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

TL;DR

This study evaluates multiple digital data streams as early indicators of COVID-19 activity changes in the US, demonstrating that digital signals can predict case and death surges weeks in advance, aiding early warning efforts.

Contribution

It introduces a Bayesian model to estimate the timing of COVID-19 activity changes using diverse digital traces, providing a framework for real-time early warning systems.

Findings

Digital signals predict COVID-19 growth 2-3 weeks in advance

Exponential decay in cases follows NPI implementation by 5-6 weeks

Combined indicators can improve early outbreak detection

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.