Mapping suitability for Buruli ulcer at fine spatial scales across Africa: a modelling study
Authors: Simpson Hope , Tabah Earnest Njih , Phillips Richard , Frimpong Michael , Maman Issaka , Timothy Joseph , Saunderson Paul , Pullan Rachel L , Cano Jorge 
Published: April 2020

Background

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. Active BU case searches can limit morbidity by identifying cases and linking them to treatment, but these are mostly restricted to well-known endemic areas. A better understanding of environmental suitability for environmental reservoirs of M. ulcerans and BU disease would advance understanding of the disease’s ecology and burden, and could inform targeted surveillance.

Methodology/Principal Findings

We used previously compiled point-level datasets of BU and M. ulcerans occurrence, evidence for BU occurrence within national and sub-national areas, and diverse environmental datasets. 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 × 5km. Climate and atmospheric variables were the strongest predictors of both distributions, while indicators of human disturbance including damming and deforestation, drove local variation in suitability. 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 29.1 million.

Conclusions/Significance

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.

Author summary

Like many neglected tropical diseases primarily affecting the rural poor, Buruli ulcer (BU) is under-detected and under-reported within routine health information systems. As such, the burden and distribution are not fully known, impeding appropriate targeting of health resources, control, and care for people affected. Having previously evaluated and mapped the existing evidence for BU and its causative agent M. ulcerans, we concluded that the disease was likely to occur beyond the range of known endemic areas. However, we were left with the question of where exactly these undetected cases might be occurring. Answering this question required a more fine-scale approach: BU is highly focal, presumably due to local variation in the environmental factors which determine suitability for M. ulcerans survival and transmission to humans. We used the compiled evidence and geographical datasets to build statistical models representing the relationship between environmental factors and previously reported cases. This allowed us to define the ecological niche of BU, and subsequently to identify areas across Africa where this niche was met, providing suitable conditions for the disease. We constructed separate models of suitability for M. ulcerans, using locations where its DNA had been detected in environmental sources. Unsurprisingly, suitability for M. ulcerans was predicted to be wider than, but geographically overlapping with that for BU. This implies that beyond the conditions necessary for survival of the bacterium, additional factors are required for transmission to humans. The high-resolution suitability maps we present are intended to guide case search activities which may identify endemic areas beyond the known endemic range. Data on the true prevalence of BU from targeted case searches within predicted-suitable areas will also allow us to validate and refine the models, and potentially predict the actual probability of cases occurring within predicted suitable areas.