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.date.accessioned | 2023-08-01T14:16:19Z | |
| dc.date.available | 2023-08-01T14:16:19Z | |
| dc.date.issued | 2022-08 | |
| dc.date.updated | 2023-08-01T14:16:18Z | |
| 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.identifier | 10.1038/s41597-022-01517-w | |
| dc.identifier.issn | 2052-4463 | |
| dc.identifier.issn | 2052-4463 | |
| dc.identifier.uri | ||
| dc.language | eng | |
| dc.publisher | Springer Science and Business Media LLC | |
| dc.relation.ispartof | Scientific data | |
| dc.relation.isversionof | 10.1038/s41597-022-01517-w | |
| dc.subject | US COVID-19 Forecast Hub Consortium | |
| dc.subject | Humans | |
| dc.subject | Forecasting | |
| dc.subject | United States | |
| dc.subject | Pandemics | |
| dc.subject | Centers for Disease Control and Prevention, U.S. | |
| dc.subject | COVID-19 | |
| dc.title | The United States COVID-19 Forecast Hub dataset. | |
| dc.type | Journal article | |
| pubs.begin-page | 462 | |
| pubs.issue | 1 | |
| pubs.organisational-group | Duke | |
| pubs.organisational-group | Pratt School of Engineering | |
| pubs.organisational-group | School of Medicine | |
| pubs.organisational-group | Trinity College of Arts & Sciences | |
| pubs.organisational-group | Basic Science Departments | |
| pubs.organisational-group | Biostatistics & Bioinformatics | |
| pubs.organisational-group | Electrical and Computer Engineering | |
| pubs.organisational-group | Computer Science | |
| pubs.organisational-group | Biostatistics & Bioinformatics, Division of Integrative Genomics | |
| pubs.publication-status | Published | |
| pubs.volume | 9 |
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