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This study explores the vaccine prioritization strategy to reduce the overallburden of the pandemic when the supply is limited. Existing methods conductmacro-level or simplified micro-level vaccine distribution by assuming thehomogeneous behavior within subgroup populations and lacking mobility dynamicsintegration. Directly applying these models for micro-level vaccine allocationleads to sub-optimal solutions due to the lack of behavioral-related details.To address the issue, we first incorporate the mobility heterogeneity indisease dynamics modeling and mimic the disease evolution process using aTrans-vaccine-SEIR model. Then we develop a novel deep reinforcement learningto seek the optimal vaccine allocation strategy for the high-degreespatial-temporal disease evolution system. The graph neural network is used toeffectively capture the structural properties of the mobility contact networkand extract the dynamic disease features. In our evaluation, the proposedframework reduces 7% - 10% of infections and deaths than the baselinestrategies. Extensive evaluation shows that the proposed framework is robust toseek the optimal vaccine allocation with diverse mobility patterns in themicro-level disease evolution system. In particular, we find the optimalvaccine allocation strategy in the transit usage restriction scenario issignificantly more effective than restricting cross-zone mobility for the top10% age-based and income-based zones. These results provide valuable insightsfor areas with limited vaccines and low logistic efficacy.

Cooperating Graph Neural Networks with Deep Reinforcement Learning for Vaccine Prioritization