  In March 2020, the Austrian government introduced a widespread lock-down in
response to the COVID-19 pandemic. Based on subjective impressions and
anecdotal evidence, Austrian public and private life came to a sudden halt.
Here we assess the effect of the lock-down quantitatively for all regions in
Austria and present an analysis of daily changes of human mobility throughout
Austria using near-real-time anonymized mobile phone data. We describe an
efficient data aggregation pipeline and analyze the mobility by quantifying
mobile-phone traffic at specific point of interest (POI), analyzing individual
trajectories and investigating the cluster structure of the origin-destination
graph. We found a reduction of commuters at Viennese metro stations of over
80\% and the number of devices with a radius of gyration of less than 500 m
almost doubled. The results of studying crowd-movement behavior highlight
considerable changes in the structure of mobility networks, revealed by a
higher modularity and an increase from 12 to 20 detected communities. We
demonstrate the relevance of mobility data for epidemiological studies by
showing a significant correlation of the outflow from the town of Ischgl (an
early COVID-19 hotspot) and the reported COVID-19 cases with an 8-day time lag.
This research indicates that mobile phone usage data permits the
moment-by-moment quantification of mobility behavior for a whole country. We
emphasize the need to improve the availability of such data in anonymized form
to empower rapid response to combat COVID-19 and future pandemics.
