From A Timeline Contact Graph to Close Contact Tracing and Infection Diffusion Intervention (从时间线接触图到密切接触者追踪和感染扩散干预)

期刊：IEEE Transactions on Knowledge and Data Engineering（TKDE）

卷期/文章号：36(12): 8328–8340（2024 年 12 月）

DOI：10.1109/TKDE.2024.3423476

作者：Yipeng Zhang, Zhifeng Bao, Yuchen Li, Baihua Zheng, Xiaoli Wang

英文摘要:

   This paper proposes a novel graph structure to address the problems of information spreading in a real-world, frequently updating graph, with two main contributions at hand: accurately tracing infection diffusion according to fine-grained user movements and finding vulnerable vertices under the virus immunization scenario to mitigate infection diffusion. Unlike previous work that primarily predicts the long-term epidemic trend at the census level, this study aims to intervene in the short-term at the individual level. Therefore, two downstream tasks are formulated to illustrate practicalities: Epidemic Mitigating in Public Area problem (EAM) and Epidemic Maximized Spread in Public Area problem (ESA),where EMA aims to find intervention strategies, and ESA is an adversarial solution against the intervention strategy to test the robustness. Comprehensive experiments are conducted using two real-world datasets with millions of public transport trips, which demonstrate the effectiveness of our approach and highlight the importance of considering the dynamic nature of close contacts in epidemic modelling.

中文摘要:

    本文提出了一种新颖的图结构来解决现实世界中频繁更新的图中的信息传播问题，主要贡献在于：根据细粒度的用户移动准确追踪感染扩散；以及在病毒免疫场景下找到脆弱顶点以减缓感染扩散。与以往主要在人口普查层面预测长期疫情趋势的研究不同，本研究旨在在个人层面进行短期干预。因此，我们制定了两个下游任务来说明实用性：公共区域流行病缓解问题 (EAM) 和公共区域流行病最大化传播问题 (ESA)。其中，EMA 旨在寻找干预策略，而 ESA 是针对干预策略的对抗解，用于测试其鲁棒性。我们使用两个包含数百万次公共交通出行的真实数据集进行了全面的实验，证明了我们方法的有效性，并强调了在流行病建模中考虑密切接触者的动态特性的重要性。