The potential distribution of Bacillus anthracis suitability across Uganda using INLA

To reduce the veterinary, public health, environmental, and economic burden associated with anthrax outbreaks, it is vital to identify the spatial distribution of areas suitable for Bacillus anthracis, the causative agent of the disease. Bayesian approaches have previously been applied to estimate uncertainty around detected areas of B. anthracis suitability. However, conventional simulation-based techniques are often computationally demanding. To solve this computational problem, we use Integrated Nested Laplace Approximation (INLA) which can adjust for spatially structured random effects, to predict the suitability of B. anthracis across Uganda. We apply a Generalized Additive Model (GAM) within the INLA Bayesian framework to quantify the relationships between B. anthracis occurrence and the environment. We consolidate a national database of wildlife, livestock, and human anthrax case records across Uganda built across multiple sectors bridging human and animal partners using a One Health approach. The INLA framework successfully identified known areas of species suitability in Uganda, as well as suggested unknown hotspots across Northern, Eastern, and Central Uganda, which have not been previously identified by other niche models. The major risk factors for B. anthracis suitability were proximity to water bodies (0-0.3 km), increasing soil calcium (between 10 and 25 cmolc/kg), and elevation of 140-190 m. The sensitivity of the final model against the withheld evaluation dataset was 90% (181 out of 202 = 89.6%; rounded up to 90%). The prediction maps generated using this model can guide future anthrax prevention and surveillance plans by the relevant stakeholders in Uganda.

Automated summary

By integrating data from multiple sectors, applying Bayesian methods and generalized additive models, the suitability of Bacillus anthracis in Uganda was successfully identified and unknown hotspots were suggested. The major risk factors include proximity to water bodies, increasing soil calcium, and elevation. The model showed a high sensitivity to the evaluation dataset, and the prediction maps can guide future anthrax prevention and surveillance plans.