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

Abstract

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.

Figure 1

The COVID-19 Mobility Map for Europe and the US. The two maps represent respectively the European and US states with different shades of mobility from the highest (HM) in bright red to the lowest (LM) in cyan. At the bottom of the figure there are three tadpole-like plots showing correlations between the four mobility reduction categories: Residential and Workplace from Google, Driving and Walking from Apple. The head of the tadpoles correspond to the average over 6 weeks after social distancing begins, while the tail indicates a 8 week average. The colour code in the three plots reflects the maps one. The maps are drawn with Wolfram Mathematica.

Figure 2

Temporal anatomy of COVID-19 social distancing effects. In panel (a), we show $\mathrm{\Delta }t$ and the percentage variation $\mathrm{\Delta }\gamma$ in the infection rate for the European countries considered in this study. In panel (b), we show the same for all the US states. In panels (c, d) we display the same results in the form of histograms, for Europe and the US separately, highlighting that $\mathrm{\Delta }t$ clusters around similar values. In panel (e), we illustrate the subdivision of the first wave epidemic curve in three temporal regions: A before social distancing as defined via mobility data occurs, B until an effect is observed in the epidemic curve as a change in $\gamma$, and C covering the later times. $\mathrm{\Delta }t$ equals to the duration of the period B.

Figure 3

Immobility indicator for the European countries and the US states. Values of the immobility indicator $╲M$ for Europe (top) and the US (bottom). The colour code corresponds to the ranking of each European country and each US state, matching the one used in Fig. 1.

Figure 4

Infection rate compared to the mobility data. Racecar plots showing the fitted infection rates $\gamma$ versus the Google/Apple mobility categories. The vertical segment indicates the difference between 6 week (dot) and 8 week averages; the horizontal bars indicate the fit error on $\gamma$.