Strong long ties facilitate epidemic containment on mobility networks.

IF 2.2 Q2 MULTIDISCIPLINARY SCIENCES PNAS nexus Pub Date : 2024-11-15 eCollection Date: 2024-11-01 DOI:10.1093/pnasnexus/pgae515
Jianhong Mou, Suoyi Tan, Juanjuan Zhang, Bin Sai, Mengning Wang, Bitao Dai, Bo-Wen Ming, Shan Liu, Zhen Jin, Guiquan Sun, Hongjie Yu, Xin Lu
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Abstract

The analysis of connection strengths and distances in the mobility network is pivotal for delineating critical pathways, particularly in the context of epidemic propagation. Local connections that link proximate districts typically exhibit strong weights. However, ties that bridge distant regions with high levels of interaction intensity, termed strong long (SL) ties, warrant increased scrutiny due to their potential to foster satellite epidemic clusters and extend the duration of pandemics. In this study, SL ties are identified as outliers on the joint distribution of distance and flow in the mobility network of Shanghai constructed from 1 km × 1 km high-resolution mobility data. We propose a grid-joint isolation strategy alongside a reaction-diffusion transmission model to assess the impact of SL ties on epidemic propagation. The findings indicate that regions connected by SL ties exhibit a small spatial autocorrelation and display a temporal similarity pattern in disease transmission. Grid-joint isolation based on SL ties reduces cumulative infections by an average of 17.1% compared with other types of ties. This work highlights the necessity of identifying and targeting potentially infected remote areas for spatially focused interventions, thereby enriching our comprehension and management of epidemic dynamics.

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强长联系有助于遏制流动网络中的流行病。
对流动网络中的连接强度和距离进行分析,对于划定关键路径至关重要,尤其是在流行病传播的背景下。连接邻近地区的本地连接通常具有很强的权重。然而,连接远距离地区的纽带具有较高的互动强度,被称为 "强长(SL)纽带",由于其有可能促进卫星疫情集群并延长大流行病的持续时间,因此值得加强研究。在本研究中,我们利用 1 km × 1 km 的高分辨率流动数据构建了上海的流动网络,将强长联系识别为距离和流量联合分布中的异常值。我们提出了网格连接隔离策略和反应扩散传播模型,以评估 SL 连接对疫情传播的影响。研究结果表明,由 SL 联系连接的区域表现出较小的空间自相关性,并在疾病传播中显示出时间相似性模式。与其他类型的纽带相比,基于 SL 纽带的网格连接隔离可将累积感染率平均降低 17.1%。这项工作强调了识别潜在感染偏远地区并将其作为空间重点干预对象的必要性,从而丰富了我们对流行病动态的理解和管理。
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