Modelling the spatial distribution of mycetoma in Sudan.

Sudan ensemble models environmental modelling machine learning mycetoma

Journal

Transactions of the Royal Society of Tropical Medicine and Hygiene
ISSN: 1878-3503
Titre abrégé: Trans R Soc Trop Med Hyg
Pays: England
ID NLM: 7506129

Informations de publication

Date de publication:
01 10 2021
Historique:
received: 17 03 2021
revised: 18 04 2021
accepted: 26 04 2021
pubmed: 27 5 2021
medline: 30 1 2022
entrez: 26 5 2021
Statut: ppublish

Résumé

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. 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. 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. 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.

Sections du résumé

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.

Identifiants

pubmed: 34037803
pii: 6284823
doi: 10.1093/trstmh/trab076
pmc: PMC8486737
doi:

Substances chimiques

Soil 0

Types de publication

Journal Article Research Support, Non-U.S. Gov't

Langues

eng

Sous-ensembles de citation

IM

Pagination

1144-1152

Subventions

Organisme : Wellcome Trust
Pays : United Kingdom
Organisme : World Health Organization
ID : 001
Pays : International
Organisme : Wellcome Trust
ID : 201900/Z/16/Z
Pays : United Kingdom
Organisme : Department of Health
Pays : United Kingdom

Informations de copyright

© The Author(s) 2021. Published by Oxford University Press on behalf of Royal Society of Tropical Medicine and Hygiene.

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Auteurs

Rowa Hassan (R)

Mycetoma Research Centre, Soba University Hospital, University of Khartoum, Khartoum, 11111, Sudan.
Department of Global Health and Infection, Brighton and Sussex Medical School, Falmer, Brighton, BN1 9PX, UK.

Hope Simpson (H)

Department of Disease Control, London School of Hygiene and Tropical Medicine, London, WC1E 7HT, UK.

Jorge Cano (J)

Department of Disease Control, London School of Hygiene and Tropical Medicine, London, WC1E 7HT, UK.

Sahar Bakhiet (S)

Mycetoma Research Centre, Soba University Hospital, University of Khartoum, Khartoum, 11111, Sudan.

Eltayeb Ganawa (E)

Department of GIS and Cartography, Faculty of Geography and Environmental Sciences, University of Khartoum, Khartoum, 11111, Sudan.

Daniel Argaw (D)

World Health Organization, Control of Neglected Tropical Diseases, Innovative and Intensified Disease Management, Geneva, Switzerland.

Melanie J Newport (MJ)

Department of Global Health and Infection, Brighton and Sussex Medical School, Falmer, Brighton, BN1 9PX, UK.

Kebede Deribe (K)

Department of Global Health and Infection, Brighton and Sussex Medical School, Falmer, Brighton, BN1 9PX, UK.
School of Public Health, College of Health Sciences, Addis Ababa University, Addis Ababa, Ethiopia.

Ahmed Hassan Fahal (AH)

Mycetoma Research Centre, Soba University Hospital, University of Khartoum, Khartoum, 11111, Sudan.

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