Empowering Epidemic Response: The Role of Reinforcement Learning in Infectious Disease Control

A team of researchers led by Mutong Liu has published a concise, 8-page survey on arXiv (ID: 2603.25771) reviewing the emerging application of reinforcement learning (RL) for infectious disease control. The paper, titled 'Empowering Epidemic Response: The Role of Reinforcement Learning in Infectious Disease Control,' synthesizes rapidly growing literature, particularly from the COVID-19 era, that explores how RL agents can optimize complex, long-term intervention strategies. Unlike traditional models, RL frameworks can adapt to dynamic real-world systems and balance multiple, often competing objectives under various constraints.

The survey highlights several critical public health challenges where RL shows promise. These include optimizing the allocation of limited resources (like vaccines or hospital beds), strategically balancing non-pharmaceutical interventions (e.g., lockdowns) with economic 'livelihoods,' and coordinating mixed policies across multiple regions. The authors conclude by discussing future research directions, positioning RL as a powerful computational tool to assist public health sectors in designing more effective and adaptive responses to future epidemics. It's important to note this is a pre-print survey for academic discussion and not a peer-reviewed clinical guideline.