Explore the vast range of titles available.

Despite control efforts, human schistosomiasis remains prevalent throughout Africa, Asia, and South America. The global schistosomiasis burden has changed little since the new anthelmintic drug, praziquantel, promised widespread control. We evaluated large-scale schistosomiasis control attempts over the past century and across the globe by identifying factors that predict control program success: snail control (e.g., molluscicides or biological control), mass drug administrations (MDA) with praziquantel, or a combined strategy using both. For data, we compiled historical information on control tactics and their quantitative outcomes for all 83 countries and territories in which: (i) schistosomiasis was allegedly endemic during the 20th century, and (ii) schistosomiasis remains endemic, or (iii) schistosomiasis has been \"eliminated,\" or is \"no longer endemic,\" or transmission has been interrupted. Widespread snail control reduced prevalence by 92 ± 5% (N = 19) vs. 37 ± 7% (N = 29) for programs using little or no snail control. In addition, ecological, economic, and political factors contributed to schistosomiasis elimination. For instance, snail control was most common and widespread in wealthier countries and when control began earlier in the 20th century. Snail control has been the most effective way to reduce schistosomiasis prevalence. Despite evidence that snail control leads to long-term disease reduction and elimination, most current schistosomiasis control efforts emphasize MDA using praziquantel over snail control. Combining drug-based control programs with affordable snail control seems the best strategy for eliminating schistosomiasis.

Eliminating human parasitic disease often requires interrupting complex transmission pathways. Even when drugs to treat people are available, disease control can be difficult if the parasite can persist in nonhuman hosts. Here, we show that restoration of a natural predator of a parasite’s intermediate hosts may enhance drug-based schistosomiasis control. Our study site was the Senegal River Basin, where villagers suffered a massive outbreak and persistent epidemic after the 1986 completion of the Diama Dam. The dam blocked the annual migration of native river prawns (Macrobrachium vollenhoveni) that are voracious predators of the snail intermediate hosts for schistosomiasis. We tested schistosomiasis control by reintroduced river prawns in a before-after-control-impact field experiment that tracked parasitism in snails and people at two matched villages after prawns were stocked at one village’s river access point. The abundance of infected snails was 80% lower at that village, presumably because prawn predation reduced the abundance and average life span of latently infected snails. As expected from a reduction in infected snails, human schistosomiasis prevalence was 18 ± 5% lower and egg burden was 50 ± 8% lower at the prawn-stocking village compared with the control village. In a mathematical model of the system, stocking prawns, coupled with infrequent mass drug treatment, eliminates schistosomiasis from high-transmission sites. We conclude that restoring river prawns could be a novel contribution to controlling, or eliminating, schistosomiasis.

Schistosomiasis is a neglected tropical disease caused by Schistosoma parasites. Schistosoma are obligate parasites of freshwater Biomphalaria and Bulinus snails, thus controlling snail populations is critical to reducing transmission risk. As snails are sensitive to environmental conditions, we expect their distribution is significantly impacted by global change. Here, we used machine learning, remote sensing, and 30 years of snail occurrence records to map the historical and current distribution of forward-transmitting Biomphalaria hosts throughout Brazil. We identified key features influencing the distribution of suitable habitat and determined how Biomphalaria habitat has changed with climate and urbanization over the last three decades. Our models show that climate change has driven broad shifts in snail host range, whereas expansion of urban and peri-urban areas has driven localized increases in habitat suitability. Elucidating change in Biomphalaria distribution—while accounting for non-linearities that are difficult to detect from local case studies—can help inform schistosomiasis control strategies. Schistosomiasis is an emerging urban and peri-urban disease in Brazil and freshwater snails are an obligate host of the causative parasite. Here, the authors investigate the ecological suitability for the three freshwater snail hosts in Brazil and identify changes over time driven by climate and urbanisation.

