Towards a Theoretical Framework for Malaria Elimination in Nigeria: Integrating Transmission Heterogeneity, Intervention Synergies, and Health System Constraints

Achieving malaria elimination in Nigeria demands a coherent theoretical framework that transcends single-intervention modelling to capture the complex interaction of transmission heterogeneity, intervention synergies, and health system capacity constraints at sub-national resolution. This doctoral research develops an integrative theoretical framework for malaria elimination applicable to Nigeria's diverse epidemiological settings. A mixed-methods design spanning qualitative framework synthesis, secondary analysis of national malaria indicator survey datasets from 2018 to 2023, and stochastic agent-based modelling was employed. Geospatial analysis of entomological inoculation rate, bednet coverage, indoor residual spraying deployment, case management quality, and health facility density data was performed across all six geopolitical zones using Bayesian hierarchical models. Qualitative stakeholder interviews with national programme coordinators, state malaria officials, and international technical partners provided contextual validation of model assumptions. The framework synthesises epidemiological threshold theory with health system science and implementation science principles, proposing a multi-level causal pathway model linking structural health system determinants to programmatic outcome heterogeneity. Original contributions include a quantitative index of elimination readiness and a scenario simulation tool for predicting the minimum intervention package required for local elimination in different transmission intensity settings. Evidence from 2020 to 2024 highlights persistent subnational heterogeneity as the primary obstacle to Nigeria's elimination trajectory. Recommendations will be submitted to the National Malaria Elimination Programme for strategic planning. Keywords: malaria elimination framework, Nigeria, transmission heterogeneity, health system, agent-based modelling.