Reinforcement learning emerges as a powerful tool for optimizing disease control strategies and public health interventions during epidemics.

Reinforcement learning (RL) algorithms are being applied to optimize both non-pharmaceutical and pharmaceutical intervention strategies for controlling infectious disease spread

RL's adaptive nature allows it to handle dynamic disease systems and maximize long-term health outcomes while managing real-world constraints

Research publications on RL applications for epidemic response have surged, particularly following COVID-19, though comprehensive surveys on this topic remain limited

RL approaches assist public health sectors in preventing, controlling, and responding to infectious disease outbreaks through data-driven decision-making