Scientific Data (Aug 2022)

The United States COVID-19 Forecast Hub dataset

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

DOI
https://doi.org/10.1038/s41597-022-01517-w
Journal volume & issue
Vol. 9, no. 1
pp. 1 – 15

Abstract

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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.