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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. Schistosome infection in Senegal is associated with different spatial extents of risk and ecological drivers for Schistosoma haematobium and S. mansoni

   https://doi.org/10.1371/journal.pntd.0009712  

   Jones, Isabel J.; Sokolow, Susanne H.; Chamberlin, Andrew J.; Lund, Andrea J.; Jouanard, Nicolas; Bandagny, Lydie; Ndione, Raphaël; Senghor, Simon; Schacht, Anne-Marie; Riveau, Gilles; et al (September 2021, PLOS Neglected Tropical Diseases)

   Secor, W. Evan (Ed.)

   Schistosome parasites infect more than 200 million people annually, mostly in sub-Saharan Africa, where people may be co-infected with more than one species of the parasite. Infection risk for any single species is determined, in part, by the distribution of its obligate intermediate host snail. As the World Health Organization reprioritizes snail control to reduce the global burden of schistosomiasis, there is renewed importance in knowing when and where to target those efforts, which could vary by schistosome species. This study estimates factors associated with schistosomiasis risk in 16 villages located in the Senegal River Basin, a region hyperendemic for Schistosoma haematobium and S . mansoni . We first analyzed the spatial distributions of the two schistosomes’ intermediate host snails ( Bulinus spp. and Biomphalaria pfeifferi , respectively) at village water access sites. Then, we separately evaluated the relationships between human S . haematobium and S . mansoni infections and (i) the area of remotely-sensed snail habitat across spatial extents ranging from 1 to 120 m from shorelines, and (ii) water access site size and shape characteristics. We compared the influence of snail habitat across spatial extents because, while snail sampling is traditionally done near shorelines, we hypothesized that snails further from shore also contribute to infection risk. We found that, controlling for demographic variables, human risk for S . haematobium infection was positively correlated with snail habitat when snail habitat was measured over a much greater radius from shore (45 m to 120 m) than usual. S . haematobium risk was also associated with large, open water access sites. However, S . mansoni infection risk was associated with small, sheltered water access sites, and was not positively correlated with snail habitat at any spatial sampling radius. Our findings highlight the need to consider different ecological and environmental factors driving the transmission of each schistosome species in co-endemic landscapes. 

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3. 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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4. 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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5. Interventions can shift the thermal optimum for parasitic disease transmission

   https://doi.org/10.1073/pnas.2017537118  

   Nguyen, Karena H.; Boersch-Supan, Philipp H.; Hartman, Rachel B.; Mendiola, Sandra Y.; Harwood, Valerie J.; Civitello, David J.; Rohr, Jason R. (March 2021, Proceedings of the National Academy of Sciences)

   Temperature constrains the transmission of many pathogens. Interventions that target temperature-sensitive life stages, such as vector control measures that kill intermediate hosts, could shift the thermal optimum of transmission, thereby altering seasonal disease dynamics and rendering interventions less effective at certain times of the year and with global climate change. To test these hypotheses, we integrated an epidemiological model of schistosomiasis with empirically determined temperature-dependent traits of the human parasiteSchistosoma mansoniand its intermediate snail host (Biomphalariaspp.). We show that transmission risk peaks at 21.7 °C (T_opt), and simulated interventions targeting snails and free-living parasite larvae increasedT_optby up to 1.3 °C because intervention-related mortality overrode thermal constraints on transmission. ThisT_optshift suggests that snail control is more effective at lower temperatures, and global climate change will increase schistosomiasis risk in regions that move closer toT_opt. Considering regional transmission phenologies and timing of interventions when local conditions approachT_optwill maximize human health outcomes. 

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