Human mobility data in the COVID-19 pandemic: characteristics, applications, and challenges
Human mobility data in the COVID-19 pandemic: characteristics, applications, and challenges
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DOI：
10.1080/17538947.2021.1952324
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发表时间：
2021-07-14
影响因子：
5.1
通讯作者：
Li, Zhenlong
中科院分区：
文献类型：
--
作者：
Hu, Tao;Wang, Siqin;Li, Zhenlong
COVID-19 大流行给世界各地带来了前所未有的挑战。许多研究应用流动性数据来探索随时间变化的时空趋势，调查与其他变量的关联，并预测或模拟 COVID-19 的传播。我们的目标是提供人员流动开放数据的全面概述，以指导研究人员和政策制定者针对 COVID-19 大流行和其他传染病爆发进行数据驱动的评估和决策。我们从面向数据的角度回顾了最近关于 COVID-19 和人员流动的出版物，总结了 COVID-19 研究中的流动数据使用情况。我们确定了移动数据的三个主要来源：公共交通系统、移动运营商和移动电话应用程序。通常使用四种方法来估计人员流动性：基于公共交通的流量、社会活动模式、基于索引的流动性数据和社交媒体衍生的流动性数据。我们通过评估数据隐私、质量、时空覆盖、高性能数据存储和处理以及可访问性来比较移动数据集的特征。我们还提出了使用移动数据的挑战和未来方向。本综述对于了解在 COVID-19 大流行和未来疾病爆发中人员流动数据的使用和获取做出了关键贡献。
The COVID-19 pandemic poses unprecedented challenges around the world. Many studies have applied mobility data to explore spatiotemporal trends over time, investigate associations with other variables, and predict or simulate the spread of COVID-19. Our objective was to provide a comprehensive overview of human mobility open data to guide researchers and policymakers in conducting data-driven evaluations and decision-making for the COVID-19 pandemic and other infectious disease outbreaks. We summarized the mobility data usage in COVID-19 studies by reviewing recent publications on COVID-19 and human mobility from a data-oriented perspective. We identified three major sources of mobility data: public transit systems, mobile operators, and mobile phone applications. Four approaches have been commonly used to estimate human mobility: public transit-based flow, social activity patterns, index-based mobility data, and social media-derived mobility data. We compared mobility datasets' characteristics by assessing data privacy, quality, space-time coverage, high-performance data storage and processing, and accessibility. We also present challenges and future directions of using mobility data. This review makes a pivotal contribution to understanding the use of and access to human mobility data in the COVID-19 pandemic and future disease outbreaks.