Mapping the potential risk of mycetoma infection in Sudan and South Sudan using ecological niche modeling

Abstract

In 2013, the World Health Organization (WHO) recognized mycetoma as one of the neglected tropical conditions due to the efforts of the mycetoma consortium. This same consortium formulated knowledge gaps that require further research. One of these gaps was that very few data are available on the epidemiology and transmission cycle of the causative agents. Previous work suggested a soil-borne or Acacia thorn-prick-mediated origin of mycetoma infections, but no studies have investigated effects of soil type and Acacia geographic distribution on mycetoma case distributions. Here, we map risk of mycetoma infection across Sudan and South Sudan using ecological niche modeling (ENM). For this study, records of mycetoma cases were obtained from the scientific literature and GIDEON; Acacia records were obtained from the Global Biodiversity Information Facility. We developed ENMs based on digital GIS data layers summarizing soil characteristics, land-surface temperature, and greenness indices to provide a rich picture of environmental variation across Sudan and South Sudan. ENMs were calibrated in known endemic districts and transferred countrywide; model results suggested that risk is greatest in an east-west belt across central Sudan. Visualizing ENMs in environmental dimensions, mycetoma occurs under diverse environmental conditions. We compared niches of mycetoma and Acacia trees, and could not reject the null hypothesis of niche similarity. This study revealed contributions of different environmental factors to mycetoma infection risk, identified suitable environments and regions for transmission, signaled a potential mycetoma-Acacia association, and provided steps towards a robust risk map for the disease.

Figure 1. Geographic distribution of mycetoma cases and Acacia trees across Sudan and South Sudan (crosses and dotted circles, respectively).

Some areas across the region (in white) were not included in some analyses for lack of data on soil characteristics.

Figure 2. Potential mycetoma distribution based on occurrences in endemic districts.

Potential distributions of mycetoma were based on different environmental variables; models were calibrated in mycetoma-endemic districts, and transferred across all of Sudan and South Sudan. White areas have no soils data, and therefore have no model predictions.

Figure 3. Visualization of mycetoma ecological niches (i.e., the set of environmental values under which the species can potentially maintain populations) in two-dimensional environmental spaces based on different environmental variables.

The diagram shows the entire environmental availability across Sudan and South Sudan (light gray color), and conditions identified as suitable across Sudan and South Sudan (black color) and across endemic districts (pink).

Figure 4. Background similarity test of similarity between mycetoma and Acacia ecological niches across Sudan and South Sudan.

Niche overlap values were based on Hellinger's I, and Schoener's D metrics of similarity. Observed values are shown as black line with a blue arrow; null distribution is shown as a histogram.

Figure 5. Coincidence between ecological niche model predictions based on LST, NDVI, soils, and Acacia (the latter based on LST and NDVI only) with the independent additional case data from the Mycetoma Research Center.