Species distribution modeling to predict tsetse fly (Glossina spp.) habitat suitability in Kenya

This study addresses the critical challenge of African animal trypanosomosis (AAT) and human African trypanosomosis (HAT) in Kenya, where tsetse fly infestations span 38 of 47 counties. To bridge existing entomological data gaps exacerbated by climate change and human activity, researchers utilized machine learning algorithms (RF, SVM, MaxEnt, and GLM) and ensemble modeling to map habitat suitability. By integrating satellite-derived environmental data with demographic indicators, the study found that approximately 26% of Kenya is suitable for the Glossina genus, while 9% specifically suits Glossina pallidipes. The findings reveal that habitat suitability is driven by vegetation density and soil moisture but limited by high temperatures (above 40°C) and higher elevations, providing a high-precision roadmap for more cost-effective disease