Adaptive Multi-Echelon Epidemic–Supply Chain Optimization at National Scale

Motivated by systemic misalignments between vaccine supply, evolving disease burden, and public demand observed during COVID-19, this paper presents an adaptive epidemic resource allocation framework for multi-echelon, multi-period vaccine allocation. The framework integrates spatiotemporal disease dynamics, demand, and operational constraints into vaccination policy design, operationalizing supply chain viability through adaptive reallocation, mass-vaccination-center activation, and multi-echelon coordination. Rather than relying on probabilistic scenario modeling, infection transmission rates are externally estimated leveraging data-driven methods grounded in epidemic compartmental structure and validated using Seasonal Autoregressive Integrated Moving Average (SARIMA). Fairness is incorporated through max-min per-capita and knapsack-based formulations prioritizing regions based on socioeconomic vulnerability, healthcare access, and infection burden. Numerical results show that, unlike parity-based metrics, our approach balances equity and efficiency and improves campaign performance by reducing myopic decision-making. To address computational challenges, we develop two dynamic Benders-like decomposition algorithms that exploit temporal and spatial structure, achieving substantial gains over commercial solvers and near-exact benchmarks, with less than 1% optimality gap. Calibrated with US COVID-19 data, the framework is estimated to prevent 2 million infections and 30,000 deaths within six months and maintain viability under nationwide and local-temporal shocks through operational scalability, spatial substitution, and adaptive multi-echelon coordination.