Country-wide Mobility Changes Observed Using Mobile Phone Data During COVID-19 Pandemic

Abstract

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 d ata a ggregation pipeline and analyze the mobility by quantifying mobile-phone traffic at specific point of interests (POIs), 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 f rom t he t own o f I schgl ( an e arly 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.Index Terms-big-data, call-data-records (CDR) Apache-Spark, graph-analysis, mobility This work was funded by the Austrian Research Promotion Agency (FFG) under project 857136, the Austrian Science Fund FWF under project P29252, the WWTF under project COV 20-017 and COV20-035 and the Medizinisch-Wissenschaftlicher Fonds des Buergermeisters der Bundeshauptstadt Wien under project CoVid004.

Socio-economic determinants of mobility responses during the first wave of COVID-19 in Italy: from provinces to neighbourhoods

“…Since the very beginning of the COVID-19 pandemic, changes in human mobility ensuing the non-pharmaceutical interventions (NPIs) adopted in many countries worldwide have been measured through the analysis of mobile phone data [6]. To mention a few examples, previous studies have investigated changes in human movements through mobile phone data in Austria, China, Japan, the UK, Germany and the USA [7][8][9][10]. Several of these studies suggested that mobility restrictions unevenly impact different socio-economic strata and that income inequalities are associated with a different capacity to afford prolonged social distancing [11][12][13].…”

Synthetic Reproduction and Augmentation of COVID-19 Case Reporting Data by Agent-Based Simulation

“…By successive improvement in the simulation of physical contact behavior and disease transmission, we hope to increase the insight into actual transmission paths and in the occurrence of infection clusters (Leclerc et al 2020). In particular, by interlacing of statistical information on social relations and social structuring (Schneckenreither and Popper 2017) and on geographic mobility patterns (Heiler et al 2020) we infer and dynamically reproduce transmission trajectories as observed or anticipated in reality. We intend to provide synthetic data on transmission clusters including superspreading events in combination with geographic and socio-structural information in the future.…”

“…Besides the data we process for parameterizing and calibrating the model, we use additional data to validate the model dynamics by comparing the corresponding outcomes of the model with the numbers reported in this independent data. This data includes time-series of the hospitalization, severity and fatality in cases (Federal Ministry of Internal Affairs 2020; Federal Ministry of Social Affairs, Health, Care and Consumer Protection 2020a) as well as the age distribution in confirmed cases (Federal Ministry of Social Affairs, Health, Care and Consumer Protection 2020a) and mobility data provided by mobile phone companies (Heiler et al 2020). Each of these data sets is used to validate separate sub-aspects (modules) of our model.…”

Varieties of mobility measures: Comparing survey and mobile phone data during the COVID-19 pandemic

“…To mitigate the effect of data skew, we use the daily median radius of gyration to calculate weekly aggregated mobility averages. For further details on the data and the calculation of ROG refer to Heiler et al (2020). As the GSM data includes information about the age group and gender of its users, we also compare GSM and survey self-reports within and between important subgroups.…”