Abstract
In response to the novel coronavirus disease (COVID-19), governments have introduced severe policy measures with substantial effects on human behavior. Here, we perform a large-scale, spatio-temporal analysis of human mobility during the COVID-19 epidemic. We derive human mobility from anonymized, aggregated telecommunication data in a nationwide setting (Switzerland; February 10–April 26, 2020), consisting of ∼1.5 billion trips. In comparison to the same time period from 2019, human movement in Switzerland dropped by 49.1 %. The strongest reduction is linked to bans on gatherings of more than 5 people, which is estimated to have decreased mobility by 24.9 %, followed by venue closures (stores, restaurants, and bars) and school closures. As such, human mobility at a given day predicts reported cases 7–13 days ahead. A 1 % reduction in human mobility predicts a 0.88–1.11 % reduction in daily reported COVID-19 cases. When managing epidemics, monitoring human mobility via telecommunication data can support public decision-makers in two ways. First, it helps in assessing policy impact; second, it provides a scalable tool for near real-time epidemic surveillance, thereby enabling evidence-based policies.
Dokumententyp: | Zeitschriftenartikel |
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Keywords: | Artificial Intelligence; AI;COVID-19; epidemiology; human mobility; telecommunication data; Bayesian modeling, Künstliche Intelligenz; KI |
Fakultät: | Betriebswirtschaft > Institute of Artificial Intelligence (AI) in Management |
Themengebiete: | 000 Informatik, Informationswissenschaft, allgemeine Werke > 000 Informatik, Wissen, Systeme |
URN: | urn:nbn:de:bvb:19-epub-94953-2 |
Sprache: | Englisch |
Dokumenten ID: | 94953 |
Datum der Veröffentlichung auf Open Access LMU: | 08. Mrz. 2023, 07:58 |
Letzte Änderungen: | 08. Mrz. 2023, 07:58 |