In today?s era of rapid environmental change, understanding the implications for infectious disease is a priority for both science and society. The aim of this project is to study environmental factors that promote a debilitating parasitic disease called schistosomiasis, or ?snail fever.? Schistosomiasis affects more than 200 million people worldwide, especially in sub-Saharan Africa. Recent research developed by this project team supports the hypothesis that disease transmission is higher near sites where people come into contact with natural water sources that have more habitat available for the aquatic snails that are hosts of the parasite. By studying local water circulation, combined with mapping and field environmental sampling at local disease transmission hotspots, this research will investigate how seasonal and year-to-year change in the habitat of disease-carrying snails affects disease risk for people. In collaboration with public health organizations in low-income countries, team members will use this knowledge to build a predictive mapping tool that measures schistosomiasis risk across large landscapes. The team will employ new technologies, such as satellite and drone imagery and artificial intelligence to make predictions. The disease risk mapping tool is intended to support public health decision-makers to better protect human health by efficiently distributing life-saving anti-parasitic medicine where it is needed most, and by employing well-designed environmental interventions that reduce the risk of environmental transmission from disease-carrying snails to people. This project will also support the training and professional development of underrepresented groups at the high school, undergraduate, graduate and postdoctoral levels, through direct involvement in research, intensive courses and international workshops.
It is well known that long-term control of schistosomiasis requires accurate prediction of the spatial distribution of freshwater intermediate snail hosts in rapidly changing ecosystems. Yet, standard techniques for monitoring these intermediate hosts are labor-intensive and time-consuming, and provide information limited to the small areas that are manually sampled. Consequently, in the low-income countries where schistosomiasis control is most needed, large-scale programs to fight this disease generally operate with little understanding of where transmission hotspots are, and what types of intervention are most effective. This project has three objectives: (1) to determine the spatial scale at which disease transmission occurs through microscale hydrological modelling and mapping of aquatic vegetation; (2) to develop a new generation of machine learning applications that use drone and satellite imagery to identify key habitat extent for snails of public health importance, and, ultimately, transmission hotspots at regional scales; and, (3) to integrate field data with precision mapping of habitat to parameterize mathematical network models of schistosomiasis dynamics, and use Optimal Control theory to identify combinations of cost-effective strategies for disease control. This project will provide a research framework and epidemiological models based on ecological theory to predict disease dynamics that can be used to respond to other diseases (e.g., vector-borne and water borne) with complex ecologies and environmental drivers.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
