Monitoring the COVID-19 epidemic with nationwide telecommunication data

TL;DR

This study analyzes nationwide telecommunication data in Switzerland to track human mobility changes during COVID-19, demonstrating how mobility reductions correlate with case declines and supporting epidemic management.

Contribution

It introduces a large-scale spatio-temporal analysis of telecommunication data to monitor COVID-19 mobility patterns and assess policy impacts in real-time.

Findings

Mobility dropped by 49.1% during the epidemic.

Mobility reductions predict case declines 7-13 days ahead.

Monitoring mobility supports policy assessment and epidemic surveillance.

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.