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The development of water management infrastructures, such as dams and canals, are important components of society’s response to feed a growing human population and to fight climate change. Yet, these changes in land use can also increase the transmission risk for waterborne diseases. Transmission risk associated with artificial reservoirs has been extensively documented for schistosomiasis, a parasitic disease of poverty that infects more than 240 million people worldwide. Over 90% of these cases are in sub-Saharan Africa, a region that is being steadily reshaped by climate change. Controlling the parasite’s obligate intermediate host snail is key to reducing transmission of this disease. Using commercial aquaculture to farm marketable species which predate upon these snails in vulnerable regions can have multiple positive effects, including the improved socioeconomic and nutritional health of surrounding communities. Here the authors assessed the viability of using the aquaculture of snail predators to simultaneously control schistosomiasis infection rates while alleviating economic and/or nutritional poverty in endemic regions of sub-Saharan Africa. A PRISMA-based 6-step systematic methodology was used to explore the primary literature using the case study of Côte d’Ivoire and two native species of snail predator to make evidence-based conclusions on the viability of this method for controlling schistosomiasis. This detailed thematic examination of the literature concluded that using specific approaches and species, aquaculture could be effective in reducing economic poverty and chronic malnourishment along with high levels of schistosomiasis infection. More current species-specific aquaculture data and consumer survey data are, however, needed to determine the economic and logistical effectiveness of farming native snail predators in-country. These and other opportunities for future research are highlighted.
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This content will become publicly available on August 2, 2025
Species distribution modeling for disease ecology: A multi-scale case study for schistosomiasis host snails in Brazil
Species distribution models (SDMs) are increasingly popular tools for profiling disease risk in ecology, particularly for infectious diseases of public health importance that include an obligate non-human host in their transmission cycle. SDMs can create high-resolution maps of host distribution across geographical scales, reflecting baseline risk of disease. However, as SDM computational methods have rapidly expanded, there are many outstanding methodological questions. Here we address key questions about SDM application, using schistosomiasis risk in Brazil as a case study. Schistosomiasis is transmitted to humans through contact with the free-living infectious stage ofSchistosomaspp. parasites released from freshwater snails, the parasite’s obligate intermediate hosts. In this study, we compared snail SDM performance across machine learning (ML) approaches (MaxEnt, Random Forest, and Boosted Regression Trees), geographic extents (national, regional, and state), types of presence data (expert-collected and publicly-available), and snail species (Biomphalaria glabrata,B.straminea, andB.tenagophila). We used high-resolution (1km) climate, hydrology, land-use/land-cover (LULC), and soil property data to describe the snails’ ecological niche and evaluated models on multiple criteria. Although all ML approaches produced comparable spatially cross-validated performance metrics, their suitability maps showed major qualitative differences that required validation based on local expert knowledge. Additionally, our findings revealed varying importance of LULC and bioclimatic variables for different snail species at different spatial scales. Finally, we found that models using publicly-available data predicted snail distribution with comparable AUC values to models using expert-collected data. This work serves as an instructional guide to SDM methods that can be applied to a range of vector-borne and zoonotic diseases. In addition, it advances our understanding of the relevant environment and bioclimatic determinants of schistosomiasis risk in Brazil.
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- PAR ID:
- 10553251
- Editor(s):
- Dar, Kamran Shaukat
- Publisher / Repository:
- amran Shaukat Dar, The University of Newcastle, AUSTRALIA
- Date Published:
- Journal Name:
- PLOS Global Public Health
- Volume:
- 4
- Issue:
- 8
- ISSN:
- 2767-3375
- Page Range / eLocation ID:
- e0002224
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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