Monitoring the COVID-19 epidemic with nationwide telecommunication data

Significance To manage the current epidemic, policymakers need tools that help them in evidence-based decision making. In particular, decision support is needed to assess policy measures by their ability to enforce social distancing. A solution is offered by our work: We use mobility data derived from telecommunication metadata as a proxy for social distancing, and, based on this, we demonstrate how the effect of policy measures can be monitored in a nationwide setting. Compared to the status quo, this provides a clear benefit: Monitoring policy measures through case counts has a substantial time lag, whereas our approach allows for monitoring in near real time. 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.