Using climate and epidemiologic data to find schistosomiasis hotspots to target treatment
Schistosomiasis is a parasitic disease affecting more than 150 million people in low- and middle-income countries, and is well-documented to have key climate drivers that affect transmission. The disease spreads unevenly, with some communities experiencing far higher infection rates than others. These "hotspots" persist even after mass treatment campaigns, but identifying them requires expensive, labor-intensive monitoring. Researchers don't yet fully understand why some places become hotspots while others don't, or how climate and environmental conditions drive that pattern.
This project will use field data from an ongoing schistosomiasis study in Côte d'Ivoire to investigate how climate and ecological factors, including freshwater snail populations (which transmit the parasite), shape where and when hotspots emerge. The team will develop a predictive modeling tool to help national disease control programs identify high-risk areas more efficiently, and will gather feedback from program staff and policymakers to make the tool as practical as possible. The goal is to give health authorities in affected countries better, more affordable ways to target their resources — reducing the outsized burden this disease places on some of the world's most vulnerable communities.