Journal
SCIENCE ADVANCES
Volume 7, Issue 10, Pages -Publisher
AMER ASSOC ADVANCEMENT SCIENCE
DOI: 10.1126/sciadv.abd6989
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Funding
- National Institute of General Medical Sciences of the National Institutes of Health [R01GM130668]
- Google Cloud
- Google Cloud Research Credits program
- McGovern Foundation
- Chleck Foundation
- Austrian Science Fund (FWF) [P 29135-N29]
- Austrian Federal Ministry of Climate Action, Environment, Energy, Mobility, Innovation, and Technology (BMK) [878652]
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The study found that changes in digital data stream activity can anticipate increases in confirmed cases and deaths of COVID-19 by 2 to 3 weeks, and confirmed cases and deaths also decrease 2 to 4 weeks after implementation of non-pharmaceutical interventions. Combining health and behavioral data may help in identifying disease activity changes in advance.
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 non-pharmaceutical 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.
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