Integrative One Health Framework for Predicting and Preventing Zoonotic Disease Emergence at Livestock-Wildlife Interfaces in Nigeria: An Original Theoretical and Empirical Contribution

This dissertation develops and empirically validates an original integrative One Health framework for predicting and preventing zoonotic disease emergence at livestock-wildlife interface zones across Nigeria's six geopolitical zones. Existing One Health frameworks have been conceptualised at global and national levels without adequate operationalisation for the specific institutional, ecological, and epidemiological conditions of sub-Saharan African nations where zoonotic disease emergence risk is highest. The dissertation employs a four-phase methodology: systematic literature review of zoonotic emergence predictors in West Africa; longitudinal ecological and serological surveillance at 30 livestock-wildlife interface sites across all six zones over two years; network analysis of livestock movement and wildlife migration data; and stakeholder co-design of the original predictive framework with veterinary, public health, and wildlife management professionals. Primary data include 2,400 sera from livestock and wildlife, environmental metagenomic samples, and 90 key informant interviews. A novel risk stratification model is developed using machine learning applied to serological, ecological, and livestock movement predictors. Validation is conducted against historical outbreak data. The dissertation proposes the Nigerian Interface Emergence Risk Model (NIERM) as an original theoretical and empirical contribution that quantifies site-level emergence risk and identifies priority intervention leverage points. Policy implications include a nationally applicable zoonotic disease prevention protocol for interface management. Keywords: One Health, zoonotic emergence, livestock-wildlife interface, predictive model, Nigeria.