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Mining Google and Apple mobility data: temporal anatomy for COVID-19 social distancing
Corentin Cot1,2, Giacomo Cacciapaglia3,4, Francesco Sannino5,6
1Institut de Physique des 2 Infinis (IP2I), CNRS/IN2P3, UMR5822, 69622, Villeurbanne, France.
Scientific Reports
|February 19, 2021
Summary
Google and Apple mobility data reveal social distancing measures significantly reduced COVID-19 infection rates. This study quantifies the impact, showing a 20-70% decrease in infections across Europe and the US.
Area of Science:
- Epidemiology
- Public Health
- Data Science
Background:
- The first wave of the COVID-19 pandemic necessitated widespread social distancing measures.
- Understanding the precise impact of these measures on infection rates is crucial for public health policy.
Purpose of the Study:
- To identify, quantify, and classify the effects of social distancing on the COVID-19 pandemic's first wave.
- To independently assess social distancing impacts using mobility data, separate from political decisions.
Main Methods:
- Utilized Google and Apple mobility data to track changes in population movement.
- Analyzed mobility reduction trends to identify periods and degrees of social distancing.
- Correlated mobility changes with COVID-19 infection rates in Europe and the United States.
Main Results:
- A consistent time lag of two to five weeks was observed between mobility reduction and decreased infection rates.
- Social distancing measures showed a quantifiable impact, reducing infection rates by 20-40% in Europe.
- In the United States, social distancing resulted in a more significant reduction of 30-70% in infection rates.
Conclusions:
- Mobility data provides a reliable, independent measure of social distancing effectiveness during the COVID-19 pandemic.
- Social distancing demonstrably lowered infection rates, with a universal time scale for its impact.
- The findings offer valuable insights for informing future public health interventions during pandemics.
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