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

Abstract

The rapid spread of COVID-19 infections on a global level has highlighted the need for accurate, transparent and timely information regarding collective mobility patterns to inform de-escalation strategies as well as to provide forecasting capacity for re-escalation policies aiming at addressing further waves of the virus. Such information can be extracted using aggregate anonymized data from innovative sources such as mobile positioning data. This paper presents lessons learnt and results of a unique Business-to-Government initiative between several mobile network operators in Europe and the European Commission. Mobile positioning data have supported policy-makers and practitioners with evidence and data-driven knowledge to understand and predict the spread of the disease, the effectiveness of the containment measures, their socio-economic impacts while feeding scenarios at European Union scale and in a comparable way across countries. The challenges of these data sharing initiative are not limited to data quality, harmonization, and comparability across countries, however important they are. Equally essential aspects that need to be addressed from the onset are related to data privacy, security, fundamental rights, and commercial sensitivity.

Analysis of the impact of non-compulsory measures on human mobility in Japan during the COVID-19 pandemic

“…For example, the European Commission asked European Mobile Network Operators to share fully anonymized and aggregated mobility data to improve the quality of modeling and forecasting for the pandemic at EU level ( European Commission, 2020 ). These data have supported policy-makers and practitioners with evidence and data-driven knowledge to understand and predict the spread of the disease, the effectiveness of the containment measures, their socio-economic impacts while feeding scenarios at European Union scale and in a comparable way across countries ( Iacus et al, 2021 ; Vespe et al, 2021 ). Because most mobile-device data are aggregate data of population in a target grid cell, information that can provide more detailed mobility patterns is not available.…”

Differentially private multivariate time series forecasting of aggregated human mobility with deep learning: Input or gradient perturbation?

“…Indeed, human mobility analysis can benefit individuals and society enabling local authorities to improve urban planning, enhance the transportation system, and assist in decision-making to respond to critical situations (e.g., natural disasters [21,28]). Within a recent context, due to the ongoing Coronavirus Disease 2019 (COVID-19) pandemic [56], on 8 April 2020, the European Commission asked MNOs in the European region to share anonymized and aggregated mobility data to help to fight the outbreak [54,25], which has also been done in other parts of the world as described in [8]. This vision is also shared by, e.g., Buckee et al [15] and Oliver et al [39], which highlight the importance of aggregate mobility data and mobile phone data like CDRs for fighting the COVID-19 outbreak.…”

“…As one can notice, there is a clear difference between the first period of analysis (low mobility activity) and the second one (high mobility activity). This type of mobility analysis provides important insights on mobility patterns for public authorities and policymakers, for example [54,8].…”

“…This way, MNOs tend to publish aggregated mobility data [8,57,54,53,40], e.g., the number of users by coarse location at a given timestamp, which, in other words, represents a multivariate time series dataset that can be used for predictive mobility [30]. Nevertheless, as recent studies have shown, even aggregated mobility data can be subject to membership inference attacks [42,43] and users' trajectory recovery attack [53,57], thus requiring proper sanitization.…”

“…It is worth mentioning that human mobility forecast information is of great importance for public and/or private organizations to identify strategies to propose better decision-making solutions for society [55,35,8,39,21,28,15,54]. Therefore, in this paper, extensive experiments were carried out with a real-world mobility dataset collected by Orange [40] on analyzing CDRs in 6 coarse regions in Paris, France, which we publish openly as an open mobility dataset.…”