# Cooperating Graph Neural Networks with Deep Reinforcement Learning for Vaccine Prioritization

arXiv: https://arxiv.org/abs/2305.05163 · HTML: https://mlsanity.com/paper/2305.05163/

## 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 (most cited first, 6 of 6)

- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Neural Message Passing for Quantum Chemistry](https://mlsanity.com/paper/1704.01212/) (2017)
- [Fast Graph Representation Learning with PyTorch Geometric](https://mlsanity.com/paper/1903.02428/) (2019)
- [Graph Neural Networks: A Review of Methods and Applications](https://mlsanity.com/paper/1812.08434/) (2018)
- [Controlling Graph Dynamics with Reinforcement Learning and Graph Neural Networks](https://mlsanity.com/paper/2010.05313/) (2020)
- [Halting SARS-CoV-2 by Targeting High-Contact Individuals](https://mlsanity.com/paper/2005.08907/) (2020)

## Built on by (most cited first, 9 of 9)

- [Regret Analysis of Policy Gradient Algorithm for Infinite Horizon Average Reward Markov Decision Processes](https://mlsanity.com/paper/2309.01922/) (2023)
- [Improved Sample Complexity Analysis of Natural Policy Gradient Algorithm with General Parameterization for Infinite Horizon Discounted Reward Markov Decision Processes](https://mlsanity.com/paper/2310.11677/) (2023)
- [Order-Optimal Regret with Novel Policy Gradient Approaches in Infinite-Horizon Average Reward MDPs](https://mlsanity.com/paper/2404.02108/) (2024)
- [Learning General Parameterized Policies for Infinite Horizon Average Reward Constrained MDPs via Primal-Dual Policy Gradient Algorithm](https://mlsanity.com/paper/2402.02042/) (2024)
- [Global Convergence for Average Reward Constrained MDPs with Primal-Dual Actor Critic Algorithm](https://mlsanity.com/paper/2505.15138/) (2025)
- [Towards Global Optimality for Practical Average Reward Reinforcement Learning without Mixing Time Oracles](https://mlsanity.com/paper/2403.11925/) (2024)
- [Empowering Epidemic Response: The Role of Reinforcement Learning in Infectious Disease Control](https://mlsanity.com/paper/2603.25771/) (2026)
- [Match or Replay: Self Imitating Proximal Policy Optimization](https://mlsanity.com/paper/2603.27515/) (2026)
- [Network-Based Interventions for HIV Prevention via Cascade-Aware Suppression of Transmission](https://mlsanity.com/paper/2605.20218/) (2026)

## 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.