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

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.

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

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.

Precision mapping of snail habitat provides a powerful indicator of human schistosomiasis transmission.

Recently, the World Health Organization recognized that efforts to interrupt schistosomiasis transmission through mass drug administration have been ineffective in some regions; one of their new recommended strategies for global schistosomiasis control emphasizes targeting the freshwater snails that transmit schistosome parasites. We sought to identify robust indicators that would enable precision targeting of these snails. At the site of the world's largest recorded schistosomiasis epidemic-the Lower Senegal River Basin in Senegal-intensive sampling revealed positive relationships between intermediate host snails (abundance, density, and prevalence) and human urogenital schistosomiasis reinfection (prevalence and intensity in schoolchildren after drug administration). However, we also found that snail distributions were so patchy in space and time that obtaining useful data required effort that exceeds what is feasible in standard monitoring and control campaigns. Instead, we identified several environmental proxies that were more effective than snail variables for predicting human infection: the area covered by suitable snail habitat (i.e., floating, nonemergent vegetation), the percent cover by suitable snail habitat, and size of the water contact area. Unlike snail surveys, which require hundreds of person-hours per site to conduct, habitat coverage and site area can be quickly estimated with drone or satellite imagery. This, in turn, makes possible large-scale, high-resolution estimation of human urogenital schistosomiasis risk to support targeting of both mass drug administration and snail control efforts.

Sex-dependent interactions between leptin, wasting and humoral immunity in two ethnic communities of school-aged children differentially exposed to Schistosoma haematobium.

Background: Leptin is a nutritional hormone whose production is generally higher in females. We investigated how leptin is associated with sex dimorphism during urinary schistosomiasis in relation with wasting. Methods: A cross-sectional study was carried out in three villages in northern Senegal. Ninety-eight school-aged children belonging to the Fulani or Wolof villages were enrolled. We performed parasitic diagnosis and anthropometric measurement to evaluate nutritional status. We collected peripheral blood to determine the amount of circulating leptin and immunoglobulin G (IgG), IgG4 and IgE directed to soluble worm antigen preparation (SWAP). Results: The prevalence of Schistosoma haematobium infection was higher among boys regardless of ethnic group, but exposure to parasites did not exacerbate malnutrition. The greater ability of girls to produce leptin was not altered by schistosomiasis and was recovered in both ethnic groups. However, while the usual correlation between leptin and fat storage was preserved in Fulani girls, it was disrupted in Fulani boys, who displayed a remarkable susceptibility for wasting. Finally, we observed that leptin was negatively associated with the level of antibodies in Wolof boys. Conclusions: Leptin can be disconnected from body fat and may exert a sex-dependent influence on host immune response to S. haematobium infection in Senegalese children.