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

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.

Estimating the Prevalence of COVID-19 in the United States: Three Complementary Approaches (preprint)

Effectively designing and evaluating public health responses to the ongoing COVID-19 pandemic requires accurate estimation of the weekly incidence of COVID-19. Unfortunately, a lack of systematic testing across the United States (US) due to equipment shortages and varying testing strategies has hindered the usefulness of the reported positive COVID-19 case counts. We introduce three complementary approaches to estimate the cumulative incidence of symptomatic COVID-19 during the early outbreak in each state in the US as well as in New York City, using a combination of excess influenza-like illness reports, COVID-19 test statistics, and COVID-19 mortality reports. Instead of relying on an estimate from a single data source or method that may be biased, we provide multiple estimates, each relying on different assumptions and data sources. Across our three approaches, there is a consistent conclusion that estimated state-level COVID-19 symptomatic case counts from March 1 to April 4, 2020 varied from 5 to 50 times greater than the official positive test counts. Nationally, our estimates of COVID-19 symptomatic cases in the US as of April 4 have a likely range of 2.2 to 5.1 million cases, with possibly as high as 8.1 million cases, up to 26 times greater than the cumulative confirmed cases of about 311,000. Extending our method to May 16, 2020, we estimate that cumulative symptomatic incidence ranges from 6.0 to 12.2 million, which compares with 1.5 million positive test counts. Our approaches demonstrate the value of leveraging existing influenza-like-illness surveillance systems during the flu season for measuring the burden of new diseases that share symptoms with influenza-like-illnesses. Our methods may prove useful in assessing the burden of COVID-19 during upcoming flu seasons in the US and other countries with comparable influenza surveillance systems.