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.
Behavioral gender differences have been found for a wide range of human activities including the way people communicate, move, provision themselves, or organize leisure activities. Using mobile phone data from 1.2 million devices in Austria (15% of the population) across the first phase of the COVID-19 crisis, we quantify gender-specific patterns of communication intensity, mobility, and circadian rhythms. We show the resilience of behavioral patterns with respect to the shock imposed by a strict nation-wide lock-down that Austria experienced in the beginning of the crisis with severe implications on public and private life. We find drastic differences in gender-specific responses during the different phases of the pandemic. After the lock-down gender differences in mobility and communication patterns increased massively, while circadian rhythms tended to synchronize. In particular, women had fewer but longer phone calls than men during the lock-down. Mobility declined massively for both genders, however, women tended to restrict their movement stronger than men. Women showed a stronger tendency to avoid shopping centers and more men frequented recreational areas. After the lock-down, males returned back to normal quicker than women; young age-cohorts return much quicker. Differences are driven by the young and adolescent population. An age stratification highlights the role of retirement on behavioral differences. We find that the length of a day of men and women is reduced by 1 h. We interpret and discuss these findings as signals for underlying social, biological and psychological gender differences when coping with crisis and taking risks.
Despite their importance, federal grant systems often need more clarity regarding cost-effectiveness, lack of transparency, and slow feedback cycles. Sports funding systems aimed at improving child health and contributing to sustainable development goals are incredibly challenging due to their heterogeneity in stakeholders and regional aspects. Here, we analyze how we tackled these challenges in a transdisciplinary EU project in Austria, targeting the use of agent-based modeling for evidence-based policymaking in a co-creation process with policy stakeholders in the domain of federal sports grants to improve the health and well-being of children and youth. The initial and executed set of procedures is described, along with lessons learned during the project’s lifetime. These lessons derive a framework that provides an adapted set of processes, supporting methods, and critical decision points for an improved use of transdisciplinarity. In addition, the steps of the developed framework are combined with essential aspects of knowledge integration, following the main phases of the policy cycle and providing suggestions for required skills and competencies for capacity building concerning implementing the developed framework in the public sector. Our results show that the combination of transdisciplinarity, human-centered policymaking, and sports, supported by cutting-edge technologies such as agent-based modeling, can achieve significantly better results than a pure disciplinary approach and generate positive spill-over effects.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.