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US COVID-19 Forecast Hub Consortium [Author]; Cramer, Estee Y. [Author]; Huang, Yuxin [Author]; Wang, Yijin [Author]; Ray, Evan L. [Author]; Cornell, Matthew [Author]; Bracher, Johannes [Author]; Brennen, Andrea [Author]; Rivadeneira, Alvaro J. Castro [Author]; Gerding, Aaron [Author]; House, Katie [Author]; Jayawardena, Dasuni [Author]; Kanji, Abdul Hannan [Author]; Khandelwal, Ayush [Author]; Le, Khoa [Author]; Mody, Vidhi [Author]; Mody, Vrushti [Author]; Niemi, Jarad [Author]; Stark, Ariane [Author]; Shah, Apurv [Author]; Wattanchit, Nutcha [Author]; Zorn, Martha W. [Author]; Reich, Nicholas G. [Author]; Gneiting, Tilmann [Author]; [...]

The United States COVID-19 Forecast Hub dataset

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  • Media type: Text; E-Article
  • Title: The United States COVID-19 Forecast Hub dataset
  • Contributor: US COVID-19 Forecast Hub Consortium [Author]; Cramer, Estee Y. [Author]; Huang, Yuxin [Author]; Wang, Yijin [Author]; Ray, Evan L. [Author]; Cornell, Matthew [Author]; Bracher, Johannes [Author]; Brennen, Andrea [Author]; Rivadeneira, Alvaro J. Castro [Author]; Gerding, Aaron [Author]; House, Katie [Author]; Jayawardena, Dasuni [Author]; Kanji, Abdul Hannan [Author]; Khandelwal, Ayush [Author]; Le, Khoa [Author]; Mody, Vidhi [Author]; Mody, Vrushti [Author]; Niemi, Jarad [Author]; Stark, Ariane [Author]; Shah, Apurv [Author]; Wattanchit, Nutcha [Author]; Zorn, Martha W. [Author]; Reich, Nicholas G. [Author]; Gneiting, Tilmann [Author]; [...]
  • Published: Nature Research, 2022-08-17
  • Published in: Scientific Data, 9 (1), Art.-Nr.: 462 ; ISSN: 2052-4463, 2052-4436
  • Language: English
  • DOI: https://doi.org/10.5445/IR/1000150017; https://doi.org/10.1038/s41597-022-01517-w
  • ISSN: 2052-4463; 2052-4436
  • Keywords: Mathematics
  • Origination:
  • Footnote: Diese Datenquelle enthält auch Bestandsnachweise, die nicht zu einem Volltext führen.
  • Description: 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.
  • Access State: Open Access