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1. Human Schistosoma exposure risk in rice fields and an exploration of fish species for snail and schistosomiasis biocontrol

   https://doi.org/10.1371/journal.pgph.0004726  

   Sack, Alexandra; Selland, Emily; Bakhoum, Sidy; Seck, Momy; Jouanard, Nicolas; Magblenou, Louis Dossou; Rohr, Jason R (June 2025, PLOS Global Public Health)

   Broadhurst, Mara Jana (Ed.)

   Schistosomiasis is a devastating parasitic disease in which the infectious stage to humans is released by intermediate host snails. The Senegal River Basin (SRB) is a high-risk area for both urogenital and fecal human schistosomiasis and has extensive rice cultivation. However, occupational risk of schistosomiasis to people working in irrigated rice fields is not well established. We performed intermediate host snail surveys from 2022-2023 in rice fields and irrigation canals throughout the SRB. We discovered human schistosome-shedding snails in rice fields and adjacent irrigation canals during the rice growing and non-growing seasons, establishing a clear occupational exposure risk to rice farmers. Relative to the non-growing season, this risk was higher in the rice growing and harvest season when more people are in the rice fields. Rice-fish co-culturing might reduce this occupational risk to rice farmers if local fish species consume enough snail intermediate hosts to reduceSchistosomatransmission. Our predation trials revealed that localHeterotis niloticusandHemichromisspp. fish consumed significant numbers ofBiomphalaria pfeifferiandBulinusspp. snails, and separate trials revealed that these same snail species exhibited only moderate avoidance and refuge use responses to fish chemical cues. These results indicate that there is exposure toSchistosomaparasites in rice fields in the SRB and introducing local fish to rice fields has promise for reducing this exposure as well as providing a protein source to rice farming families. We encourage future studies to more fully explore the benefits of rice-fish co-culturing in the West Africa. 

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2. Identification of Snails and Schistosoma of Medical Importance via Convolutional Neural Networks: A Proof-of-Concept Application for Human Schistosomiasis

   https://doi.org/10.3389/fpubh.2021.642895  

   Tallam, Krti; Liu, Zac Yung-Chun; Chamberlin, Andrew J.; Jones, Isabel J.; Shome, Pretom; Riveau, Gilles; Ndione, Raphael A.; Bandagny, Lydie; Jouanard, Nicolas; Eck, Paul Van; et al (July 2021, Frontiers in Public Health)

   null (Ed.)

   In recent decades, computer vision has proven remarkably effective in addressing diverse issues in public health, from determining the diagnosis, prognosis, and treatment of diseases in humans to predicting infectious disease outbreaks. Here, we investigate whether convolutional neural networks (CNNs) can also demonstrate effectiveness in classifying the environmental stages of parasites of public health importance and their invertebrate hosts. We used schistosomiasis as a reference model. Schistosomiasis is a debilitating parasitic disease transmitted to humans via snail intermediate hosts. The parasite affects more than 200 million people in tropical and subtropical regions. We trained our CNN, a feed-forward neural network, on a limited dataset of 5,500 images of snails and 5,100 images of cercariae obtained from schistosomiasis transmission sites in the Senegal River Basin, a region in western Africa that is hyper-endemic for the disease. The image set included both images of two snail genera that are relevant to schistosomiasis transmission – that is, Bulinus spp. and Biomphalaria pfeifferi – as well as snail images that are non-component hosts for human schistosomiasis. Cercariae shed from Bi. pfeifferi and Bulinus spp. snails were classified into 11 categories, of which only two, S. haematobium and S. mansoni , are major etiological agents of human schistosomiasis. The algorithms, trained on 80% of the snail and parasite dataset, achieved 99% and 91% accuracy for snail and parasite classification, respectively, when used on the hold-out validation dataset – a performance comparable to that of experienced parasitologists. The promising results of this proof-of-concept study suggests that this CNN model, and potentially similar replicable models, have the potential to support the classification of snails and parasite of medical importance. In remote field settings where machine learning algorithms can be deployed on cost-effective and widely used mobile devices, such as smartphones, these models can be a valuable complement to laboratory identification by trained technicians. Future efforts must be dedicated to increasing dataset sizes for model training and validation, as well as testing these algorithms in diverse transmission settings and geographies. 

