tetano
Editor, Senior Moderator
Proc Natl Acad Sci U S A
. 2021 Jun 29;118(26):e2100664118.
doi: 10.1073/pnas.2100664118.
Monitoring the COVID-19 epidemic with nationwide telecommunication data
Joel Persson[SUP] 1 [/SUP], Jurriaan F Parie[SUP] 2 [/SUP], Stefan Feuerriegel[SUP] 2 [/SUP]
Affiliations
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, spatiotemporal analysis of human mobility during the COVID-19 epidemic. We derive human mobility from anonymized, aggregated telecommunication data in a nationwide setting (Switzerland; 10 February to 26 April 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 five people, which are 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 to 13 d ahead. A 1% reduction in human mobility predicts a 0.88 to 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.
Keywords: Bayesian modeling; COVID-19; epidemiology; human mobility; telecommunication data.
. 2021 Jun 29;118(26):e2100664118.
doi: 10.1073/pnas.2100664118.
Monitoring the COVID-19 epidemic with nationwide telecommunication data
Joel Persson[SUP] 1 [/SUP], Jurriaan F Parie[SUP] 2 [/SUP], Stefan Feuerriegel[SUP] 2 [/SUP]
Affiliations
- PMID: 34162708
- DOI: 10.1073/pnas.2100664118
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, spatiotemporal analysis of human mobility during the COVID-19 epidemic. We derive human mobility from anonymized, aggregated telecommunication data in a nationwide setting (Switzerland; 10 February to 26 April 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 five people, which are 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 to 13 d ahead. A 1% reduction in human mobility predicts a 0.88 to 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.
Keywords: Bayesian modeling; COVID-19; epidemiology; human mobility; telecommunication data.