ArticleScientific reports2024
A machine learning approach for modeling the occurrence of the major intermediate hosts for schistosomiasis in East Africa.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 1 of them a synthesis that pooled it.
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Who cites it
8 citing papers in PubMed, 1 synthesis or guideline pooled it, 16 citations in OpenAlex.
- The use of artificial intelligence based modelling techniques in One Health-related infectious disease studies in Sub-Saharan Africa: a review.Frontiers in artificial intelligence · 2026Pooled it
- Pharos: a mobile GIS application for network assessment and ground-truth mapping to support mHealth study design in low-resource settings.International journal of geographical information science : IJGIS · 2026Article
- Future climate and demographic changes will almost double the risk of schistosomiasis transmission in the Lake Victoria Basin.One health (Amsterdam, Netherlands) · 2025Review
- Epidemiology and Current Challenges of Human Schistosomiasis Along the Shabelle River, Somali Regional State, Eastern Ethiopia.Acta parasitologica · 2025Article
- Risk Prediction of Emerging Sites Infested with Schistosome-Transmitting Oncomelania hupensis in Shanghai, China.The American journal of tropical medicine and hygiene · 2025Article
- Significant Progress in the Study of African Freshwater Snails Over the Past 260 Years.Ecology and evolution · 2025Review
- Schistosomiasis transmission: A machine learning analysis reveals the importance of agrochemicals on snail abundance in Rwanda.PLoS neglected tropical diseases · 2024Article
- Article
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Authors and funding
5 authors at 1 institution in 2 countries.
Funding
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 Biomphalaria, while Bulinus occurrence is additionally influenced by soil clay content and nitrogen concentration. Favorable climate conditions indicate a high prevalence of IHs in East Africa, while the intricate connection with geography might signify either dispersal limitations or environmental filtering. Predicted probabilities demonstrate non-linear patterns, with Bulinus being more likely to occur than Biomphalaria in the region. This study provides foundational framework insights for targeted schistosomiasis prevention and control strategies in the region, assisting health workers and policymakers in their efforts.
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Registered trials
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