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1. Modeling how and why aquatic vegetation removal can free rural households from poverty-disease traps

   https://doi.org/10.1073/pnas.2411838121  

   Doruska, Molly_J; Barrett, Christopher_B; Rohr, Jason_R (December 2024, Proceedings of the National Academy of Sciences)

   Infectious disease can reduce labor productivity and incomes, trapping subpopulations in a vicious cycle of ill health and poverty. Efforts to boost African farmers’ agricultural production through fertilizer use can inadvertently promote the growth of aquatic vegetation that hosts disease vectors. Recent trials established that removing aquatic vegetation habitat for snail intermediate hosts reduces schistosomiasis infection rates in children, while converting the harvested vegetation into compost boosts agricultural productivity and incomes. We develop a bioeconomic model that interacts an analytical microeconomic model of agricultural households’ behavior, health status, and incomes over time with a dynamic model of schistosomiasis disease ecology. We calibrate the model with field data from northern Senegal. We show analytically and via simulation that local conversion of invasive aquatic vegetation to compost changes the feedback among interlinked disease, aquatic, and agricultural systems, reducing schistosomiasis infection and increasing incomes relative to the current status quo, in which villagers rarely remove aquatic vegetation. Aquatic vegetation removal disrupts the poverty-disease trap by reducing habitat for snails that vector the infectious helminth and by promoting the production of compost that returns to agricultural soils nutrients that currently leach into surface water from on-farm fertilizer applications. The result is healthier people, more productive labor, cleaner water, more productive agriculture, and higher incomes. Our model illustrates how this ecological intervention changes the feedback between the human and natural systems, potentially freeing rural households from poverty-disease traps. 

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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. 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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4. Risk of Human Exposure to the Intestinal Schistosome, Schistosoma mansoni, across Seasons along the Senegal River

   https://doi.org/10.24966/GHR-2566/100038  

   JR, Rohr (December 2021, Gastroenterology and Hepatology Research)

   Background: Schistosomiasis is an emerging disease associated with changes to the environment that have increased human contact rates with disease-causing parasites, flatworms that are released from freshwater snails. For example, schistosomiasis remains a major public health problem in Northern Senegal, where prevalence in schoolchildren often reaches 90%. Aim: This study focuses on the impact of seasonality on the risk of human exposure (RHE) to Schistosoma mansoni, defined as the total number of cercariae (the free-living life stage that infects humans) shed from all Biomphalaria pfeifferi snails collected at a site using standardized methods. We focus on RHE because it is rarely quantified and a recent study demonstrated that snails stop shedding cercariae when snail densities increase and thus per capita snail resources become limited [2], suggesting that densities of snails might not be directly proportional to RHE to schistosomes. Method: We sampled four water access points in three villages every other week during the early (Dry1) and later dry seasons (Dry2) and the rainy season, quantifying the abundance of infected and non-infected snail intermediate hosts, cercariae released per infected snail, and water chemistry. We used simple and multiple linear regressions to assess how seasonality and environmental parameters affect non-infected and infected snail abundance and RHE. Results: Although RHE was found across all seasons, the abundance of infected and non-infected snail intermediate hosts and cercariae, as well as prevalence (23.71%), were all highest in the rainy season. In the rainy season, RHE was positively associated with the density of snail hosts and their periphyton food resource. Conclusion: Although previous studies have examined the influence of seasonality on snail densities, few studies have explored the effects of seasonality on cercarial densities, which is the primary source of infection to humans. Our study demonstrates that cercarial densities are greater in the rainy season than in the early or late dry seasons. Given that cercarial densities directly pose risk of infection to humans, unlike non-infected or infected snails, these finding should help to inform decision making and schistosomiasis control efforts in West Africa. 

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5. 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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