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Cramer, E.Y.* ; Huang, Y.* ; Wang, Y.* ; Ray, E.L.* ; Cornell, M.* ; Bracher, J.* ; Brennen, A.* ; Rivadeneira, A.J.C.* ; Gerding, A.* ; House, K.* ; Jayawardena, D.* ; Kanji, A.H.* ; Khandelwal, A.* ; Le, K.* ; Mody, V.* ; Mody, V.* ; Niemi, J.V.* ; Stark, A.* ; Shah, A.A.* ; Wattanchit, N.* ; Zorn, M.W.* ; Reich, N.G.* ; Gneiting, T.* ; Mühlemann, A.* ; Gu, Y.* ; Chen, Y.* ; Chintanippu, K.* ; Jivane, V.* ; Khurana, A.* ; Kumar, A.* ; Lakhani, A.* ; Mehrotra, P.* ; Pasumarty, S.* ; Shrivastav, M.* ; You, J.* ; Bannur, N.* ; Deva, A.* ; Jain, S.* ; Kulkarni, M.* ; Merugu, S.* ; Raval, A.* ; Shingi, S.* ; Tiwari, A.* ; White, J.* ; Adiga, A.* ; Hurt, B.* ; Lewis, B.* ; Marathe, M.* ; Peddireddy, A.S.* ; Porebski, P.* ; Venkatramanan, S.* ; Wang, L.* ; Dahan, M.* ; Fox, S.D.* ; Gaither, K.* ; Lachmann, M.* ; Meyers, L.A.* ; Scott, J.G.* ; Tec, M.* ; Woody, S.* ; Srivastava, A.* ; Xu, T.* ; Cegan, J.C.* ; Dettwiller, I.D.* ; England, W.P.* ; Farthing, M.W.* ; George, G.E.* ; Hunter, R.H.* ; Lafferty, B.* ; Linkov, I.* ; Mayo, M.L.* ; Parno, M.D.* ; Rowland, M.A.* ; Trump, B.D.* ; Chen, S.* ; Faraone, S.V.* ; Hess, J.* ; Morley, C.P.* ; Salekin, A.* ; Wang, D.* ; Zhang-James, Y.* ; Baer, T.M.* ; Corsetti, S.M.* ; Eisenberg, M.C.* ; Falb, K.* ; Martin, E.T.* ; McCauley, E.* ; Myers, R.L.* ; Schwarz, T.* ; Gibson, G.C.* ; Sheldon, D.* ; Gao, L.* ; Ma, Y.* ; Wu, D.* ; Yu, R.* ; Jin, X.* ; Wang, Y.X.* ; Yan, X.* ; Chen, Y.Q.*

The United States COVID-19 Forecast Hub dataset.

Sci. Data 9 (2022)
Verlagsversion DOI
Open Access Gold
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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Publikationstyp Artikel: Journalartikel
Dokumenttyp Wissenschaftlicher Artikel
ISSN (print) / ISBN 2052-4463
e-ISSN 2052-4463
Zeitschrift Scientific Data
Quellenangaben Band: 9, Heft: 1 Seiten: , Artikelnummer: , Supplement: ,
Verlag Nature Publishing Group
Verlagsort London
Begutachtungsstatus Peer reviewed
Institut(e) Helmholtz AI - KIT (HAI - KIT)