Name:
s41597-022-01517-w.pdf
Size:
1.573Mb
Format:
PDF
Description:
Final Published Version
Affiliation
Department of Mathematics, University of ArizonaIssue Date
2022
Metadata
Show full item recordPublisher
Nature ResearchCitation
Cramer, E. Y., Huang, Y., Wang, Y., Ray, E. L., Cornell, M., Bracher, J., Brennen, A., Rivadeneira, A. J. C., Gerding, A., House, K., Jayawardena, D., Kanji, A. H., Khandelwal, A., Le, K., Mody, V., Mody, V., Niemi, J., Stark, A., Shah, A., … US COVID-19 Forecast Hub Consortium. (2022). The United States COVID-19 Forecast Hub dataset. Scientific Data, 9(1).Journal
Scientific DataRights
Copyright © The Author(s) 2022. This article is licensed under a Creative Commons Attribution 4.0 International License.Collection Information
This item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at repository@u.library.arizona.edu.Abstract
Academic researchers, government agencies, industry groups, and individuals have produced forecasts at an unprecedented scale during the COVID-19 pandemic. To leverage these forecasts, the United States Centers for Disease Control and Prevention (CDC) partnered with an academic research lab at the University of Massachusetts Amherst to create the US COVID-19 Forecast Hub. Launched in April 2020, the Forecast Hub is a dataset with point and probabilistic forecasts of incident cases, incident hospitalizations, incident deaths, and cumulative deaths due to COVID-19 at county, state, and national, levels in the United States. Included forecasts represent a variety of modeling approaches, data sources, and assumptions regarding the spread of COVID-19. The goal of this dataset is to establish a standardized and comparable set of short-term forecasts from modeling teams. These data can be used to develop ensemble models, communicate forecasts to the public, create visualizations, compare models, and inform policies regarding COVID-19 mitigation. These open-source data are available via download from GitHub, through an online API, and through R packages. © 2022, The Author(s).Note
Open access journalISSN
2052-4463PubMed ID
35915104Version
Final published versionae974a485f413a2113503eed53cd6c53
10.1038/s41597-022-01517-w
Scopus Count
Collections
Except where otherwise noted, this item's license is described as Copyright © The Author(s) 2022. This article is licensed under a Creative Commons Attribution 4.0 International License.