Hierarchical Models for Detecting Mobility Clusters during COVID-19

Necati A. Ayan, Arti Ramesh, A. Seetharam, A. A. D. A. Rocha
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引用次数: 1

Abstract

Analyzing and developing mobility models that accurately capture human mobility is critical for combating the COVID-19 pandemic and minimizing the spread of the disease. In this paper, we design a two-layer hierarchical mobility model to model user mobility in the city of Rio de Janeiro and its suburbs by analyzing cellular network connectivity data during the COVID-19 pandemic. To this end, we collaborate with one of the main network providers in Brazil, TIM Brazil, to collect user connectivity logs from April 5th, 2020 to July 2nd, 2020, which we use to generate mobility graphs. We adopt the Louvain community detection algorithm in the first layer of our hierarchical model to detect the main communities in the city from the mobility graphs. Our model then uses the KMeans++ and Agglomerative clustering methods in the second layer to identify high, medium, and low mobility clusters within each community. Via extensive experiments, we show that the Louvain, and the Kmeans++ and Agglomerative algorithms outperform traditional clustering approaches in the first and second layers, respectively. Our results also demonstrate that our hierarchical model is able to pinpoint main mobility locations within each community and can be used by authorities to implement partial lockdown measures in place of widely unpopular complete lockdowns.
COVID-19期间移动集群检测的分层模型
分析和开发准确捕捉人类流动性的流动性模型对于抗击COVID-19大流行和最大限度地减少疾病传播至关重要。本文通过分析2019冠状病毒病大流行期间的蜂窝网络连接数据,设计了一个双层分层移动性模型,对里约热内卢市及其郊区的用户移动性进行建模。为此,我们与巴西的主要网络提供商之一TIM Brazil合作,收集2020年4月5日至2020年7月2日的用户连接日志,并使用这些日志生成移动性图。我们在分层模型的第一层采用Louvain社区检测算法,从移动图中检测城市的主要社区。然后,我们的模型在第二层使用kmeans++和Agglomerative聚类方法来识别每个社区内的高、中、低流动性集群。通过大量的实验,我们表明Louvain、kmeans++和Agglomerative算法分别在第一层和第二层优于传统的聚类方法。我们的研究结果还表明,我们的分层模型能够确定每个社区内的主要流动地点,当局可以利用它来实施部分封锁措施,以取代广泛不受欢迎的完全封锁。
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