📖 ABSTRACT/OVERVIEW
Malaria transmission in Nigeria's Middle Belt exhibits complex spatiotemporal heterogeneity driven by the interaction of landscape ecology, entomological factors, and socioeconomic determinants, yet integrative spatial epidemiological models at fine geographic resolution that jointly characterise these drivers remain absent from the Nigerian malaria control evidence base. This dissertation develops an original spatial epidemiological framework for modelling malaria transmission dynamics and entomological correlates in the Middle Belt states of Benue, Kogi, Niger, and Nassarawa, making theoretical and empirical contributions at the interface of geomatics, epidemiology, and landscape ecology. Remote sensing data from Sentinel-2, MODIS, SRTM, and CHIRPS rainfall products are processed to derive landscape ecology variables including permanent water body density, rice paddy distribution, vegetation phenology, and land surface temperature. Entomological sampling using CDC light traps at ninety spatially stratified sites provides Anopheles vector species composition and abundance data. A hierarchical Bayesian spatial model is developed to jointly estimate the relationships between environmental covariates, entomological abundance, and health facility-reported malaria incidence, explicitly modelling spatial autocorrelation and overdispersion. The dissertation introduces the Transmission Enabling Landscape Index as a novel composite remote sensing indicator calibrated against vector abundance field data. Projected transmission intensity maps under two IPCC SSP land use and climate scenarios are produced for 2040. The research framework provides a scalable methodology for malaria transmission modelling applicable across West African endemic zones. Keywords: spatial epidemiology, malaria, remote sensing, Middle Belt, Bayesian modelling.
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