RESUMEN
The U.S. 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. Significance statementThe U.S. COVID-19 Trends and Impact Survey (CTIS) has operated continuously since April 6, 2020, collecting over 20 million responses. The largest public health survey ever conducted in the United States, CTIS was designed to facilitate detailed demographic and geographic analyses, track trends over time, and accommodate rapid response to emerging priorities. Using examples of CTIS results illuminating trends in symptoms, risks, mitigating behaviors, testing and vaccination in relation to evolving high-priority policy questions over 12 months of the pandemic, we illustrate the value of online surveys for tracking patterns and trends in COVID outcomes as an adjunct to official reporting, and showcase unique insights that would not be visible through traditional public health reporting.
RESUMEN
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 de-identified 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 is 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.