论文标题
加强流行控制：挽救生命和经济
Reinforced Epidemic Control: Saving Both Lives and Economy
论文作者
论文摘要
在大多数城市中，拯救生命或经济是流行病控制的困境，而智能追踪技术会引起人们的隐私问题。在本文中，我们提出了一种不需要私人数据的生命或经济困境的解决方案。我们通过对仅依赖原点设计（OD）数据的区域间活动性的动态控制来抑制流行病的传播来绕过私有数据的要求。我们开发了双目标加固学习者的流行病控制剂（Dureca）来搜索迁移率控制策略，这些策略可以同时最大程度地减少感染扩散并最大程度地保留迁移率。杜尔卡（Dureca）雇用了一个新型的图神经网络，即流动-GNN，以估计城市流动性引起的病毒传播风险。估计的风险用于支持加强学习代理，以产生移动性控制动作。 Dureca的培训具有结构良好的奖励功能，该功能捕获了流行病控制与保留流动性之间的自然权衡关系。此外，我们设计了两种探索策略，以提高代理商的搜索效率并帮助它摆脱当地最佳效果。现实世界中的OD数据集上的广泛实验结果表明，杜莱卡能够以极低的水平抑制感染，同时保留了该市76％的移动性。我们的实施可从https://github.com/anyleopeace/durleca/获得。
Saving lives or economy is a dilemma for epidemic control in most cities while smart-tracing technology raises people's privacy concerns. In this paper, we propose a solution for the life-or-economy dilemma that does not require private data. We bypass the private-data requirement by suppressing epidemic transmission through a dynamic control on inter-regional mobility that only relies on Origin-Designation (OD) data. We develop DUal-objective Reinforcement-Learning Epidemic Control Agent (DURLECA) to search mobility-control policies that can simultaneously minimize infection spread and maximally retain mobility. DURLECA hires a novel graph neural network, namely Flow-GNN, to estimate the virus-transmission risk induced by urban mobility. The estimated risk is used to support a reinforcement learning agent to generate mobility-control actions. The training of DURLECA is guided with a well-constructed reward function, which captures the natural trade-off relation between epidemic control and mobility retaining. Besides, we design two exploration strategies to improve the agent's searching efficiency and help it get rid of local optimums. Extensive experimental results on a real-world OD dataset show that DURLECA is able to suppress infections at an extremely low level while retaining 76\% of the mobility in the city. Our implementation is available at https://github.com/anyleopeace/DURLECA/.
