Informace o publikaci

The United States COVID-19 Forecast Hub dataset

Autoři	CRAMER Estee Y KRAUS Andrea HUANG Yuxin KRAUS David 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
Rok publikování	2022
Druh	Článek v odborném periodiku
Časopis / Zdroj	Scientific Data
Fakulta / Pracoviště MU	Přírodovědecká fakulta
Citace
www	https://www.nature.com/articles/s41597-022-01517-w
Doi	http://dx.doi.org/10.1038/s41597-022-01517-w
Klíčová slova	Computer science; Databases; Scientific data; Software; Viral infection
Popis	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.