Evolving Epidemic Management Rules Using Deep Neuroevolution: A Novel Approach to Inspection Scheduling and Outbreak Minimization

In epidemic management, the unpredictable dynamics of outbreaks, the constraints of available resources, and the complexity of variable interactions pose a significant challenge in designing effective management rules. While traditional mathematical models offer insights into the spread of the outbreaks, they are not effective due to the lack of available information on the outbreaks. Moreover, they are valuable for the evaluation of different management rules but do not provide management rules. In this paper, we propose a novel approach that leverages Reinforcement Learning (RL) to automatically design epidemic management rules. By using RL, we formulate the problem as determining the optimal daily inspection frequency during an outbreak with the goal of minimizing both the inspection cost and the epidemic size. A management rule is trained using a deep neuroevolution algorithm called Evolutionary Strategy (ES). An epidemic simulator is developed to provide a realistic and dynamic environment for evaluating our proposed approach’s effectiveness. Extensive experiments have shown that our RL-based approach outperformed conventional methods and can better balance the trade-off between the inspection cost and epidemic size. The findings from this work provide not only a framework for autonomous learning of epidemic management rules, but also potential implications for real-world epidemic control and policy-making.