Schistosomiasis is a snail-borne parasitic disease that ranks among the most important water-based diseases of humans in developing countries. Increased prevalence and spread of human schistosomiasis to non-endemic areas has been consistently linked with water resource management related to agricultural expansion. However, the role of agrochemical pollution in human schistosome transmission remains unexplored, despite strong evidence of agrochemicals increasing snail-borne diseases of wildlife and a projected 2- to 5-fold increase in global agrochemical use by 2050. Using a field mesocosm experiment, we show that environmentally relevant concentrations of fertilizer, a herbicide, and an insecticide, individually and as mixtures, increase densities of schistosome-infected snails by increasing the algae snails eat and decreasing densities of snail predators. Epidemiological models indicate that these agrochemical effects can increase transmission of schistosomes. Identifying agricultural practices or agrochemicals that minimize disease risk will be critical to meeting growing food demands while improving human wellbeing. Agrochemicals can affect the life cycle of human parasites in unexpected ways. Here, Halstead et al. show in mesocosm experiments that agrochemicals increase the density of snails hosting schistosome parasites, and modeling analysis suggests this could lead to increased risk of human schistosomiasis.

Schistosomiasis is a parasitic infection that is widespread in sub-Saharan Africa, where it represents a major health problem. We study the drivers of its geographical distribution in Senegal via a spatially explicit network model accounting for epidemiological dynamics driven by local socioeconomic and environmental conditions, and human mobility. The model is parameterized by tapping several available geodatabases and a large dataset of mobile phone traces. It reliably reproduces the observed spatial patterns of regional schistosomiasis prevalence throughout the country, provided that spatial heterogeneity and human mobility are suitably accounted for. Specifically, a fine-grained description of the socioeconomic and environmental heterogeneities involved in local disease transmission is crucial to capturing the spatial variability of disease prevalence, while the inclusion of human mobility significantly improves the explanatory power of the model. Concerning human movement, we find that moderate mobility may reduce disease prevalence, whereas either high or low mobility may result in increased prevalence of infection. The effects of control strategies based on exposure and contamination reduction via improved access to safe water or educational campaigns are also analyzed. To our knowledge, this represents the first application of an integrative schistosomiasis transmission model at a whole-country scale.

Despite awareness that disease emergence may be related to ecological change, few studies have rigorously analyzed the underlying environmental drivers of the dynamics of disease emergence. This may be due to the fact that ecological change and disease emergence are often mediated through complex and large‐scale processes that are not amenable to traditional reductionist approaches to causal inference. Here, we suggest strategies assembled from diverse disciplines, including ecology, epidemiology, and the social sciences, to analyze complex relationships, promote cooperation, increase efficiency, and minimize bias when investigating the ecological drivers of disease emergence. These techniques, which complement traditional hypothesis testing, include epidemiologic causal criteria, strong inference, causal diagrams, model selection, and triangulation. We also present several examples from recent emerging infectious disease investigations, including Hendra virus, Nipah virus, coral diseases, and avian influenza, where these techniques were successfully applied. Here, we outline some of the barriers to advancing our understanding of causation in disease ecology and offer some solutions for investigating large‐scale ecological drivers, such as global warming, pollution, and land‐use change.

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.

Background Schistosomiasis is responsible for the second highest burden of disease among neglected tropical diseases globally, with over 90 percent of cases occurring in African regions where drugs to treat the disease are only sporadically available. Additionally, human re-infection after treatment can be a problem where there are high numbers of infected snails in the environment. Recent experiments indicate that aquatic factors, including plants, nutrients, or predators, can influence snail abundance and parasite production within infected snails, both components of human risk. This study investigated how snail host abundance and release of cercariae (the free swimming stage infective to humans) varies at water access sites in an endemic region in Senegal, a setting where human schistosomiasis prevalence is among the highest globally. Methods/Principal findings We collected snail intermediate hosts at 15 random points stratified by three habitat types at 36 water access sites, and counted cercarial production by each snail after transfer to the laboratory on the same day. We found that aquatic vegetation was positively associated with per-capita cercarial release by snails, probably because macrophytes harbor periphyton resources that snails feed upon, and well-fed snails tend to produce more parasites. In contrast, the abundance of aquatic macroinvertebrate snail predators was negatively associated with per-capita cercarial release by snails, probably because of several potential sublethal effects on snails or snail infection, despite a positive association between snail predators and total snail numbers at a site, possibly due to shared habitat usage or prey tracking by the predators. Thus, complex bottom-up and top-down ecological effects in this region plausibly influence the snail shedding rate and thus, total local density of schistosome cercariae. Conclusions/Significance Our study suggests that aquatic macrophytes and snail predators can influence per-capita cercarial production and total abundance of snails. Thus, snail control efforts might benefit by targeting specific snail habitats where parasite production is greatest. In conclusion, a better understanding of top-down and bottom-up ecological factors that regulate densities of cercarial release by snails, rather than solely snail densities or snail infection prevalence, might facilitate improved schistosomiasis control.

