This study explores the vaccine prioritization strategy to reduce the overall burden of the pandemic when the supply is limited. Existing methods conduct macro-level or simplified micro-level vaccine distribution by assuming the homogeneous behavior within subgroup populations and lacking mobility dynamics integration. Directly applying these models for micro-level vaccine allocation leads to sub-optimal solutions due to the lack of behavioral-related details. To address the issue, we first incorporate the mobility heterogeneity in disease dynamics modeling and mimic the disease evolution process using a Trans-vaccine-SEIR model. Then we develop a novel deep reinforcement learning to seek the optimal vaccine allocation strategy for the high-degree spatial-temporal disease evolution system. The graph neural network is used to effectively capture the structural properties of the mobility contact network and extract the dynamic disease features. In our evaluation, the proposed framework reduces 7% - 10% of infections and deaths than the baseline strategies. Extensive evaluation shows that the proposed framework is robust to seek the optimal vaccine allocation with diverse mobility patterns in the micro-level disease evolution system. In particular, we find the optimal vaccine allocation strategy in the transit usage restriction scenario is significantly more effective than restricting cross-zone mobility for the top 10% age-based and income-based zones. These results provide valuable insights for areas with limited vaccines and low logistic efficacy.

翻译：本研究探索了在疫苗供应受限时，通过优先分配策略降低疫情总体负担的方法。现有方法假设子群体内行为同质且缺乏流动性动态整合，仅进行宏观或简化的微观层面疫苗分配。由于缺乏行为相关细节，直接将这些模型应用于微观层面疫苗分配会导致次优解。针对该问题，我们首先在疾病动力学建模中纳入流动性异质性，并利用跨疫苗-SEIR模型模拟疾病演化过程。随后，我们提出一种新型深度强化学习方法，为高维度时空疾病演化系统寻求最优疫苗分配策略。通过图神经网络有效捕获流动性接触网络的结构特性，并提取动态疾病特征。实验评估表明，相较于基线策略，所提框架可减少7%-10%的感染人数与死亡病例。大量实验验证了该框架在微观疾病演化系统中对不同流动性模式具有鲁棒性，能实现最优疫苗分配。特别值得注意的是，在交通出行限制情景下，针对年龄排名前10%区域与收入排名前10%区域，最优疫苗分配策略的效果显著优于跨区域流动性限制策略。这些结果为疫苗短缺且物流效率低下的区域提供了重要启示。