Spatial Analysis of Anthropogenic Landscape Disturbance and Buruli Ulcer Disease in Benin

Abstract

Background: Land use and land cover (LULC) change is one anthropogenic disturbance linked to infectious disease emergence. Current research has focused largely on wildlife and vector-borne zoonotic diseases, neglecting to investigate landscape disturbance and environmental bacterial infections. One example is Buruli ulcer (BU) disease, a necrotizing skin disease caused by the environmental pathogen Mycobacterium ulcerans (MU). Empirical and anecdotal observations have linked BU incidence to landscape disturbance, but potential relationships have not been quantified as they relate to land cover configurations.

Methodology/principal findings: A landscape ecological approach utilizing Bayesian hierarchical models with spatial random effects was used to test study hypotheses that land cover configurations indicative of anthropogenic disturbance were related to Buruli ulcer (BU) disease in southern Benin, and that a spatial structure existed for drivers of BU case distribution in the region. A final objective was to generate a continuous, risk map across the study region. Results suggested that villages surrounded by naturally shaped, or undisturbed rather than disturbed, wetland patches at a distance within 1200 m were at a higher risk for BU, and study outcomes supported the hypothesis that a spatial structure exists for the drivers behind BU risk in the region. The risk surface corresponded to known BU endemicity in Benin and identified moderate risk areas within the boundary of Togo.

Conclusions/significance: This study was a first attempt to link land cover configurations representative of anthropogenic disturbances to BU prevalence. Study results identified several significant variables, including the presence of natural wetland areas, warranting future investigations into these factors at additional spatial and temporal scales. A major contribution of this study included the incorporation of a spatial modeling component that predicted BU rates to new locations without strong knowledge of environmental factors contributing to disease distribution.

Fig 1. Study area and land cover classes.

Fig 2. BU positive and BU negative community locations.

Fig 3. Examples of land cover configurations resulting in high and low values for the three landscape metric calculations.

Fig 4. New locations gridded at 5 km intervals for BU rate predictions at unobserved locations.

Fig 5. Observed (A) vs. fitted (B) BU rates.

Fig 6. Map of spatial random effects. Red, orange, and yellow colors indicate areas where unknown, spatially structured variables that were not included in the model make a higher contribution to BU rates than in other areas of the study region.

Fig 7. Interpolated mean of the posterior predictive distribution.

Predicted rates per 100,000 individuals. Red, orange, and yellow colors indicate areas with higher BU rates predicted, while blue and green colors represent areas predicted to have low BU rates.

Fig 8. A) Areas with aggregated and complexly shaped wetland and forest patches surrounding locations with high BU prevalence.

B) Uniformly shaped forest patches surrounding locations with no BU prevalence.