Monitoring the COVID-19 epidemic with nationwide telecommunication data

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.

Impact of the stringency of lockdown measures on covid-19: A theoretical model of a pandemic

“…How effective have lockdowns been at reducing the covid-19 infection and mortality rates? Now that many countries have had more than a year under lockdown, it is possible to study empirically the effectiveness of lockdowns in reducing the spread of covid-19 and the related mortality [ 1 – 5 ]. These lockdowns are non-pharmaceutical interventions of contact among persons within or between populations including restricting travel, closing schools, prohibiting public gatherings, requiring workplace closures, all designed to slow the contagion of the virus.…”

“…These lockdowns are non-pharmaceutical interventions of contact among persons within or between populations including restricting travel, closing schools, prohibiting public gatherings, requiring workplace closures, all designed to slow the contagion of the virus. In a recent (published June 2021) large-scale study employing a spatiotemporal analysis of human mobility during the COVID-19 epidemic in Switzerland, it was found that a 1% reduction in human mobility predicted a 0.88 to 1.11% reduction in daily reported COVID-19 cases [ 1 ]. But even as the pandemic has abated in some countries with slowing infection and mortality, it has sped up in some African countries, Columbia, and Brazil [ 1 , 2 ].…”

“…In a recent (published June 2021) large-scale study employing a spatiotemporal analysis of human mobility during the COVID-19 epidemic in Switzerland, it was found that a 1% reduction in human mobility predicted a 0.88 to 1.11% reduction in daily reported COVID-19 cases [ 1 ]. But even as the pandemic has abated in some countries with slowing infection and mortality, it has sped up in some African countries, Columbia, and Brazil [ 1 , 2 ]. The coronavirus appeared in China and then spread to Europe, first in Italy then Spain and subsequently to other countries [ 6 ].…”

Surveillance of SARS-CoV-2 transmission in educational institutions, August to December 2020, Germany

“…Based on data from the first wave of COVID-19, a number of modelling studies provide inconclusive guidance to policy makers. While two publications, one from several countries and one from Switzerland [ 9 , 10 ], concluded that school closures contributed markedly to the reduction of SARS-CoV-2 transmission and individual mobility, respectively, two other studies, one using cross-country data and one from Japan rated school closures among the least effective measures to reduce COVID-19 incidence rates [ 11 , 12 ]. Accordingly, a recent review on SARS-CoV-2 setting-specific transmission rates concluded that there is ‘limited data to explore transmission patterns in […] schools […], highlighting the need for further research in such settings’ [ 13 ].…”

Quantifying behavior change during the first year of the COVID-19 pandemic in the United States

“…Self-reported behavior during the pandemic is expensive to collect and might not be reliable. Conversely, mobile device (i.e., smartphone) location data can be used to quantitatively capture the movement of people [Kishore et al 2020, Persson et al 2021], but they are not well-suited to capturing small social gatherings, which may play a large role in the spread of SARS-CoV-2 [Tupper et al 2020, Whaley et al 2021]. However, digital invitation services can be used to collect data on the size and frequency of social gatherings.…”