Mapping suitability for Buruli ulcer at fine spatial scales across Africa: A modelling study

Abstract

Buruli ulcer (BU) is a disabling and stigmatising neglected tropical disease (NTD). Its distribution and burden are unknown because of underdiagnosis and underreporting. It is caused by Mycobacterium ulcerans, an environmental pathogen whose environmental niche and transmission routes are not fully understood. The main control strategy is active surveillance to promote early treatment and thus limit morbidity, but these activities are mostly restricted to well-known endemic areas. A better understanding of environmental suitability for the bacterium and disease could inform targeted surveillance, and advance understanding of the ecology and burden of BU. We used previously compiled point-level datasets of BU and M. ulcerans occurrence, evidence for BU occurrence within national and sub-national areas, and a suite of relevant environmental covariates in a distribution modelling framework. We fitted relationships between BU and M. ulcerans occurrence and environmental predictors by applying regression and machine learning based algorithms, combined in an ensemble model to characterise the optimal ecological niche for the disease and bacterium across Africa at a resolution of 5km x 5km. Proximity to waterbodies was the strongest predictor of suitability for BU, followed potential evapotranspiration. The strongest predictors of suitability for M. ulcerans were deforestation and potential evapotranspiration. We identified patchy foci of suitability throughout West and Central Africa, including areas with no previous evidence of the disease. Predicted suitability for M. ulcerans was wider but overlapping with that of BU. The estimated population living in areas predicted suitable for the bacterium and disease was 46.1 million. These maps could be used to inform burden estimations and case searches which would generate a more complete understanding of the spatial distribution of BU in Africa, and may guide control programmes to identify cases beyond the well-known endemic areas.

Fig 1. Selection of model occurrence points from Buruli ulcer database.

Selection is shown separately for Buruli ulcer occurrences (A) and environmental occurrences of Mycobacterium ulcerans DNA.

Fig 2

Distribution of occurrence records for environmental modelling of Burli ulcer (BU) and Mycobacterium ulcerans (MU) (A) Pink dots represent origins of clinically-diagnosed BU cases, red dots represent confirmed cases. (B) Red dots show locations where M. ulcerans DNA has been isolated from environmental samples and distinguished from DNA from other mycobacteria by multiplex qPCR, or by variable nucleotide tandem repeat, or mycobacterial interspersed repetitive unit typing. All maps were produced in ArcMap 10.7 (ESRI Inc., Redlands CA, USA).

Fig 3

A.) Predicted environmental suitability for the occurrence of BU disease and associated error of prediction. B.) Predicted environmental suitability for the occurrence of M. ulcerans in the environment and associated error of prediction. All maps were produced in ArcMap 10.7 (ESRI Inc., Redlands CA, USA).

Fig 4. Predicted overlap of environmental suitability for BU and of M. ulcerans occurrence.

Pink colour represents areas where Mycobacterium ulcerans (MU) is predicted to occur based on the optimal threshold of environmental suitability (0.56) but where Buruli ulcer (BU) is not predicted. Red represents areas where BU is predicted based on the optimal threshold of environmental suitability (0.51) but MU is not. Both BU and MU are predicted to occur in areas shown in dark red. All maps were produced in ArcMap 10.7 (ESRI Inc., Redlands CA, USA).