加强疫情控制：既挽救生命又挽救经济
Reinforced Epidemic Control: Saving Both Lives and Economy
摘要：
在大多数城市，拯救生命还是经济是疫情控制的两难选择，而智能追踪技术则引发了人们对隐私的担忧。在本文中，我们提出了一个解决方案，不需要私人数据的生活或经济困境。我们绕过私人数据的要求，通过动态控制区域间的流动性，仅依赖于原产地（OD）数据来抑制流行病的传播。我们开发了双目标强化学习流行病控制代理（DURLECA）搜索移动控制策略，可以同时最大限度地减少感染传播和最大限度地保持流动性。DURLECA采用一种新的图神经网络，即Flow-GNN，来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强化学习代理生成移动控制动作。DURLECA的训练是由一个构造良好的奖励函数指导的，该函数捕捉了流行病控制和流动性保持之间的自然权衡关系。此外，我们还设计了两种探索策略，以提高智能体的搜索效率，帮助其摆脱局部最优。在真实世界OD数据集上的广泛实验结果表明，DURLECA能够将感染抑制在极低的水平，同时保持城市中76%的流动性。我们的实现可以在https://github.com/anyleopeace/DURLECA/上获得。
英文摘要：
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/.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
