Due to an unprecedented agreement with the European Mobile Network Operators, the Joint Research Centre of the European Commission was in charge of collecting and analyze mobile positioning data to provide scientific evidence to policy makers to face the COVID-19 pandemic. This work introduces a live anomaly detection system for these high-frequency and high-dimensional data collected at European scale. To take into account the different granularity in time and space of the data, the system has been designed to be simple, yet robust to the data diversity, with the aim of detecting abrupt increase of mobility towards specific regions as well as sudden drops of movements. A web application designed for policy makers, makes possible to visualize the anomalies and perceive the effect of containment and lifting measures in terms of their impact on human mobility as well as spot potential new outbreaks related to large gatherings.
Keywords: Anomaly detection; Data science; High-dimensional time series analysis; Mobile network operator data.
© Japanese Federation of Statistical Science Associations 2021.