Abstract
In response to the COVID-19 pandemic, countries have sought to control SARS-CoV-2 transmission by restricting population movement through social distancing interventions, thus reducing the number of contacts. Mobility data represent an important proxy measure of social distancing, and here, we characterise the relationship between transmission and mobility for 52 countries around the world. Transmission significantly decreased with the initial reduction in mobility in 73% of the countries analysed, but we found evidence of decoupling of transmission and mobility following the relaxation of strict control measures for 80% of countries. For the majority of countries, mobility explained a substantial proportion of the variation in transmissibility (median adjusted R-squared: 48%, interquartile range - IQR - across countries [27–77%]). Where a change in the relationship occurred, predictive ability decreased after the relaxation; from a median adjusted R-squared of 74% (IQR across countries [49–91%]) pre-relaxation, to a median adjusted R-squared of 30% (IQR across countries [12–48%]) post-relaxation. In countries with a clear relationship between mobility and transmission both before and after strict control measures were relaxed, mobility was associated with lower transmission rates after control measures were relaxed indicating that the beneficial effects of ongoing social distancing behaviours were substantial.
Highlights
In response to the COVID-19 pandemic, countries have sought to control SARS-CoV-2 transmission by restricting population movement through social distancing interventions, reducing the number of contacts
Understanding how well mobility data reflects population contact rates and whether that relationship is changing in countries that are transitioning, exiting or re-entering lockdown measures is important for tracking the trajectory of national epidemics and assessing the effectiveness of ongoing control measures
Based on an estimated mobility reduction in the UK of 15% as of October 25th, 2020, we predict that the epidemic in the UK was not under control as of October 25th, 2020 (Fig. 2b, Rt,i in red above 1) this is uncertain given that 15% is within the 95% credible interval of our estimated threshold post change
Summary
In response to the COVID-19 pandemic, countries have sought to control SARS-CoV-2 transmission by restricting population movement through social distancing interventions, reducing the number of contacts. Social-distancing, case isolation, and shielding have been widely used to limit community-level transmission of SARS-CoV2 and protect vulnerable groups[7,8]. These interventions aim to reduce mobility and contacts within the population and to reduce the transmission of SARS-CoV-2, as measured by the effective reproduction number (R, the average number of secondary cases caused by a primary case). Understanding how well mobility data reflects population contact rates and whether that relationship is changing in countries that are transitioning, exiting or re-entering lockdown measures is important for tracking the trajectory of national epidemics and assessing the effectiveness of ongoing control measures. The framework is applied to 52 countries with sustained SARS-CoV-2 transmission based on two distinct country-specific automated measures of human mobility, Apple and Google mobility data
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