Theoretical Framework for Landscape-Level Vector Control: Integrating Remote Sensing, Population Genetics, and Epidemiological Modeling for Anopheles gambiae Management in the Lake Chad Basin, North East Nigeria

Landscape-level malaria vector control requires an integrated theoretical framework that accounts for vector population structure, habitat connectivity, and epidemiological heterogeneity simultaneously. The Lake Chad Basin in North East Nigeria presents a highly dynamic landscape where receding lake margins, expanding irrigation, and climate variability create continuously shifting vector breeding environments that no existing control framework adequately addresses. This study develops and empirically validates a landscape-level vector control framework integrating satellite remote sensing, population genetics, and vectorial capacity modeling for An. gambiae complex in the Lake Chad Basin. Multi-temporal Sentinel-2 imagery will map habitat suitability and change over a five-year period. Microsatellite and SNP genotyping of An. gambiae s.l. collected at 30 sites will characterize population genetic structure and estimate dispersal corridors. A spatially explicit stochastic model incorporating these data with entomological and climate inputs will simulate malaria transmission under alternative intervention scenarios. The framework will be validated against retrospective malaria incidence data from health facilities in Borno and Yobe states. This research makes an original theoretical contribution by demonstrating how genetic connectivity data can improve the spatial targeting efficiency of mass vector control operations. Keywords: landscape vector control, remote sensing, population genetics, malaria modeling, Lake Chad Basin