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墨西哥在 COVID-19 大流行期间的城际旅行网络。

Intermunicipal travel networks of Mexico during the COVID-19 pandemic.

机构信息

CINVESTAV-IPN, Irapuato, Mexico.

Astronomer LTD, Cincinnati, USA.

出版信息

Sci Rep. 2023 May 26;13(1):8566. doi: 10.1038/s41598-023-35542-5.

Abstract

Human mobility networks are widely used for diverse studies in geography, sociology, and economics. In these networks, nodes usually represent places or regions and links refer to movement between them. They become essential when studying the spread of a virus, the planning of transit, or society's local and global structures. Therefore, the construction and analysis of human mobility networks are crucial for a vast number of real-life applications. This work presents a collection of networks that describe the human travel patterns between municipalities in Mexico in the 2020-2021 period. Using anonymized mobile location data, we constructed directed, weighted networks representing the volume of travels between municipalities. We analysed changes in global, local, and mesoscale network features. We observe that changes in these features are associated with factors such as COVID-19 restrictions and population size. In general, the implementation of restrictions at the start of the COVID-19 pandemic in early 2020, induced more intense changes in network features than later events, which had a less notable impact in network features. These networks will result very useful for researchers and decision-makers in the areas of transportation, infrastructure planning, epidemic control and network science at large.

摘要

人类移动性网络在地理、社会学和经济学等领域的各种研究中被广泛应用。在这些网络中,节点通常代表地点或区域,而边则表示它们之间的移动。当研究病毒的传播、交通规划或社会的局部和全局结构时,它们变得至关重要。因此,人类移动性网络的构建和分析对于大量的现实生活应用至关重要。本工作展示了一组网络,这些网络描述了 2020-2021 年期间墨西哥各城市之间的人类出行模式。我们使用匿名的移动位置数据构建了表示城市间出行量的有向加权网络。我们分析了全局、局部和中尺度网络特征的变化。我们观察到,这些特征的变化与 COVID-19 限制和人口规模等因素有关。一般来说,2020 年初 COVID-19 大流行开始时实施的限制措施,导致网络特征发生了更剧烈的变化,而后来的事件对网络特征的影响则不那么显著。这些网络将对交通、基础设施规划、疫情控制和网络科学等领域的研究人员和决策者非常有用。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9776/10220018/9a9fd6ed3bef/41598_2023_35542_Fig1_HTML.jpg

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