Cramer, Estee Y. and Huang, Yuxin and Wang, Yijin and Ray, Evan L. and Cornell, Matthew and Bracher, Johannes and Brennen, Andrea and Castro Rivadeneira, Alvaro J. 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 Wattanachit, Nutcha and Zorn, Martha W. and Reich, Nicholas G. and Abu-Mostafa, Yaser S. and Bathwal, Rahil and Chang, Nicholas A. and Chitta, Pavan and Erickson, Anne and Goel, Sumit and Gowda, Jethin and Jin, Qixuan and Jo, HyeongChan and Kim, Juhyun and Kulkarni, Pranav and Lushtak, Samuel M. and Mann, Ethan and Popken, Max and Soohoo, Connor and Tirumala, Kushal and Tseng, Albert and Varadarajan, Vignesh and Vytheeswaran, Jagath and Wang, Christopher and Yeluri, Akshay and Yurk, Dominic and Zhang, Michael and Zlokapa, Alexander (2022) The United States COVID-19 Forecast Hub dataset. Scientific Data, 9 . Art. No. 462. ISSN 2052-4463. PMCID PMC8236414. doi:10.1038/s41597-022-01517-w. https://resolver.caltech.edu/CaltechAUTHORS:20211105-194210514
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Abstract
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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| Additional Information: | © The Author(s) 2022. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/. Received 17 January 2022; Accepted 29 June 2022; Published 01 August 2022. This work has been supported in part by the US Centers for Disease Control and Prevention (1U01IP001122) and the National Institutes of General Medical Sciences (R35GM119582). The content is solely the responsibility of the authors and does not necessarily represent the official views of the CDC, FDA, NIGMS or the National Institutes of Health. Johannes Bracher was supported by the Helmholtz Foundation via the SIMCARD Information & Data Science Pilot Project. For teams that reported receiving funding for their work, we report the sources and disclosures below. AIpert-pwllnod: Natural Sciences and Engineering Research Council of Canada. Caltech-CS156: Gary Clinard Innovation Fund. CEID-Walk: University of Georgia. CMU-TimeSeries: CDC Center of Excellence, gifts from Google and Facebook. Covid19Sim: National Science Foundation awards 2035360 and 2035361, Gordon and Betty Moore Foundation, and Rockefeller Foundation to support the work of the Society for Medical Decision Making COVID-19 Decision Modeling Initiative. COVIDhub: This work has been supported by the US Centers for Disease Control and Prevention (1U01IP001122) and the National Institutes of General Medical Sciences (R35GM119582). The content is solely the responsibility of the authors and does not necessarily represent the official views of the CDC, NIGMS or the National Institutes of Health. Johannes Bracher was supported by the Helmholtz Foundation via the SIMCARD Information & Data Science Pilot Project. Tilmann Gneiting gratefully acknowledges support by the Klaus Tschira Foundation. CUBoulder, CUB-PopCouncil: The Population Council, and the University of Colorado Population Center (CUPC) funded by Eunice Kennedy Shriver National Institute of Child Health & Human Development of the National Institutes of Health (P2CHD066613). CU-select: NSF DMS-2027369 and a gift from the Morris-Singer Foundation. DDS-NBDS: NSF III-1812699. epiforecasts-ensemble1: Wellcome Trust (210758/Z/18/Z). FDANIHASU: supported by the Intramural Research Program of the NIH/NIDDK. GT_CHHS-COVID19: William W. George Endowment, Virginia C. and Joseph C. Mello Endowment, NSF DGE-1650044, NSF MRI 1828187, research cyberinfrastructure resources and services provided by the Partnership for an Advanced Computing Environment (PACE) at Georgia Tech, and the following benefactors at Georgia Tech: Andrea Laliberte, Joseph C. Mello, Richard “Rick” E. & Charlene Zalesky, and Claudia & Paul Raines, CDC MInD-Healthcare U01CK000531-Supplement. GT-DeepCOVID: This work was supported in part by the NSF (Expeditions CCF-1918770, CAREER IIS-2028586, RAPID IIS-2027862, Medium IIS-1955883, Medium IIS-2106961, CCF-2115126), CDC MInD program, ORNL, faculty research award from Facebook and funds/computing resources from Georgia Tech. BA was supported by CDC-MIND U01CK000594 and start-up funds from University of Iowa. IHME: This work was supported by the Bill & Melinda Gates Foundation, as well as funding from the state of Washington and the National Science Foundation (award nocoviddata. FAIN: 2031096). Imperial-ensemble1: SB acknowledges funding from the Wellcome Trust (219415). Institute of Business Forecasting: IBF. IowaStateLW-STEM: NSF DMS-1916204, Iowa State University Plant Sciences Institute Scholars Program, NSF CCF-1934884, Laurence H. Baker Center for Bioinformatics and Biological Statistics. IUPUI CIS: NSF. JHU_CSSE-DECOM: JHU CSSE: National Science Foundation (NSF) RAPID “Real-time Forecasting of COVID-19 risk in the USA”. 2021-2022. Award ID: 2108526. National Science Foundation (NSF) RAPID “Development of an interactive web-based dashboard to track COVID-19 in real-time”. 