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Schistosomiasis, a debilitating parasitic disease of poverty affecting more than 250 million people worldwide, is contracted upon contact with the larval form of the parasite, known as cercaria, emerging from infected freshwater snails, the obligate intermediate host of the parasite. Understanding how infectious larvae can be transported in rivers and irrigation canals is crucial to fine-tune environmental interventions targeting the parasite and its intermediate host. Specifically, lateral cavities along many tropical rivers act as water access points but can also entrap parasitic larvae and provide low-velocity environments for snail-supporting vegetation to flourish, creating potential areas of high schistosomiasis infection. In this paper, the circulation of larvae in a typical transmission site along the Lampsar River in Senegal is modeled under a range of wind and vegetation conditions to better understand how such environmental factors affect their transport. We found that wind direction has a large influence on the distribution and abundance of parasitic larvae at the water access point, whereas increasing wind speed scales velocities but does not affect flow patterns. The area of coverage of vegetation can significantly alter flow magnitudes and circulation patterns for the same wind speed and direction. Increasing vegetation coverage generally leads to an increase in larvae residence time in the side pond, but the relationship is non-monotonic with five regimes of residence time behavior based on vegetation patch radius. The results suggest that there is an optimal patch radius at which larvae residence time and velocity deviations within the side pond are maximized. 

In Africa, many kids get sick from tiny worms called Schistosoma. These worms can slow children’s growth and development; damage the liver, intestines, and bladder; and sometimes lead to cancer or even death. Schistosoma can keep communities poor by reducing people’s ability to work. Over 800 million people are at risk of infection. People get infected when they play or wash in water filled with certain plants and snails. These plants grow fast because fertilizer from farmers’ fields washes into the water when it rains. We found that removing these plants can reduce Schistosoma. Plants that are removed can be turned into food for animals, compost for farms, or gas for cooking and electricity. This solution helps protect kids from getting sick and can even help to slow climate change. By working together, communities can clean their waterbodies and create a healthier, happier future, which is a win-win for people and nature. 

Abstract Aedes aegyptimosquitos are the primary vector for dengue, chikungunya, and Zika viruses and tend to breed in small containers of water, with a propensity to breed in small piles of trash and abandoned tires. This study piloted the use of aerial imaging to map and classify potentialAe. aegyptibreeding sites with a specific focus on trash, including discarded tires. Aerial images of coastal and inland sites in Kenya were obtained using an unmanned aerial vehicle. Aerial images were reviewed for identification of trash and suspected trash mimics, followed by extensive community walk-throughs to identify trash types and mimics by description and ground photography. An expert panel reviewed aerial images and ground photos to develop a classification scheme and evaluate the advantages and disadvantages of aerial imaging versus walk-through trash mapping. A trash classification scheme was created based on trash density, surface area, potential for frequent disturbance, and overall likelihood of being a productiveAe. aegyptibreeding site. Aerial imaging offers a novel strategy to characterize, map, and quantify trash at risk of promotingAe. aegyptiproliferation, generating opportunities for further research on trash associations with disease and trash interventions. 

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. 

Background:Schistosomiasis is endemic throughout all regions of Côte d’Ivoire, however, species of the intermediate snail host vary across bioclimatic zones. Hence, a deeper knowledge of the influence of climatic on the life history traits of the intermediate snail host is crucial to understand the environmental determinants of schistosomiasis in a rapidly changing climate. The aim of this study was to run a common garden experiment to assess differences in survival, somatic growth and fecundity of bothBulinus truncatusandBiomphalaria pfeifferisnails collected in three different bioclimatic areas. Methods:A cross-sectional malacological survey was conducted in February 2021 in the south, center and north of Côte d’Ivoire. We sampled two populations ofB. truncatus, the intermediate host snail ofSchistosoma haematobium, from northern and central Côte d’Ivoire, and two populations ofBi.pfeifferi, the intermediate host snail forSchistosoma mansoni, from the southern and central regions. Snails collected at the human-water contact sites were brought in the laboratory where they reproduced. The first generation snails (G₁) for each population were reared under the same laboratory conditions, i.e., at 24°C–26°C, during 63 days (9 weeks), to estimate survival, growth, and fecundity. Results:We found that G₁Bulinussnails from the north population showed higher survival and growth rates during our study and higher number of eggs at first reproduction, compared to the ones from the central region. ForBi.pfeifferi, no significant difference in survival rate was observed between G₁snails from the southern and central populations, whereas those from the south exhibited higher growth rates and higher number of eggs per individual at first reproduction than G₁snails from the central population. Conclusion:Our study provides evidence for heterogeneity in snails’ life-history traits in response to temperature among the populations from the three climatic regions. Further experiments from multiple populations are needed to confirm that snails express traits under optimal conditions, can lead to expansion of their geographical range and hence an increase in the risk of schistosomiasis transmission. Transplantation experiments will be required to assess implications of the changing climate on snails persistence, distribution and abundance. 

The geographical range of schistosomiasis is affected by the ecology of schistosome parasites and their obligate host snails, including their response to temperature. Previous models predicted schistosomiasis’ thermal optimum at 21.7°C, which is not compatible with the temperature in sub-Saharan Africa (SSA) regions where schistosomiasis is hyperendemic. We performed an extensive literature search for empirical data on the effect of temperature on physiological and epidemiological parameters regulating the free-living stages ofS.mansoniandS.haematobiumand their obligate host snails, i.e.,Biomphalariaspp. andBulinusspp., respectively. We derived nonlinear thermal responses fitted on these data to parameterize a mechanistic, process-based model of schistosomiasis. We then re-cast the basic reproduction number and the prevalence of schistosome infection as functions of temperature. We found that the thermal optima for transmission ofS.mansoniandS.haematobiumrange between 23.1–27.3°C and 23.6–27.9°C (95% CI) respectively. We also found that the thermal optimum shifts toward higher temperatures as the human water contact rate increases with temperature. Our findings align with an extensive dataset of schistosomiasis prevalence in SSA. The refined nonlinear thermal-response model developed here suggests a more suitable current climate and a greater risk of increased transmission with future warming for more than half of the schistosomiasis suitable regions with mean annual temperature below the thermal optimum. 

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. 

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.