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The United States COVID-19 Forecast Hub dataset

Journal Article · · Scientific Data
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  1. University of Massachusetts Amherst, Amherst, MA (United States); US COVID-19 Forecast Hub Consortium et al.

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.

Research Organization:
Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)
Sponsoring Organization:
USDOE Laboratory Directed Research and Development (LDRD) Program; Centers for Disease Control and Prevention (CDC); National Institutes of General Medical Sciences (NIGMS); National Institutes of Health (NIH); National Science Foundation (NSF)
Contributing Organization:
US COVID-19 Forecast Hub Consortium
Grant/Contract Number:
89233218CNA000001
OSTI ID:
1975006
Alternate ID(s):
OSTI ID: 2481560
Report Number(s):
LA-UR--21-29146; LA-UR--24-31533
Journal Information:
Scientific Data, Journal Name: Scientific Data Journal Issue: 1 Vol. 9; ISSN 2052-4463
Publisher:
Nature Publishing GroupCopyright Statement
Country of Publication:
United States
Language:
English

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Comparing trained and untrained probabilistic ensemble forecasts of COVID-19 cases and deaths in the United States journal July 2022
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Carceral-community epidemiology, structural racism, and COVID-19 disparities journal May 2021
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“A 30% Chance of Rain Tomorrow”: How Does the Public Understand Probabilistic Weather Forecasts? journal June 2005
Accuracy of real-time multi-model ensemble forecasts for seasonal influenza in the U.S. journal November 2019
Evaluating epidemic forecasts in an interval format journal February 2021
Modeling of Future COVID-19 Cases, Hospitalizations, and Deaths, by Vaccination Rates and Nonpharmaceutical Intervention Scenarios — United States, April–September 2021 journal May 2021

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