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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. 

Abstract Climate drives population dynamics through multiple mechanisms, which can lead to seemingly context-dependent effects of climate on natural populations. For climate-sensitive diseases, such as dengue, chikungunya, and Zika, climate appears to have opposing effects in different contexts. Here we show that a model, parameterized with laboratory measured climate-driven mosquito physiology, captures three key epidemic characteristics across ecologically and culturally distinct settings in Ecuador and Kenya: the number, timing, and duration of outbreaks. The model generates a range of disease dynamics consistent with observed Aedes aegypti abundances and laboratory-confirmed arboviral incidence with variable accuracy (28–85% for vectors, 44–88% for incidence). The model predicted vector dynamics better in sites with a smaller proportion of young children in the population, lower mean temperature, and homes with piped water and made of cement. Models with limited calibration that robustly capture climate-virus relationships can help guide intervention efforts and climate change disease projections. 

Meet the world’s most wanted parasite: a mummified caterpillar with a fungus growing right out of its face. Even stranger: it costs three times its weight in gold! This super expensive fungus grows in the alpine regions of the Himalayas and Tibetan Plateau where it is cold and dry. Its promised health benefits include increased strength and cures for many diseases. Recently, it has become very popular around the world. Its price has increased and collectors have started harvesting more. But lately, some people have become concerned that the fungus populations are declining. We wanted to see if that is the case, and if so, why? So, we interviewed local harvesters and analyzed environmental models of the region. Our results showed that there is a decline in the caterpillar fungus populations, and the main causes are overharvesting and climate change. 

We consider two aspects of the human enterprise that profoundly affect the global environment: population and consumption. We show that fertility and consumption behavior harbor a class of externalities that have not been much noted in the literature. Both are driven in part by attitudes and preferences that are not egoistic but socially embedded; that is, each household’s decisions are influenced by the decisions made by others. In a famous paper, Garrett Hardin [G. Hardin, Science 162, 1243–1248 (1968)] drew attention to overpopulation and concluded that the solution lay in people “abandoning the freedom to breed.” That human attitudes and practices are socially embedded suggests that it is possible for people to reduce their fertility rates and consumption demands without experiencing a loss in wellbeing. We focus on fertility in sub-Saharan Africa and consumption in the rich world and argue that bottom-up social mechanisms rather than top-down government interventions are better placed to bring about those ecologically desirable changes.