Developing an Original One Health Framework for Predicting Zoonotic Spillover Risk at the Human-Wildlife Interface in Nigeria’s Middle Belt, North Central Zone

This dissertation develops and empirically validates an original One Health framework for quantifying and predicting zoonotic disease spillover risk at human-wildlife interfaces in Nigeria's Middle Belt, covering Benue, Kogi, Nasarawa, Niger, and Plateau States. Wildlife-to-human pathogen spillover is a primary driver of emerging infectious disease events, and the Middle Belt's mosaic of forest remnants, farmland, and pastoralist communities creates high-intensity contact zones between people, livestock, and wildlife. The dissertation employs a four-component methodology: systematic wildlife health surveillance using camera trap-linked pathogen sampling of wildlife and paired household serosurveys over 18 months at 30 sites; social contact network analysis quantifying human-livestock-wildlife interaction patterns using GPS time-matching wearables; ecological risk modelling integrating habitat connectivity, host community diversity, and contact rates; and machine learning-based integration of all data streams into a site-level spillover risk prediction model. An original Zoonotic Spillover Risk Index (ZSRI) is constructed and validated against documented spillover events from retrospective outbreak records. Bat coronavirus, Borrelia-related spirochaetes, and Orthohantavirus sequences previously undetected in Nigeria are recovered from wildlife samples, representing novel pathogen discoveries. ZSRI scores at five sites correctly predicted two new outbreak events recorded during the validation period. The framework is implemented as an open-access digital prediction tool for use by state-level public health and veterinary services. Keywords: One Health, zoonotic spillover, Middle Belt Nigeria, spillover risk index, emerging pathogens.