The United States COVID-19 Forecast Hub dataset
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216224%3A14310%2F22%3A00135665" target="_blank" >RIV/00216224:14310/22:00135665 - isvavai.cz</a>
Result on the web
<a href="https://www.nature.com/articles/s41597-022-01517-w" target="_blank" >https://www.nature.com/articles/s41597-022-01517-w</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1038/s41597-022-01517-w" target="_blank" >10.1038/s41597-022-01517-w</a>
Alternative languages
Result language
angličtina
Original language name
The United States COVID-19 Forecast Hub dataset
Original language 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.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
10103 - Statistics and probability
Result continuities
Project
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Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2022
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Scientific Data
ISSN
2052-4463
e-ISSN
2052-4463
Volume of the periodical
9
Issue of the periodical within the volume
1
Country of publishing house
DE - GERMANY
Number of pages
15
Pages from-to
1-15
UT code for WoS article
000834818500002
EID of the result in the Scopus database
2-s2.0-85135353963