Spatially explicit sampling frameworks to identify regions of increased mosquito abundance
Vector control interventions often lack comprehensive information on vector population distribution and dynamics. This knowledge gap poses challenges in targeting interventions effectively, especially in areas with heterogeneous transmission and where complementary vector control tools may be required to achieve sustained impact on disease transmission. In this study, we implemented a spatially explicit sampling framework for improved vector surveillance in coastal Kenya. Our stratified lattice with close pair sampling design aimed to characterise the vector dynamics of the primary malaria-transmitting species in the area and assess the ecotype classifications effectiveness at identifying clear population patterns. The study collected 3,621 mosquitoes, with An. funestus s.l. being the most abundant malaria vector. The inclusion of the ecotype classification significantly improved spatial abundance model estimates for An. gambiae and Culex spp. Wetlands, topographic wetness index, and proximity to rivers were associated with increased mosquito abundance. Spatial modelling revealed high abundance regions near the Galana-Sabaki River. Our study demonstrates the applicability of a reproducible spatial sampling approach to identify areas with high vector abundance and inform targeted vector control strategies. The study highlights the importance of ecological stratification and a spatial explicit sampling approach for predicting mosquito presence when prior data is limited and underscores the potential for refining future sampling for control efforts.