The contribution of telco data to fight the COVID-19 pandemic: Experience of Telefonica throughout its footprint

Abstract

The COVID-19 pandemic is a global challenge for humanity, in which a large number of resources are invested to develop effective vaccines and treatments. At the same time, governments try to manage the spread of the disease while alleviating the strong impact derived from the slowdown in economic activity. Governments were forced to impose strict lockdown measures to tackle the pandemic. This significantly changed people’s mobility and habits, subsequently impacting the economy. In this context, the availability of tools to effectively monitor and quantify mobility was key for public institutions to decide which policies to implement and for how long. Telefonica has promoted different initiatives to offer governments mobility insights throughout many of the countries where it operates in Europe and Latin America. Mobility indicators with high spatial granularity and frequency of updates were successfully deployed in different formats. However, Telefonica faced many challenges (not only technical) to put these tools into service in a short timing: from reducing latency in insights to ensuring the security and privacy of information. In this article, we provide details on how Telefonica engaged with governments and other stakeholders in different countries as a response to the pandemic. We also cover the challenges faced and the shared learnings from Telefonica’s experience in those countries.

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

“…Because both temporal and spatial information is available in CDRs, these data have become one of the most important data sources for research on human mobility [15,30,35,8,21]. 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]).…”

“…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.…”