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3. Transmission potential of human schistosomes can be driven by resource competition among snail intermediate hosts

   https://doi.org/10.1073/pnas.2116512119  

   Civitello, David J.; Angelo, Teckla; Nguyen, Karena H.; Hartman, Rachel B.; Starkloff, Naima C.; Mahalila, Moses P.; Charles, Jenitha; Manrique, Andres; Delius, Bryan K.; Bradley, L. M.; et al (February 2022, Proceedings of the National Academy of Sciences)

   Predicting and disrupting transmission of human parasites from wildlife hosts or vectors remains challenging because ecological interactions can influence their epidemiological traits. Human schistosomes, parasitic flatworms that cycle between freshwater snails and humans, typify this challenge. Human exposure risk, given water contact, is driven by the production of free-living cercariae by snail populations. Conventional epidemiological models and management focus on the density of infected snails under the assumption that all snails are equally infectious. However, individual-level experiments contradict this assumption, showing increased production of schistosome cercariae with greater access to food resources. We built bioenergetics theory to predict how resource competition among snails drives the temporal dynamics of transmission potential to humans and tested these predictions with experimental epidemics and demonstrated consistency with field observations. This resource-explicit approach predicted an intense pulse of transmission potential when snail populations grow from low densities, i.e., when per capita access to resources is greatest, due to the resource-dependence of cercarial production. The experiment confirmed this prediction, identifying a strong effect of infected host size and the biomass of competitors on per capita cercarial production. A field survey of 109 waterbodies also found that per capita cercarial production decreased as competitor biomass increased. Further quantification of snail densities, sizes, cercarial production, and resources in diverse transmission sites is needed to assess the epidemiological importance of resource competition and support snail-based disruption of schistosome transmission. More broadly, this work illustrates how resource competition can sever the correspondence between infectious host density and transmission potential. 

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4. Species distribution modeling for disease ecology: A multi-scale case study for schistosomiasis host snails in Brazil

   https://doi.org/10.1371/journal.pgph.0002224  

   Singleton, Alyson L; Glidden, Caroline K; Chamberlin, Andrew J; Tuan, Roseli; Palasio, Raquel_G S; Pinter, Adriano; Caldeira, Roberta L; Mendonça, Cristiane_L F; Carvalho, Omar S; Monteiro, Miguel V; et al (August 2024, PLOS Global Public Health)

   Dar, Kamran Shaukat (Ed.)

   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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5. Visualization of schistosomiasis snail habitats using light unmanned aerial vehicles

   https://doi.org/10.4081/gh.2020.818  

   Chamberlin, Andrew J.; Jones, Isabel J.; Lund, Andrea J.; Jouanard, Nicolas; Riveau, Gilles; Ndione, Raphaël; Sokolow, Susanne H.; Wood, Chelsea L.; Lafferty, Kevin D.; De Leo, Giulio A. (November 2020, Geospatial Health)

   null (Ed.)

   Schistosomiasis, or “snail fever”, is a parasitic disease affecting over 200 million people worldwide. People become infected when exposed to water containing particular species of freshwater snails. Habitats for such snails can be mapped using lightweight, inexpensive and field-deployable consumer-grade Unmanned Aerial Vehicles (UAVs), also known as drones. Drones can obtain imagery in remote areas with poor satellite imagery. An unexpected outcome of using drones is public engagement. Whereas sampling snails exposes field technicians to infection risk and might disturb locals who are also using the water site, drones are novel and fun to watch, attracting crowds that can be educated about the infection risk. 

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