mlsanityData to 14 Oct 2026

Cooperating Graph Neural Networks with Deep Reinforcement Learning for Vaccine Prioritization

arXiv 2305.05163 ↗First posted 9 May 2023q-bio.PE cs.AI cs.LGcited by 9

Not on the map. Why?

Abstract (arXiv)

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.

Key facts. Cited by 9 papers in the map (data through 14 Oct 2026). Builds on 6 of its 80 references that are in the map. First posted on arXiv 9 May 2023.

Builds on

6 of 80 references.

References · 6

  1. 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,361
  2. 2017Neural Message Passing for Quantum Chemistrycited by 3,487
  3. 2019Fast Graph Representation Learning with PyTorch Geometriccited by 2,434
  4. 2018Graph Neural Networks: A Review of Methods and Applicationscited by 1,576
  5. 2020Controlling Graph Dynamics with Reinforcement Learning and Graph Neural Networkscited by 30
  6. 2020Halting SARS-CoV-2 by Targeting High-Contact Individualscited by 2

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 2; 2024: 3; 2025: 1; 2026: 3.

9 papers.

  1. 2023Regret Analysis of Policy Gradient Algorithm for Infinite Horizon Average Reward Markov Decision Processescited by 26
  2. 2023Improved Sample Complexity Analysis of Natural Policy Gradient Algorithm with General Parameterization for Infinite Horizon Discounted Reward Markov Decision Processescited by 25
  3. 2024Order-Optimal Regret with Novel Policy Gradient Approaches in Infinite-Horizon Average Reward MDPscited by 14
  4. 2024Learning General Parameterized Policies for Infinite Horizon Average Reward Constrained MDPs via Primal-Dual Policy Gradient Algorithmcited by 9
  5. 2025Global Convergence for Average Reward Constrained MDPs with Primal-Dual Actor Critic Algorithmcited by 8
  6. 2024Towards Global Optimality for Practical Average Reward Reinforcement Learning without Mixing Time Oraclescited by 6
  7. 2026Empowering Epidemic Response: The Role of Reinforcement Learning in Infectious Disease Controlcited by 0
  8. 2026Match or Replay: Self Imitating Proximal Policy Optimizationcited by 0
  9. 2026Network-Based Interventions for HIV Prevention via Cascade-Aware Suppression of Transmissioncited by 0