Spatial Distribution and Ecological Niche Modelling of Schistosoma Haematobium Bilharz and Schistosoma Mansoni Sambo: Assessment of Risk Maps for Schistosomiasis Spread in Benin (West Africa)

Emmanuel  Schadrac Todo; Rock  Yves Aikpon; Kourouma  Koura; Antoine  Salomon Lokossou; Augustin  Aoudji; Jean Cossi  Ganglo

Emmanuel Schadrac Todo Master Biodiversity Informatics Program, Faculty of Agricultural Sciences, University of Abomey-Calavi, Bénin.
Rock Yves Aikpon 1. Centre de Recherche Entomologique de Cotonou (CREC), Littoral, Benin. 2. Université Nationale des Sciences, Technologies, Ingénieries et Mathématiques (UNSTIM), Abomey, Bénin.
Kourouma Koura Laboratoire des Sciences Forestières (LSF), University of Abomey-Calavi, Atlantique, Benin.
Antoine Salomon Lokossou 1. Centre de Recherche Entomologique de Cotonou (CREC), Littoral, Benin. 2. Université Nationale des Sciences, Technologies, Ingénieries et Mathématiques (UNSTIM), Abomey, Bénin.
Augustin Aoudji 1.Master Biodiversity Informatics Program, Faculty of Agricultural Sciences, University of Abomey-Laboratoire des Sciences Forestières (LSF), University of Abomey-Calavi, Atlantique, Benin., Bénin. 2.
Jean Cossi Ganglo 1.Master Biodiversity Informatics Program, Faculty of Agricultural Sciences, University of Abomey-Laboratoire des Sciences Forestières (LSF), University of Abomey-Calavi, Atlantique, Benin., Bénin. 2. des Sciences Forestières (LSF), University of Abomey-Calavi, Atlantique, Benin

Keywords: Schistosomiasis, Mollusc Vectors, Spatial Distribution, Modelling, Benin

Abstract

Introduction: Schistosomiasis is an infection caused by a parasite that remains silent for a long period of time and induces severe complications. Schistosoma haematobium and Schistosoma mansoni are the incriminating agents with freshwater gastropod molluscs as the disease vectors. However, it is useful to have an idea of the potential vector populations in order to model their spatial distributions and ecological niches to anticipate and avoid the disease spread in risk areas.
Methods: In order to map the potential for schistosomiasis transmission in the present and future, pathogen and vector occurrence data were collected from the Global Biodiversity Information Facility (GBIF) website, literature and fieldwork in high disease endemicity areas. Also, occurrences were processed and environmental data were downloaded to model the spatial distribution and ecological niche of the disease using various algorithms (Maxent, BRT, and GLM).
Results: A total of four disease vectors (Bulinus globosus, Bulinus truncatus, Bulinus forskalii, and Biomphalaria pfeifferi) were considered in our study. As per the different models used, almost all of Benin is exposed to the disease at present, except the Alibori department which has low-risk areas. The future projection indicates that the northern and central departments of the country are likely to present much more favourable conditions for the disease.
Conclusion: Our results call for rigorous monitoring and surveillance in the departments of Alibori, Atacora, Borgou, and Danga to limit the potential expansion of schistosomiasis in Benin.

How to cite this article:
Todo ES, Aikpon RY, Koura K, Lokossou AS, Aoudji A, Ganglo JC. Spatial Distribution and Ecological Niche Modelling of Schistosoma haematobium Bilharz and Schistosoma mansoni Sambo: Assessment of Risk Maps for Schistosomiasis pread in Benin (West Africa). J Commun Dis. 2023;55(3):48-56.

DOI: https://doi.org/10.24321/0019.5138.202337

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