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

Corentin Cot (1) , Giacomo Cacciapaglia (1) , Francesco Sannino

1 IP2I Lyon - Institut de Physique des 2 Infinis de Lyon

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.

Domaines: Physique des Hautes Energies - Phénoménologie [hep-ph]; Physique [physics]

Corentin Cot, Giacomo Cacciapaglia, Francesco Sannino. Mining Google and Apple mobility data: temporal anatomy for COVID-19 social distancing. Scientific Reports, 2021, 11 (1), pp.4150. ⟨10.1038/s41598-021-83441-4⟩. ⟨hal-03150531⟩