McGill University, Montreal, Canada.
Tsinghua University, Beijing, China.
Sci Rep. 2022 Aug 16;12(1):13843. doi: 10.1038/s41598-022-17892-8.
Mobility-control policy is a controversial nonpharmacological approach to pandemic control due to its restriction on people's liberty and economic impacts. Due to the computational complexity of mobility control, it is challenging to assess or compare alternative policies. Here, we develop a pandemic policy assessment system that employs artificial intelligence (AI) to evaluate and analyze mobility-control policies. The system includes three components: (1) a general simulation framework that models different policies to comparable network-flow control problems; (2) a reinforcement-learning (RL) oracle to explore the upper-bound execution results of policies; and (3) comprehensive protocols for converting the RL results to policy-assessment measures, including execution complexity, effectiveness, cost and benefit, and risk. We applied the system to real-world metropolitan data and evaluated three popular policies: city lockdown, community quarantine, and route management. For each policy, we generated mobility-pandemic trade-off frontiers. The results manifest that the smartest policies, such as route management, have high execution complexity but limited additional gain from mobility retention. In contrast, a moderate-level intelligent policy such as community quarantine has acceptable execution complexity but can effectively suppress infections and largely mitigate mobility interventions. The frontiers also show one or two turning points, reflecting the safe threshold of mobility retention when considering policy-execution errors. In addition, we simulated different policy environments and found inspirations for the current policy debates on the zero-COVID policy, vaccination policy, and relaxing restrictions.
移动控制政策是一种有争议的非药物控制大流行的方法，因为它限制了人们的自由和经济影响。由于移动控制的计算复杂性，评估或比较替代政策具有挑战性。在这里，我们开发了一个使用人工智能（AI）评估和分析移动控制政策的大流行政策评估系统。该系统包括三个组件：（1）一个通用模拟框架，该框架将不同的政策建模为可比的网络流量控制问题；（2）一个强化学习（RL）预言机，用于探索政策的上限执行结果；（3）将 RL 结果转换为政策评估措施的综合协议，包括执行复杂性、有效性、成本和收益以及风险。我们将该系统应用于现实大都市数据，并评估了三种流行的政策：城市封锁、社区隔离和路线管理。对于每种政策，我们生成了移动-大流行的权衡前沿。结果表明，最智能的政策，如路线管理，具有高执行复杂性，但从保留移动性中获得的额外收益有限。相比之下，社区隔离等中等水平的智能政策具有可接受的执行复杂性，但可以有效地抑制感染并在很大程度上减轻移动性干预。前沿还显示出一个或两个转折点，反映了考虑到政策执行错误时保留移动性的安全阈值。此外，我们模拟了不同的政策环境，并为当前关于零 COVID 政策、疫苗接种政策和放宽限制的政策辩论提供了启示。
