An early warning approach to monitor COVID-19 activity with multiple digital traces in near real time
Nicole Kogan, Leonardo Clemente, Parker Liautaud, Justin Kaashoek, Nicholas Link, André T. Nguyen, Fred Lu, Peter Huybers, Bernd Resch, Clemens Havas, Andreas Petutschnig, Jessica T. Davis, Matteo Chinazzi, Backtosch Mustafa, William P. Hanage, Alessandro Vespignani, Mauricio Santillana
Abstract
Given still-high levels of coronavirus disease 2019 (COVID-19) susceptibility and inconsistent transmission-containing strategies, outbreaks have continued to emerge across the United States. Until effective vaccines are widely deployed, curbing COVID-19 will require carefully timed nonpharmaceutical interventions (NPIs). A COVID-19 early warning system is vital for this. Here, we evaluate digital data streams as early indicators of state-level COVID-19 activity from 1 March to 30 September 2020. We observe that increases in digital data stream activity anticipate increases in confirmed cases and deaths by 2 to 3 weeks. Confirmed cases and deaths also decrease 2 to 4 weeks after NPI implementation, as measured by anonymized, phone-derived human mobility data. We propose a means of harmonizing these data streams to identify future COVID-19 outbreaks. Our results suggest that combining disparate health and behavioral data may help identify disease activity changes weeks before observation using traditional epidemiological monitoring.