Spatiotemporal vaccine allocation policies for epidemics with behavioral feedback dynamics

Motivated by the COVID-19 pandemic, we study how a public health authority may allocate vaccines from a limited stockpile to different jurisdictions over time. We propose an epidemiological model with time-varying contact rates determined by a stylized behavioral feedback mechanism to reflect multi-wave transmission dynamics. We evaluate the performance of various information-sensitive allocation policies (e.g., allocation proportional to local incidence) as alternatives to the widely used pro-rata policy. We also obtain optimized allocation strategies under the proposed epidemiological model with fairness and implementable freeze-period constraints. For the case of a multi-wave epidemic as represented by our compartmental model with behavioral feedback, we find that none of the alternative policies offers consistently more efficient allocations than a simple pro-rata policy across a broad range of behavioral parameter settings. In fact, in some cases the alternative policies may actually result in less efficient allocations than the pro-rata policy. Thus our results support the conclusion that the widely used pro-rata policy can be well justified because it is simple to explain/implement and does not cause unexpected adverse effects. However, if policy makers are willing to invest in more tailored strategies based on numerical optimization, then the identified optimized strategies are a more favorable option as they allow for a more efficient allocation of vaccines.