  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.

Applications;Populations and Evolution