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.

欧洲国家采取了不同的流动控制措施来遏制 COVID-19 的传播。欧盟委员会要求移动网络运营商自愿共享匿名和汇总的移动数据，以提高欧盟层面对大流行病的建模和预测质量。事实上，欧盟规模的流动性数据可以帮助了解大流行的动态，并可能限制未来浪潮的影响。尽管如此，由于缺乏在国际层面衡量传染病演变的可靠和一致的方法，因此从未对人类流动性与病毒传播之间的关系进行系统分析。一个值得注意的例外是法国和意大利，它们的超额死亡数据是一个通常被认为受国家和地区假设影响较小的间接指标，分别在省级和市级可用。使用这些信息以及匿名和汇总的移动数据，本研究表明，仅移动性就可以解释这两个欧盟国家高达 92% 的初始传播，而在采取封锁措施后它具有缓慢衰减效应，这意味着移动性限制似乎为挽救生命做出了有效贡献。还发现，内部流动比跨省流动更重要，减少人员流动对减少过多死亡的典型滞后积极影响约为 14-20 天。一项与西班牙相关的类似分析证实了这一发现，该分析在省一级使用 IgG SARS-Cov-2 抗体筛查研究，而不是过多的死亡统计数据。本研究中采用的相同方法可以很容易地扩展到其他欧洲国家，只要能够获得适当粒度级别的有关病毒传播的可靠数据即可。回顾过去的数据，相对于欧盟成员国爆发的初始阶段，本研究显示了病毒传播与人类流动性的关联程度。这些发现将支持政策制定者制定最佳的数据驱动方法，以摆脱限制，并主要用于构建未来的情景，以防出现新的疫情。这项研究表明，病毒的传播和人类流动在多大程度上是相互关联的。这些发现将支持政策制定者制定最佳的数据驱动方法，以摆脱限制，并主要用于构建未来的情景，以防出现新的疫情。这项研究表明，病毒的传播和人类流动在多大程度上是相互关联的。这些发现将支持政策制定者制定最佳的数据驱动方法，以摆脱限制，并主要用于构建未来的情景，以防出现新的疫情。