Background: Mycetoma is a neglected tropical disease that is reported worldwide and Sudan has the highest reported number of mycetoma infections across the globe. The incidence, prevalence and burden of mycetoma globally are not precisely known and its risk factors remain largely unelucidated.
Methods: This study aimed to identify the environmental predictors of fungal and bacterial mycetoma in Sudan and to identify areas of the country where these niche predictors are met. Demographic and clinical data from confirmed mycetoma patients seen at the Mycetoma Research Centre from 1991 to 2018 were included in this study. Regression and machine learning techniques were used to model the relationships between mycetoma occurrence in Sudan and environmental predictors.
Results: The strongest predictors of mycetoma occurrence were aridity, proximity to water, low soil calcium and sodium concentrations and the distribution of various species of thorny trees. The models predicted the occurrence of eumycetoma and actinomycetoma in the central and southeastern states of Sudan and along the Nile river valley and its tributaries.
Conclusion: Our results showed that the risk of mycetoma in Sudan varies geographically and is linked to identifiable environmental risk factors. Suitability maps are intended to guide health authorities, academic institutes and organisations involved in planning national scale surveys for early case detection and management, leading to better patient treatment, prevention and control of mycetoma.
Keywords: Sudan; ensemble models; environmental modelling; machine learning; mycetoma.
© The Author(s) 2021. Published by Oxford University Press on behalf of Royal Society of Tropical Medicine and Hygiene.