Accurate mapping of forest canopy height is essential for monitoring forest structure, assessing biodiversity, and informing sustainable management practices. However, obtaining high-resolution canopy height data across large tropical landscapes remains challenging and prohibitively expensive. While machine learning approaches like Random Forest have become standard for predicting forest attributes from remote sensing data, deep learning methods remain underexplored for canopy height mapping despite their potential advantages. To address this limitation, we developed a rapid, automatic, scalable, and cost-efficient deep learning framework that predicts tree canopy height at fine-grained resolution (30 × 30 m) across Indonesian Borneo’s tropical forests. Our approach integrates diverse remote sensing data, including Landsat-8, Sentinel-1, land cover classifications, digital elevation models, and NASA Carbon Monitoring System airborne LiDAR, along with derived vegetation indices, texture metrics, and climatic variables. This comprehensive data pipeline produced over 300 features from approximately 2 million observations across Bornean forests. Using LiDAR-derived canopy height measurements from ~100,000 ha as training data, we systematically compared multiple machine learning approaches and found that our neural network model achieved canopy height predictions with R2 of 0.82 and RMSE of 4.98 m, substantially outperforming traditional machine learning approaches such as Random Forest (R2 of 0.57–0.59). The model performed particularly well for forests with canopy heights between 10–40 m, though systematic biases were observed at the extremes of the height distribution. This framework demonstrates how freely available satellite data can be leveraged to extend the utility of limited LiDAR coverage, enabling cost-effective forest structure monitoring across vast tropical landscapes. The approach can be adapted to other forest regions worldwide, supporting applications in ecological research, conservation planning, and forest loss mitigation.

Schistosomiasis is a debilitating parasitic disease of poverty that affects more than 200 million people worldwide, mostly in sub-Saharan Africa, and is clearly associated with the construction of dams and water resource management infrastructure in tropical and subtropical areas. Changes to hydrology and salinity linked to water infrastructure development may create conditions favorable to the aquatic vegetation that is suitable habitat for the intermediate snail hosts of schistosome parasites. With thousands of small and large water reservoirs, irrigation canals, and dams developed or under construction in Africa, it is crucial to accurately assess the spatial distribution of high-risk environments that are habitat for freshwater snail intermediate hosts of schistosomiasis in rapidly changing ecosystems. Yet, standard techniques for monitoring snails are labor-intensive, time-consuming, and provide information limited to the small areas that can be manually sampled. Consequently, in low-income countries where schistosomiasis control is most needed, there are formidable challenges to identifying potential transmission hotspots for targeted medical and environmental interventions. In this study, we developed a new framework to map the spatial distribution of suitable snail habitat across large spatial scales in the Senegal River Basin by integrating satellite data, high-definition, low-cost drone imagery, and an artificial intelligence (AI)-powered computer vision technique called semantic segmentation. A deep learning model (U-Net) was built to automatically analyze high-resolution satellite imagery to produce segmentation maps of aquatic vegetation, with a fast and robust generalized prediction that proved more accurate than a more commonly used random forest approach. Accurate and up-to-date knowledge of areas at highest risk for disease transmission can increase the effectiveness of control interventions by targeting habitat of disease-carrying snails. With the deployment of this new framework, local governments or health actors might better target environmental interventions to where and when they are most needed in an integrated effort to reach the goal of schistosomiasis elimination.