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@article{2385351,
author = {Cramer, Estee Y and Huang, Yuxin and Wang, Yijin and Ray, Evan L and Cornell, Matthew and Bracher, Johannes and Brennen, Andrea and Rivadeneira, Alvaro J Castro and Gerding, Aaron and House, Katie and Jayawardena, Dasuni and Kanji, Abdul Hannan and Khandelwal, Ayush and Le, Khoa and Mody, Vidhi and Mody, Vrushti and Niemi, Jarad and Stark, Ariane and Shah, Apurv and Wattanchit, Nutcha and Zorn, Martha W and Reich, Nicholas G and Kraus, Andrea and Kraus, David},
article_number = {1},
doi = {http://dx.doi.org/10.1038/s41597-022-01517-w},
keywords = {Computer science; Databases; Scientific data; Software; Viral infection},
language = {eng},
issn = {2052-4463},
journal = {Scientific Data},
title = {The United States COVID-19 Forecast Hub dataset},
url = {https://www.nature.com/articles/s41597-022-01517-w},
volume = {9},
year = {2022}
}
TY - JOUR
ID - 2385351
AU - 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 - Kraus, Andrea - Kraus, David
PY - 2022
TI - The United States COVID-19 Forecast Hub dataset
JF - Scientific Data
VL - 9
IS - 1
SP - 1-15
EP - 1-15
PB - Nature Research
SN - 20524463
KW - Computer science
KW - Databases
KW - Scientific data
KW - Software
KW - Viral infection
UR - https://www.nature.com/articles/s41597-022-01517-w
N2 - 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.
ER -
CRAMER, Estee Y, 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 a Nicholas G REICH. The United States COVID-19 Forecast Hub dataset. \textit{Scientific Data}. Nature Research, 2022, roč.~9, č.~1, s.~1-15. ISSN~2052-4463. Dostupné z: https://dx.doi.org/10.1038/s41597-022-01517-w.
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