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Non-pharmaceutical interventions (NPIs) have been crucial in curbing COVID-19in the United States (US). Consequently, relaxing NPIs through a phasedre-opening of the US amid still-high levels of COVID-19 susceptibility couldlead to new epidemic waves. This calls for a COVID-19 early warning system.Here we evaluate multiple digital data streams as early warning indicators ofincreasing or decreasing state-level US COVID-19 activity between January andJune 2020. We estimate the timing of sharp changes in each data stream using asimple Bayesian model that calculates in near real-time the probability ofexponential growth or decay. Analysis of COVID-19-related activity on socialnetwork microblogs, Internet searches, point-of-care medical software, and ametapopulation mechanistic model, as well as fever anomalies captured by smartthermometer networks, shows exponential growth roughly 2-3 weeks prior tocomparable growth in confirmed COVID-19 cases and 3-4 weeks prior to comparablegrowth in COVID-19 deaths across the US over the last 6 months. We furtherobserve exponential decay in confirmed cases and deaths 5-6 weeks afterimplementation of NPIs, as measured by anonymized and aggregated human mobilitydata from mobile phones. Finally, we propose a combined indicator forexponential growth in multiple data streams that may aid in developing an earlywarning system for future COVID-19 outbreaks. These efforts represent aninitial exploratory framework, and both continued study of the predictive powerof digital indicators as well as further development of the statisticalapproach are needed.

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