COVID-19 has resulted in the mathematical modelling community producing a wide range of analyses, forecasting and scenario modelling efforts. In our work, we propose a novel approach using Reinforcement Learning to answer the question: “What agent behaviours reduce the spread of COVID-19?”. We address this question by creating an agent-based simulation environment and modelling the environment as a multi-agent Markov Decision Process (MDP). By providing agents the freedom to select their actions and learn from their experiences, RL allows agents to learn behavioural policies that reduce the spread of COVID-19. These behaviours can be analyzed, conditional on demographic attributes with the hopes of providing a more granular analysis to inform public health policymakers. The learned agent behaviours are compared to multiple baselines, as well we introduce two non-compliant scenarios, namely the addition of wildcard agents and compliance attrition. We found that the RL model was able to considerably reduce the number of infections, hospitalizations and deaths in the population, even in the non-compliance model scenarios. From the learned agent behaviours, we suggest enhancing public education on self-isolating at home and highlight the importance of mitigation strategies in schools. We hope this work will influence a new wave of research in Canada using RL for public health crises, including pandemic control. © 2022, Springer Nature Switzerland AG.
Learning COVID-19 Mitigation Strategies Using Reinforcement Learning