Human mobility and COVID-19 initial dynamics

Abstract

Countries in Europe took different mobility containment measures to curb the spread of COVID-19. The European Commission asked mobile network operators to share on a voluntarily basis anonymised and aggregate mobile data to improve the quality of modelling and forecasting for the pandemic at EU level. In fact, mobility data at EU scale can help understand the dynamics of the pandemic and possibly limit the impact of future waves. Still, since a reliable and consistent method to measure the evolution of contagion at international level is missing, a systematic analysis of the relationship between human mobility and virus spread has never been conducted. A notable exceptions are France and Italy, for which data on excess deaths, an indirect indicator which is generally considered to be less affected by national and regional assumptions, are available at department and municipality level, respectively. Using this information together with anonymised and aggregated mobile data, this study shows that mobility alone can explain up to 92% of the initial spread in these two EU countries, while it has a slow decay effect after lockdown measures, meaning that mobility restrictions seem to have effectively contribute to save lives. It also emerges that internal mobility is more important than mobility across provinces and that the typical lagged positive effect of reduced human mobility on reducing excess deaths is around 14–20 days. An analogous analysis relative to Spain, for which an IgG SARS-Cov-2 antibody screening study at province level is used instead of excess deaths statistics, confirms the findings. The same approach adopted in this study can be easily extended to other European countries, as soon as reliable data on the spreading of the virus at a suitable level of granularity will be available. Looking at past data, relative to the initial phase of the outbreak in EU Member States, this study shows in which extent the spreading of the virus and human mobility are connected. The findings will support policymakers in formulating the best data-driven approaches for coming out of confinement and mostly in building future scenarios in case of new outbreaks.

Association between the New COVID-19 Cases and Air Pollution with Meteorological Elements in Nine Counties of New York State

“…We use the number of COVID-19 cases as a proxy for a reduction in automotive traffic and a decrease in industrial activities. Some recent studies [26,27] have shown a strong association (R 2 > 0.89) between COVID-19 incidence and mobility. These authors showed that the higher the mobility the higher the number of contagions.…”

On the use of data from multiple mobile network operators in Europe to fight COVID-19

“…The indicator adopts a weekly time frequency (separating between week days and weekend mobility), and uses the NUTS3 regions as a geographic reference. For more information about the connectivity matrix and its applications, we redirect the interested reader to Iacus et al (2020a).…”

Anomaly detection of mobile positioning data with applications to COVID-19 situational awareness

“…Fig. 1 The simplified logic behind the anomaly detection strategy: a sudden drop of the volume of the cell may identify an anomaly, while one within the natural variability of the data not Some applications of these data to different contexts than the one presented here can be found in Santamaria et al (2020) and Iacus et al (2020aIacus et al ( , 2020b.…”