Cooperating Graph Neural Networks with Deep Reinforcement Learning for
  Vaccine Prioritization
Lu Ling Washim Uddin Mondal Satish V Ukkusuri
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.