An Early Warning Approach to Monitor COVID-19 Activity with Multiple Digital Traces in Near Real-Time
| Autor: | Bernd Resch, Andre T. Nguyen, Backtosch Mustafa, Fred Lu, Peter Huybers, Clemens Havas, Alessandro Vespignani, William P. Hanage, Mauricio Santillana, Nicholas B. Link, P. Liautaud, Leonardo Clemente, Andreas Petutschnig, Nicole E. Kogan, Matteo Chinazzi, Jessica T. Davis, Justin Kaashoek |
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| Rok vydání: | 2020 |
| Předmět: |
Time Factors
Coronavirus disease 2019 (COVID-19) Epidemiology Psychological intervention Article Disease Outbreaks 03 medical and health sciences 0302 clinical medicine Environmental health Humans Medicine 030212 general & internal medicine Health and Medicine health care economics and organizations Research Articles Probability 030304 developmental biology 0303 health sciences Multidisciplinary Warning system SARS-CoV-2 business.industry Data stream mining COVID-19 Outbreak SciAdv r-articles social sciences United States Coronavirus Behavioral data Epidemiological Monitoring Early warning system business Research Article |
| Zdroj: | Science Advances ArXiv |
| ISSN: | 2331-8422 |
| Popis: | Multiple digital data streams forecast COVID-19 activity weeks before traditional epidemiological surveillance. 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. |
| Databáze: | OpenAIRE |
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