Cramer, Estee Y.Huang, YuxinWang, YijinRay, Evan L.Cornell, MatthewBracher, JohannesBrennen, AndreaCastro Rivadeneira, Alvaro J.Gerding, AaronHouse, KatieJayawardena, DasuniKanji, Abdul HannanKhandelwal, AyushLe, KhoaMody, VidhiMody, VrushtiNiemi, JaradStark, ArianeShah, Apurvet al.2023-01-102023-01-1020222022Cramer, E. Y., Huang, Y., Wang, Y., Ray, E. L., Cornell, M., Bracher, J., . . . US COVID-19 Forecast Hub Consortium (2022). “The United States COVID-19 Forecast Hub dataset.” Scientific Data, 9(462). https://doi.org/10.1038/s41597-022-01517-whttps://doi.org/10.1038/s41597-022-01517-whttp://hdl.handle.net/1828/14644Academic 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.enArticle