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.

中文翻译：

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具有行为反馈动态的流行病时空疫苗分配政策

受 COVID-19 大流行的推动，我们研究了公共卫生当局如何随着时间的推移将有限库存中的疫苗分配到不同的司法管辖区。我们提出了一种流行病学模型，其接触率随时间变化，由程式化的行为反馈机制确定，以反映多波传播动态。我们评估各种信息敏感分配政策（例如，与当地发生率成比例的分配）的绩效，作为广泛使用的按比例政策的替代方案。我们还在所提出的流行病学模型下获得了具有公平性和可实施的冻结期约束的优化分配策略。对于以我们的带有行为反馈的分区模型为代表的多波流行病的情况，我们发现没有任何替代政策能够比在广泛的行为参数设置中简单的按比例政策提供一致更有效的分配。事实上，在某些情况下，替代政策实际上可能导致分配效率低于按比例政策。因此，我们的结果支持这样的结论：广泛使用的按比例政策是合理的，因为它易于解释/实施，并且不会造成意想不到的不利影响。然而，如果政策制定者愿意投资基于数值优化的更有针对性的策略，那么所确定的优化策略是更有利的选择，因为它们可以更有效地分配疫苗。