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The United States COVID-19 Forecast Hub dataset
- Cramer, Estee Y;
- Huang, Yuxin;
- Wang, Yijin;
- Ray, Evan L;
- Cornell, Matthew;
- Bracher, Johannes;
- Brennen, Andrea;
- Rivadeneira, Alvaro J Castro;
- Gerding, Aaron;
- House, Katie;
- Jayawardena, Dasuni;
- Kanji, Abdul Hannan;
- Khandelwal, Ayush;
- Le, Khoa;
- Mody, Vidhi;
- Mody, Vrushti;
- Niemi, Jarad;
- Stark, Ariane;
- Shah, Apurv;
- Wattanchit, Nutcha;
- Zorn, Martha W;
- Reich, Nicholas G
- et al.
Published Web Location
https://doi.org/10.1038/s41597-022-01517-wAbstract
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.
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