An early warning approach to monitor COVID-19 activity with multiple digital traces in near real time

Sci Adv. 2021 Mar 5;7(10):eabd6989. doi: 10.1126/sciadv.abd6989. Print 2021 Mar.


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.

Publication types

  • Research Support, N.I.H., Extramural
  • Research Support, Non-U.S. Gov't

MeSH terms

  • COVID-19 / diagnosis*
  • COVID-19 / epidemiology*
  • COVID-19 / virology
  • Disease Outbreaks
  • Epidemiological Monitoring*
  • Humans
  • Probability
  • SARS-CoV-2 / physiology*
  • Time Factors
  • United States / epidemiology