The United States COVID-19 Forecast Hub dataset
dc.contributor.author | Cramer, Estee Y. | |
dc.contributor.author | Huang, Yuxin | |
dc.contributor.author | Wang, Yijin | |
dc.contributor.author | Ray, Evan L. | |
dc.contributor.author | Cornell, Matthew | |
dc.contributor.author | Bracher, Johannes | |
dc.contributor.author | Brennen, Andrea | |
dc.contributor.author | Rivadeneira, Alvaro J. Castro | |
dc.contributor.author | Gerding, Aaron | |
dc.contributor.author | House, Katie | |
dc.contributor.author | Jayawardena, Dasuni | |
dc.contributor.author | Kanji, Abdul Hannan | |
dc.contributor.author | Khandelwal, Ayush | |
dc.contributor.author | Le, Khoa | |
dc.contributor.author | Mody, Vidhi | |
dc.contributor.author | Mody, Vrushti | |
dc.contributor.author | Niemi, Jarad | |
dc.contributor.author | Stark, Ariane | |
dc.contributor.author | Shah, Apurv | |
dc.contributor.author | Wattanchit, Nutcha | |
dc.contributor.author | Zorn, Martha W. | |
dc.contributor.author | Reich, Nicholas G. | |
dc.contributor.author | US COVID-19 Forecast Hub Consortium | |
dc.contributor.department | Computer Science, Luddy School of Informatics, Computing, and Engineering | |
dc.date.accessioned | 2024-05-14T20:27:25Z | |
dc.date.available | 2024-05-14T20:27:25Z | |
dc.date.issued | 2022-08-01 | |
dc.description.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. | |
dc.eprint.version | Final published version | |
dc.identifier.citation | 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., … Reich, N. G. (2022). The United States COVID-19 Forecast Hub dataset. Scientific Data, 9(1), 462. https://doi.org/10.1038/s41597-022-01517-w | |
dc.identifier.uri | https://hdl.handle.net/1805/40745 | |
dc.language.iso | en_US | |
dc.publisher | Springer | |
dc.relation.isversionof | 10.1038/s41597-022-01517-w | |
dc.relation.journal | Scientific Data | |
dc.rights | Attribution 4.0 International | en |
dc.rights.uri | https://creativecommons.org/licenses/by/4.0 | |
dc.source | Publisher | |
dc.subject | Databases | |
dc.subject | Viral infection | |
dc.subject | Software | |
dc.subject | Scientific data | |
dc.subject | Computer science | |
dc.title | The United States COVID-19 Forecast Hub dataset | |
dc.type | Article |