ArticleProceedings of the National Academy of Sciences of the United States of America2021
An open repository of real-time COVID-19 indicators.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 34 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
34 citing papers in PubMed.
- Recommendations for the analysis of leading indicators in epidemic surveillance.PLOS digital health · 2026Review
- Start from the end: Policy exploration to inform effective and consistent interventions applied to COVID-19 in St. Louis.PNAS nexus · 2026Article
- Forecastability of infectious disease time series: are some seasons and pathogens intrinsically more difficult to forecast?PLoS computational biology · 2026Article
- Data generation and modeling during COVID-19: utility, barriers, and priorities for future investments in public health response.Frontiers in public health · 2026Article
- Real-time forecasting of data revisions in epidemic surveillance streams.PLoS computational biology · 2025Article
- Real-time forecasting of data revisions in epidemic surveillance streams.PLoS computational biology · 2025Article
- Real-time forecasting of data revisions in epidemic surveillance streams.PLoS computational biology · 2025Article
- When data disappear: public health pays as US policy strays.The Lancet. Digital health · 2025Review
- Article
- Predictive Modeling for Pandemic Forecasting: A COVID-19 Study in New Zealand and Partner Countries.International journal of environmental research and public health · 2025Article
- Graph-Based Prediction of Spatio-Temporal Vaccine Hesitancy From Insurance Claims Data.IEEE access : practical innovations, open solutions · 2025Article
- Adaptive behaviour during epidemics: a social risk appraisal approach to modelling dynamics.Journal of the Royal Society, Interface · 2025Article
- Caregiver worry about COVID-19 as a predictor of social mitigation behaviours and SARS-CoV-2 infection in a 12-city U.S. surveillance study of households with children.Preventive medicine reports · 2025Article
- Article
- Exploring the Big Data Paradox for various estimands using vaccination data from the global COVID-19 Trends and Impact Survey (CTIS).Science advances · 2024Article
- Impacts of human mobility on the citywide transmission dynamics of 18 respiratory viruses in pre- and post-COVID-19 pandemic years.Nature communications · 2024Article
- PANDEM-Source, a tool to collect or generate surveillance indicators for pandemic management: a use case with COVID-19 data.Frontiers in public health · 2024Article
- Infectious disease surveillance needs for the United States: lessons from Covid-19.Frontiers in public health · 2024Review
- Smooth multi-period forecasting with application to prediction of COVID-19 cases.Journal of computational and graphical statistics : a joint publication of American Statistical Association, Institute of Mathematical Statistics, Interface Foundation of North America · 2024Article
- Assessing the utility of COVID-19 case reports as a leading indicator for hospitalization forecasting in the United States.Epidemics · 2023Article
Corrections and comments
- Erratum issued
Authors and funding
67 authors.
Funding
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.