An open repository of real-time COVID-19 indicators

Abstract

The COVID-19 pandemic presented enormous data challenges in the United States. Policy makers, epidemiological modelers, and health researchers all require up-to-date data on the pandemic and relevant public behavior, ideally at fine spatial and temporal resolution. The COVIDcast API is our attempt to fill this need: Operational since April 2020, it provides open access to both traditional public health surveillance signals (cases, deaths, and hospitalizations) and many auxiliary indicators of COVID-19 activity, such as signals extracted from deidentified medical claims data, massive online surveys, cell phone mobility data, and internet search trends. These are available at a fine geographic resolution (mostly at the county level) and are updated daily. The COVIDcast API also tracks all revisions to historical data, allowing modelers to account for the frequent revisions and backfill that are common for many public health data sources. All of the data are available in a common format through the API and accompanying R and Python software packages. This paper describes the data sources and signals, and provides examples demonstrating that the auxiliary signals in the COVIDcast API present information relevant to tracking COVID activity, augmenting traditional public health reporting and empowering research and decision-making.

Accurately Estimating Total COVID-19 Infections using Information Theory

“…We validate this observation using Facebook’s symptomatic surveillance dataset [49]. Here we plot the estimated symptomatic rate over time and overlay the estimates and standard error from the symptomatic surveillance data (See Figure 4).…”

“…We compare the trends of the M dl P aram Symp and B ase P aram Symp with the symptomatic surveillance results. We focus on trends rather than actual values because the symptomatic rate numbers could be biased [49] (see Methods section for a detailed discussion) and therefore cannot be compared directly with model outputs (like what we have done for serological studies). As seen in the figure, the M dl P aram Symp captures the trends of the surveyed symptomatic rate R ate Symp (represented by black plus symbols) much better than B ase P aram Symp .…”

“…The blue and red curves represent the B ase P aram ’s estimation of symptomatic rate (B ase P aram Symp ), and M dl P aram ’s estimation of symptomatic rate (M dl P aram Symp ) respectively. The black points and the shaded regions are the point estimate with standard error for R ate Symp (the COVID-related symptomatic rates derived from the symptomatic surveillance dataset [49, 51]).…”

“…The survey asks a series of questions on randomly sampled social media (Facebook) users to estimate the percentage of people who have a COVID-like symptoms such as as the fever along with cough or shortness of breath or difficulty breathing on a given day. However, there are several caveats such as they could not cover all symptoms of COVID-19 and these symptoms can be also caused by many other conditions, due to which they are not expected to be unbiased estimates for the true symptomatic rate [49]. Hence we focus on trends instead of the exact numbers.…”

Chimeric forecasting: combining probabilistic predictions from computational models and human judgment

“…Traditional data sources that were used to train models include historical counts of incident cases, deaths, and hospital admissions. A subset of models also train on novel sources of data such as self-reported COVID symptom rates and the rate of visits to a doctor, data related to mobility or contact among individuals, and social media data [34][35][36][37].…”