About: An Early Warning Approach to Monitor COVID-19 Activity with Multiple Digital Traces in Near Real-Time

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About: An Early Warning Approach to Monitor COVID-19 Activity with Multiple Digital Traces in Near Real-Time Goto Sponge NotDistinct Permalink

An Entity of Type : schema:ScholarlyArticle, within Data Space : covidontheweb.inria.fr associated with source document(s)

Attributes	Values
type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
has title	An Early Warning Approach to Monitor COVID-19 Activity with Multiple Digital Traces in Near Real-Time
Creator	Santillana, Mauricio Vespignani, Alessandro Davis, Jessica Lu, Fred Hanage, William Chinazzi, Matteo Clemente, Leonardo Link, Nicholas Nguyen, Andre Huybers, Peter Liautaud, Parker Kogan, Nicole Havas, Clemens Kaashoek, Justin Mustafa, Backtosch Petutschnig, Andreas Resch, Bernd
Source	ArXiv
abstract	Non-pharmaceutical interventions (NPIs) have been crucial in curbing COVID-19 in the United States (US). Consequently, relaxing NPIs through a phased re-opening of the US amid still-high levels of COVID-19 susceptibility could lead to new epidemic waves. This calls for a COVID-19 early warning system. Here we evaluate multiple digital data streams as early warning indicators of increasing or decreasing state-level US COVID-19 activity between January and June 2020. We estimate the timing of sharp changes in each data stream using a simple Bayesian model that calculates in near real-time the probability of exponential growth or decay. Analysis of COVID-19-related activity on social network microblogs, Internet searches, point-of-care medical software, and a metapopulation mechanistic model, as well as fever anomalies captured by smart thermometer networks, shows exponential growth roughly 2-3 weeks prior to comparable growth in confirmed COVID-19 cases and 3-4 weeks prior to comparable growth in COVID-19 deaths across the US over the last 6 months. We further observe exponential decay in confirmed cases and deaths 5-6 weeks after implementation of NPIs, as measured by anonymized and aggregated human mobility data from mobile phones. Finally, we propose a combined indicator for exponential growth in multiple data streams that may aid in developing an early warning system for future COVID-19 outbreaks. These efforts represent an initial exploratory framework, and both continued study of the predictive power of digital indicators as well as further development of the statistical approach are needed.
has issue date	2020-07-01(xsd:dateTime)
has license	arxiv
sha1sum (hex)	d5317d87ea62c5d487f54f9c5e1f427ee2a5ad66
resource representing a document's title	An Early Warning Approach to Monitor COVID-19 Activity with Multiple Digital Traces in Near Real-Time
resource representing a document's body	covid:d5317d87ea62c5d487f54f9c5e1f427ee2a5ad66#body_text
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