Mining Google and Apple mobility data: temporal anatomy for COVID-19 social distancing

被引:71
|
作者
Cot, Corentin [1 ,2 ]
Cacciapaglia, Giacomo [1 ,2 ]
Sannino, Francesco [3 ,4 ,5 ]
机构
[1] CNRS, Inst Phys 2 Infinis IP2I, IN2P3, UMR5822, F-69622 Villeurbanne, France
[2] Univ Claude Bernard Lyon 1, Univ Lyon, F-69001 Lyon, France
[3] Univ Southern Denmark, CP3 Origins & Danish Inst Adv Study, Campusvej 55, DK-5230 Odense, Denmark
[4] Univ Napoli Federico II, Dipartimento Fis & Pancini, I-80126 Naples, Italy
[5] Complesso Univ Monte S Angelo, INFN Sez Napoli, Edificio 6,Via Cintia, I-80126 Naples, Italy
关键词
D O I
10.1038/s41598-021-83441-4
中图分类号
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号
07 ; 0710 ; 09 ;
摘要
We employ the Google and Apple mobility data to identify, quantify and classify different degrees of social distancing and characterise their imprint on the first wave of the COVID-19 pandemic in Europe and in the United States. We identify the period of enacted social distancing via Google and Apple data, independently from the political decisions. Our analysis allows us to classify different shades of social distancing measures for the first wave of the pandemic. We observe a strong decrease in the infection rate occurring two to five weeks after the onset of mobility reduction. A universal time scale emerges, after which social distancing shows its impact. We further provide an actual measure of the impact of social distancing for each region, showing that the effect amounts to a reduction by 20-40% in the infection rate in Europe and 30-70% in the US.
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收藏
页数:8
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