RL for Stochastic Vaccine Allocation on Contact Networks

Mar 17, 2025 · 1 min read

Bridged deterministic optimal control and reinforcement learning to develop a stochastic vaccine allocation strategy on individual-level contact networks, enabling robust pandemic response modeling.

Highlights

  • Modeled epidemic spread using a high-dimensional continuous-time Markov process (CTMP) on a contact graph.
  • Designed a vaccination policy using policy gradient-based RL, warm-started from a mean-field ODE solution.
  • Evaluated policies on metrics like mortality and hospitalizations across synthetic and real-world network topologies.

Tools

Python, PyTorch, NetworkX, OpenAI Gym

Reinforcement Learning Reinforcement Learning Epidemic Modeling Epidemic Modeling Networked Systems Networked Systems
占俊飞
Authors
占俊飞
Master Student