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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.
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Modeling how and why aquatic vegetation removal can free rural households from poverty-disease traps
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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- PAR ID:
- 10566728
- Publisher / Repository:
- Proceedings of the National Academy of Sciences
- Date Published:
- Journal Name:
- Proceedings of the National Academy of Sciences
- Volume:
- 121
- Issue:
- 52
- ISSN:
- 0027-8424
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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