The US COVID-19 Trends and Impact Survey: Continuous real-time measurement of COVID-19 symptoms, risks, protective behaviors, testing, and vaccination

Abstract

The US COVID-19 Trends and Impact Survey (CTIS) is a large, cross-sectional, internet-based survey that has operated continuously since April 6, 2020. By inviting a random sample of Facebook active users each day, CTIS collects information about COVID-19 symptoms, risks, mitigating behaviors, mental health, testing, vaccination, and other key priorities. The large scale of the survey—over 20 million responses in its first year of operation—allows tracking of trends over short timescales and allows comparisons at fine demographic and geographic detail. The survey has been repeatedly revised to respond to emerging public health priorities. In this paper, we describe the survey methods and content and give examples of CTIS results that illuminate key patterns and trends and help answer high-priority policy questions relevant to the COVID-19 epidemic and response. These results demonstrate how large online surveys can provide continuous, real-time indicators of important outcomes that are not subject to public health reporting delays and backlogs. The CTIS offers high value as a supplement to official reporting data by supplying essential information about behaviors, attitudes toward policy and preventive measures, economic impacts, and other topics not reported in public health surveillance systems.

Accurately Estimating Total COVID-19 Infections using Information Theory

“…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]).…”

“…3. Symptomatic surveillance [49,51]: This dataset consists of point estimate Rate Symp (and standard error) of the COVID-related symptomatic rate for each county in the US starting from April 6, 2020 to date. 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.…”

“…On the other hand, MdlParam estimates total infections closer to those estimated by serological studies and better fits reported infections and symptomatic rate trends.Figure 4: MdlParam's estimation of symptomatic rate is more accurate than BaseParam's.The blue and red curves represent the BaseParam's estimation of symptomatic rate (BaseParam Symp ), and MdlParam's estimation of symptomatic rate (MdlParam Symp ) respectively. The black points and the shaded regions are the point estimate with standard error for Rate Symp (the COVID-related symptomatic rates derived from the symptomatic surveillance dataset[49,51]).…”

Smooth multi-period forecasting with application to prediction of COVID-19 cases

“…• confirmed_7dav_incidence_prop: the daily number of new confirmed COVID-19 cases (computed per 100,000 people); • smoothed_cli: the estimated percentage of people with COVID-like illness, as measured by The Delphi Group at Carnegie Mellon University U.S. COVID-19 Trends and Impact Survey (CTIS), in partnership with Facebook [11]; • smoothed_hh_cmnty_cli: the estimated percentage of people reporting illness in their local community, also measured by the Delphi US CTIS.…”

Outdoor mass gathering events and SARS-CoV-2 infection in Catalonia (North-East Spain)

“…Future research could build upon this study by employing electronic health records, insurance claims data, and geographic information systems to track the health impact of public gatherings. Such studies will expedite epidemiologic analyses, characterize the effectiveness of preventive measures implemented in each event, and provide real-time decision support regarding reopening 2 , 9 , 10 in the era of novel variants.…”