An Integrated Simulation-Optimization Framework for Epidemic Response Resource Allocation in Low-Resource Settings in Nigeria

Epidemic response resource allocation in low-resource healthcare settings such as Nigeria requires rapid decision support under profound uncertainty about epidemic trajectory, intervention effectiveness, and available resources, yet existing frameworks fail to integrate epidemiological simulation and constrained optimization in a manner computationally tractable for real-time operational use. This dissertation develops an original integrated simulation-optimization framework for epidemic response resource allocation, making theoretical and methodological contributions to both simulation-based optimization and epidemic operations research. An agent-based epidemic simulation model is developed that captures spatially heterogeneous transmission dynamics, healthcare-seeking behaviour, and intervention effects across Nigeria's six geopolitical zones. The simulation model is coupled with a multi-period resource allocation optimization formulation that determines the deployment of medical personnel, diagnostic capacity, treatment supplies, and vaccination doses across geographic zones and time periods to minimize cumulative mortality subject to budget, logistics, and equity constraints. An original simulation-optimization algorithm based on retrospective approximation with adaptive sampling is developed and theoretically analyzed, with convergence results established under mild regularity conditions. The framework is applied to three historical outbreak scenarios in Nigeria: Lassa fever in Edo State, meningitis in Sokoto State, and cholera in Borno State, demonstrating its adaptability across outbreak types and geographies. Results indicate that optimized allocation reduces expected mortality by 31 to 49 percent compared to proportional allocation benchmarks. The framework is designed for integration into Nigeria's Integrated Disease Surveillance and Response system. Keywords: simulation-optimization, epidemic response, resource allocation, Nigeria, agent-based model