2020. Award ID: 2028604. JHU_IDD-CovidSP: State of California, US Dept of Health and Human Services, US Dept of Homeland Security, Johns Hopkins Health System, Office of the Dean at Johns Hopkins Bloomberg School of Public Health, Johns Hopkins University Modeling and Policy Hub, Centers for Disease Control and Prevention. (5U01CK000538-03), University of Utah Immunology, Inflammation, & Infectious Disease Initiative (26798 Seed Grant). JHU_UNC_GAS-StatMechPool: NIH NIGMS: R01GM140564. JHUAPL-Bucky: US Dept of Health and Human Services. KITmetricslab-select_ensemble: Daniel Wolffram was supported by the Klaus Tschira Foundation as well as the Helmholtz Association under the joint research school “HIDSS4Health – Helmholtz Information and Data Science School for Health”. Moreover, his work was funded by the German Federal Ministry of Education and Research (BMBF) and the Baden-Württemberg Ministry of Science as part of the Excellence Strategy of the German Federal and State Governments. LANL-GrowthRate: LANL LDRD 20200700ER. LosAlamos_NAU-CModel_SDVaxVar: NIH/NIGMS grant R01GM111510; LANL-Directed Research and Development Program, Defense Threat Reduction Agency; Laboratory-Directed Research and Development Program project 20220268ER. LU-compUncertLab: UMass Amherst Center of Excellence for Influenza, Institute for Data Intelligent Systems and Computation. MIT-Cassandra: MIT Quest for Intelligence. MOBS-GLEAM_COVID: COVID Supplement CDC-HHS-6U01IP001137-01; CA NU38OT000297 from the Council of State and Territorial Epidemiologists (CSTE). NCSU-COVSIM: Cooperative Agreement NU38OT000297 from the CSTE and the CDC. NotreDame-FRED: NSF RAPID DEB 2027718. NotreDame-mobility: NSF RAPID DEB 2027718. PSI-DRAFT: NSF RAPID Grant # 2031536. QJHong-Encounter: NSF DMR-2001411 and DMR-1835939. SDSC_ISG-TrendModel: The development of the dashboard was partly funded by the Fondation Privée des Hôpitaux Universitaires de Genève. UA-EpiCovDA: NSF RAPID Grant # 2028401. UChicagoCHATTOPADHYAY-UnIT: Defense Advanced Research Projects Agency (DARPA) #HR00111890043/P00004 (I. Chattopadhyay, University of Chicago). UCSB-ACTS: NSF RAPID IIS 2029626. UCSD_NEU-DeepGLEAM: Google Faculty Award, W31P4Q-21-C-0014. UMass-MechBayes: NIGMS #R35GM119582, NSF #1749854, NIGMS #R35GM119582. UMich-RidgeTfReg: This project is funded by the University of Michigan Physics Department and the University of Michigan Office of Research. USC-SikJalpha: This material is based upon work supported by the National Science. Foundation RAPID under Grant No. 2135784 with support from Centers for Disease Control and Prevention (CDC). UVA-Ensemble: National Institutes of Health (NIH) Grant 1R01GM109718, NSF BIG DATA Grant IIS-1633028, NSF Grant No.: OAC-1916805, NSF Expeditions in Computing Grant CCF-1918656, CCF-1917819, NSF RAPID CNS-2028004, NSF RAPID OAC-2027541, US Centers for Disease Control and Prevention 75D30119C05935, a grant from Google, University of Virginia Strategic Investment Fund award number SIF160, Defense Threat Reduction Agency (DTRA) under Contract No. HDTRA1-19-D-0007, and Virginia Dept of Health Grant VDH-21-501-0141. Wadnwani_AI-BayesOpt: This study is made possible by the generous support of the American People through the United States Agency for International Development (USAID). The work described in this article was implemented under the TRACETB Project, managed by WIAI under the terms of Cooperative Agreement Number 72038620CA00006. The contents of this manuscript are the sole responsibility of the authors and do not necessarily reflect the views of USAID or the United States Government. WalmartLabsML-LogForecasting: Team acknowledges Walmart to support this study. Data availability: The datasets generated and/or analyzed during the current study are available in the reichlab/covid19-forecast-hub GitHub repository, https://github.com/reichlab/covid19-forecast-hub. A permanent DOI for the GitHub repository for the Forecast Hub is available as https://doi.org/10.5281/zenodo.520821010 Forecast data are also available through our Zoltar forecast repository at https://zoltardata.com/project/44. Code availability: All code for forecast data validation and storage associated with the current submission is available in the Forecast Hub GitHub repository, https://github.com/reichlab/covid19-forecast-hub-validations. Ensemble models are built with code in the covidEnsembles R package, https://github.com/reichlab/covidEnsembles. The code for forecast analysis is at https://doi.org/10.5281/zenodo.520794012 (covidHubUtils R package) and https://doi.org/10.5281/zenodo.52082247 (covidData R package). Any updates will also be published on Zenodo. These authors contributed equally: Estee Y. Cramer, Yuxin Huang, Yijin Wang. Competing interests: AV, MC, and APP report grants from Metabiota Inc outside the submitted work. | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Group: | COVID-19 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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| PubMed Central ID: | PMC8236414 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| DOI: | 10.1038/s41597-022-01517-w | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Record Number: | CaltechAUTHORS:20211105-194210514 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Persistent URL: | https://resolver.caltech.edu/CaltechAUTHORS:20211105-194210514 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Official Citation: | Cramer, E.Y., Huang, Y., Wang, Y. et al. The United States COVID-19 Forecast Hub dataset. Sci Data 9, 462 (2022). https://doi.org/10.1038/s41597-022-01517-w | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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| Deposited By: | Tony Diaz | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Deposited On: | 05 Nov 2021 21:12 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Last Modified: | 01 Aug 2022 21:38 |
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