Development of Public Health Policy by Digital Twin Microsimulation and Q-learning: A COVID-19 Booster Case Study
利用数字孪生微观模拟与 Q 学习制定公共卫生政策:新冠加强针案例
The COVID-19 pandemic highlighted the urgent need for effective vaccine policies, but traditional clinical trials often lack sufficient data to capture the diverse population characteristics necessary for comprehensive public health strategies. Ethical concerns around randomized trials during a pandemic further complicate policy development for public health. Reinforcement Learning (RL) offers a promising alternative for vaccine policy development. However, direct online RL…