Epidemiology

Pathology

Disease

Location data

Coronavirus disease 2019 (COVID-19)

Infectious disease (medical specialty)

Outbreak

Psychiatry

Philosophy

Virology

Psychological intervention

Data science

Early warning system

Phone

Warning system

2019-20 coronavirus outbreak

Computer science

Linguistics

Medical emergency

Real-time computing

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)

Medicine

Transmission (telecommunications)

Telecommunications

Environmental health

Science Advances

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.

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