Dynamic vaccine prioritization via non-Markovian final-state optimization

Effective vaccine prioritization is critical for epidemic control, yet real outbreaks exhibit memory effects that inflate state space and make long-term prediction and optimization challenging. Many strategies are therefore tuned to short-term objectives and overlook indirect protection. We develop a general age-stratified non-Markovian epidemic model that captures memory dynamics and unifies diverse models through state aggregation. Here we map non-Markovian final states to an equivalent Markovian representation, enabling real-time direct prediction of long-term vaccination effects. Leveraging this mapping, we design a dynamic prioritization strategy that continually allocates doses to minimize the predicted long-term epidemic burden, explicitly balancing indirect transmission blocking with direct protection of important groups and outperforming static policies and short-term heuristics that target only immediate direct effects. We further identify the mechanism driving shifts in vaccine prioritization as the epidemic progresses and coverage accumulates, underscoring the importance of adaptive allocations. This study makes long-term prediction tractable in systems with memory and provides actionable guidance for optimal vaccine deployment. Vaccine prioritization must account for both direct protection and indirect transmission blocking. This study develops a non-Markovian epidemic framework that predicts final outbreak burden and dynamically allocates vaccines, showing when priority should shift between groups.