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Mining Google and Apple mobility data: temporal anatomy for COVID-19 social distancing
Corentin Cot, Giacomo Cacciapaglia, Francesco Sannino

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

Science & Mathematics Published 2021 Verification Pending

Overview & Synopsis

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.

Indexed from repository: HAL Open Science (hal.science)
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Document Information

Document Title Mining Google and Apple mobility data: temporal anatomy for COVID-19 social distancing
Author / Organization Corentin Cot, Giacomo Cacciapaglia, Francesco Sannino
Subject & Discipline Multidisciplinary Academic Open Research
Categories Science & Mathematics
Publication Year 2021
License / Rights Open Access / Creative Commons
Resource Verification Listed PDF (Verification Pending)
Original Host Website HAL Open Science (hal.science)
File Format application/pdf (Portable Document Format)
Language EN
Catalog ID DOC-130555
Original Source Link Visit Source Page on hal.science »