SIR-RL: Reinforcement Learning for Optimized Policy Control during
  Epidemiological Outbreaks in Emerging Market and Developing Economies
Maeghal Jain Ziya Uddin Wubshet Ibrahim
The outbreak of COVID-19 has highlighted the intricate interplay between
public health and economic stability on a global scale. This study proposes a
novel reinforcement learning framework designed to optimize health and economic
outcomes during pandemics. The framework leverages the SIR model, integrating
both lockdown measures (via a stringency index) and vaccination strategies to
simulate disease dynamics. The stringency index, indicative of the severity of
lockdown measures, influences both the spread of the disease and the economic
health of a country. Developing nations, which bear a disproportionate economic
burden under stringent lockdowns, are the primary focus of our study. By
implementing reinforcement learning, we aim to optimize governmental responses
and strike a balance between the competing costs associated with public health
and economic stability. This approach also enhances transparency in
governmental decision-making by establishing a well-defined reward function for
the reinforcement learning agent. In essence, this study introduces an
innovative and ethical strategy to navigate the challenge of balancing public
health and economic stability amidst infectious disease outbreaks.