加强接触者追踪和流行病干预
Reinforced Contact Tracing and Epidemic Intervention
摘要：
近期爆发的COVID-19疫情对人们的生命构成严重威胁。疫情控制策略切断了人类的日常通勤，也对经济造成了损害。在本文中，我们开发了一个基于个人的强化学习流行病控制代理（IDRLECA），以寻找智能的流行病控制策略，可以同时最小化感染和移动干预的成本。IDRLECA首先使用感染概率模型来计算每个个体的当前感染概率。然后，将感染概率与个人的健康状况和运动信息一起输入到一个新的GNN，以估计病毒通过人类接触的传播。估计的风险被用来进一步支持RL代理选择个人级别的药物控制行动。IDRLECA的培训由一个专门设计的奖励函数指导，该函数考虑到流动性干预的成本和流行病控制的有效性。此外，我们设计了一个控制动作选择的约束条件，减轻了难度，进一步提高了探索效率。广泛的实验结果表明，IDRLECA可以抑制感染在一个非常低的水平，并保留超过95%的人的流动性。
英文摘要：
The recent outbreak of COVID-19 poses a serious threat to people's lives. Epidemic control strategies have also caused damage to the economy by cutting off humans' daily commute. In this paper, we develop an Individual-based Reinforcement Learning Epidemic Control Agent (IDRLECA) to search for smart epidemic control strategies that can simultaneously minimize infections and the cost of mobility intervention. IDRLECA first hires an infection probability model to calculate the current infection probability of each individual. Then, the infection probabilities together with individuals' health status and movement information are fed to a novel GNN to estimate the spread of the virus through human contacts. The estimated risks are used to further support an RL agent to select individual-level epidemic-control actions. The training of IDRLECA is guided by a specially designed reward function considering both the cost of mobility intervention and the effectiveness of epidemic control. Moreover, we design a constraint for control-action selection that eases its difficulty and further improve exploring efficiency. Extensive experimental results demonstrate that IDRLECA can suppress infections at a very low level and retain more than 95% of human mobility.
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。
