利用移动网络生态系统分析大流行情况下的大规模移动模式

IF 2.6 3区 工程技术 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS
Patricia Callejo, Marco Gramaglia, Rubén Cuevas, Ángel Cuevas, Michael Carl Tschantz
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引用次数: 0

摘要

移动网络技术无处不在、无孔不入,已经深深扎根于我们的日常生活中,只要出于非常简单的目的与之互动(如发送信息或浏览互联网),我们就会产生前所未有的大量数据,通过分析这些数据可以了解我们的行为。虽然电信公司和大型科技公司在过去几年中广泛采用了这种做法,但这种在 20 年前还难以想象的情况却只被轻微地利用来对抗 COVID-19 大流行病。在本文中,我们将讨论当前移动网络生态系统中可能存在的替代方案,以便让监管机构和流行病学家正确理解我们的移动模式,从而最大限度地提高所引入对策的效率和范围。为了验证我们的分析,我们剖析了受到大流行病严重影响的两个欧洲主要国家的用户位置精细数据集。通过两个涉及宏观和微观方面的示例,我们揭示了利用传统移动网络技术获取的这些数据的使用潜力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Analyzing Mobility Patterns at Scale in Pandemic Scenarios Leveraging the Mobile Network Ecosystem
The ubiquity and pervasiveness of mobile network technologies has made them so deeply ingrained in our everyday lives that by interacting with them for very simple purposes (e.g., messaging or browsing the Internet), we produce an unprecedented amount of data that can be analyzed to understand our behavior. While this practice has been extensively adopted by telcos and big tech companies in the last few years, this condition, which was unimaginable just 20 years ago, has only been mildly exploited to fight the COVID-19 pandemic. In this paper, we discuss the possible alternatives that we could leverage in the current mobile network ecosystem to provide regulators and epidemiologists with the right understanding of our mobility patterns, to maximize the efficiency and extent of the introduced countermeasures. To validate our analysis, we dissect a fine-grained dataset of user positions in two major European countries severely hit by the pandemic. The potential of using these data, harvested employing traditional mobile network technologies, is unveiled through two exemplary cases that tackled macro and microscopic aspects.
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来源期刊
Electronics
Electronics Computer Science-Computer Networks and Communications
CiteScore
1.10
自引率
10.30%
发文量
3515
审稿时长
16.71 days
期刊介绍: Electronics (ISSN 2079-9292; CODEN: ELECGJ) is an international, open access journal on the science of electronics and its applications published quarterly online by MDPI.
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