A big-data driven approach to analyzing and modeling human mobility trend under non-pharmaceutical interventions during COVID-19 pandemic.
A big-data driven approach to analyzing and modeling human mobility trend under non-pharmaceutical interventions during COVID-19 pandemic.
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COVID-19大流行期间非药物干预下人员流动趋势分析和建模的大数据驱动方法
DOI：
10.1016/j.trc.2020.102955
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发表时间：
2021-03
期刊：
影响因子：
--
通讯作者：
Zhang L
中科院分区：
文献类型：
--
作者：
Hu S;Xiong C;Yang M;Younes H;Luo W;Zhang L
在前所未有的2019冠状病毒病（COVID-19）挑战期间，非药物干预措施成为广泛采用的策略，以限制身体运动和相互作用，以减轻病毒传播。对于态势感知和决策支持，快速可用且准确的关于人员流动性和社交距离的大数据分析对机构和决策者来说是非常宝贵的。本文提出了一个大数据驱动的分析框架，该框架每天摄取TB级的数据，并定量评估COVID-19期间的人员流动趋势。使用美国（U.S.）超过1.5亿个月活动样本的移动终端位置数据，这项研究成功地在县一级用三个主要指标衡量了人的流动性：每人每日平均旅行次数;每日平均旅行人英里数;以及每日呆在家里的居民百分比。一组广义加性混合模型，从其他混杂效应，包括病毒效应，社会人口效应，天气效应，行业效应，和时空自相关解开政策对人口流动的影响。结果表明，政策发挥了有限的，时间减少，和区域特定的影响，人类运动。居家令仅导致人员流动性下降3.5%-7.9%，而重新开放指南导致流动性增加1.6%-5.2%。结果还表明，美国各县之间存在合理的空间异质性，其中确诊的COVID-19病例数量、收入水平、行业结构、年龄和种族分布发挥着重要作用。该框架生成的数据信息可供公众使用，以及时了解流动趋势和政策影响，并为进一步遏制病毒传播提供时间敏感的决策支持。
During the unprecedented coronavirus disease 2019 (COVID-19) challenge, non-pharmaceutical interventions became a widely adopted strategy to limit physical movements and interactions to mitigate virus transmissions. For situational awareness and decision-support, quickly available yet accurate big-data analytics about human mobility and social distancing is invaluable to agencies and decision-makers. This paper presents a big-data-driven analytical framework that ingests terabytes of data on a daily basis and quantitatively assesses the human mobility trend during COVID-19. Using mobile device location data of over 150 million monthly active samples in the United States (U.S.), the study successfully measures human mobility with three main metrics at the county level: daily average number of trips per person; daily average person-miles traveled; and daily percentage of residents staying home. A set of generalized additive mixed models is employed to disentangle the policy effect on human mobility from other confounding effects including virus effect, socio-demographic effect, weather effect, industry effect, and spatiotemporal autocorrelation. Results reveal the policy plays a limited, time-decreasing, and region-specific effect on human movement. The stay-at-home orders only contribute to a 3.5%-7.9% decrease in human mobility, while the reopening guidelines lead to a 1.6%-5.2% mobility increase. Results also indicate a reasonable spatial heterogeneity among the U.S. counties, wherein the number of confirmed COVID-19 cases, income levels, industry structure, age and racial distribution play important roles. The data informatics generated by the framework are made available to the public for a timely understanding of mobility trends and policy effects, as well as for time-sensitive decision support to further contain the spread of the virus.
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影响因子：
56.9
作者：
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通讯作者：
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DOI：
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DOI：
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发表时间：
2018-01-01
影响因子：
8.3
作者：
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通讯作者：
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DOI：
10.1016/j.trc.2016.12.008
发表时间：
2017-02-01
影响因子：
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作者：
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通讯作者：
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