects spatial regression model was built with the Integrated Nested Laplace Approximation (INLA) to analyze FMD outbreak data from 2014 to 2019. This analysis examined the spatial variation in risk and assessed potential drivers of its distribution, providing insights to support targeted and cost‐effective disease surveillance and control strategies. Cattle density, Enhanced Vegetation Index (EVI), deprivation score, and human density were associated with the risk of outbreaks but did not explain most of its spatial distribution, suggesting that additional epidemiological factors may contribute and should be examined in future models. Mean vaccination coverage was associated with an increased risk of outbreaks, consistent with the predominantly reactive vaccination program implemented during the study period. Adjusted district‐specific risk estimates and exceedance probabilities (EPs) highlighted districts across the cattle corridor, a wide zone that extends from the southwestern to the northeastern part of the country, as areas of greater risk. Lower‐risk areas mapped to large parts of the northern and western regions, a finding that may reflect limited disease detection, given the past evidence of viral exposure in these areas. A better understanding of district‐specific factors contributing to the outbreaks, together with a timely and sensitive surveillance system, is crucial for supporting evidence‐based FMD control plans. Such strategies should account for the characteristics of local communities, the typical operation of livestock production systems, and their unique challenges to improve their effectiveness.