Optimal Capacity-Constrained COVID-19 Vaccination for Heterogeneous Populations
Optimal Capacity-Constrained COVID-19 Vaccination for Heterogeneous Populations
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DOI：
10.1109/cdc51059.2022.9992682
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发表时间：
2022-12
期刊：
影响因子：
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通讯作者：
R. Arghal;S. S. Bidokhti-S.;S. Sarkar
中科院分区：
文献类型：
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作者：
R. Arghal;S. S. Bidokhti-S.;S. Sarkar
新冠肺炎和随之而来的疫苗能力限制强调了在疫苗推出过程中适当区分优先顺序的重要性。风险级别、接触率和网络拓扑的异质性使这一问题变得复杂起来，这可能会极大地和非直观地改变疫苗接种的效果，在分配资源时必须考虑到这一点。本文提出了一个通用模型来捕捉广泛的网络异构性，同时保持计算的可处理性，并将疫苗优先排序作为一个最优控制问题。庞特里亚金的最大值原理被用来推导最优的、潜在的高度动态的分配策略的性质，提供了候选策略集合中的显著减少。新冠肺炎疫苗接种的大量数值模拟被用来证实这些发现和进一步的非法最优政策特征以及各种系统、疾病和人口参数的影响。
COVID-19 and the ensuing vaccine capacity constraints have emphasized the importance of proper prioritization during vaccine rollout. This problem is complicated by heterogeneity in risk levels, contact rates, and network topology which can dramatically and unintuitively change the efficacy of vaccination and must be taken into account when allocating resources. This paper proposes a general model to capture a wide array of network heterogeneity while maintaining computational tractability and formulates vaccine prioritization as an optimal control problem. Pontryagin’s Maximum Principle is used to derive properties of optimal, potentially highly dynamic, allocation policies, providing significant reductions in the set of candidate policies. Extensive numerical simulations of COVID-19 vaccination are used to corroborate these findings and further illicit optimal policy characteristics and the effects of various system, disease, and population parameters.