A machine learning approach for modeling the occurrence of the major intermediate hosts for schistosomiasis in East Africa.

ABSTRACT: Schistosomiasis, a prevalent water-borne disease second only to malaria, significantly impacts impoverished rural communities, primarily in Sub-Saharan Africa where over 90% of the severely affected population resides. The disease, majorly caused by Schistosoma mansoni and S. haematobium parasites, relies on freshwater snails, specifically Biomphalaria and Bulinus species, as crucial intermediate host (IH) snails. Targeted snail control is advisable, however, there is still limited knowledge about the community structure of the two genera especially in East Africa. Utilizing a machine learning approach, we employed random forest to identify key features influencing the distribution of both IH snails in this region. Our results reveal geography and climate as primary factors for Biomphalari

SUBMITTER: Tabo Z 

PROVIDER: S-EPMC10881506 | biostudies-literature | 2024 Feb

REPOSITORIES: biostudies-literature