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The United States COVID-19 Forecast Hub dataset
Estee Y. Cramer, Yuxin Huang, Yijin Wang, Evan L Ray, Matthew Cornell, Johannes Bracher, Andrea Brennen, Alvaro J. Castro Rivadeneira, Aaron Gerding, Katie House, Abdul Hannan Kanji, Ayush Khandelwal, Khoa Le, Vidhi Mody, Vrushti Mody, Jarad Niemi, Ariane Stark, Apurv Shah, Nutcha Wattanchit, Martha Zorn, Nicholas G Reich, Tilmann Gneiting, Anja Mühlemann, Youyang Gu, Yixian Chen, Krishna Chintanippu, Viresh Jivane, Ankita Khurana, Ajay Kumar, Anshul Lakhani, Prakhar Mehrotra, Sujitha Pasumarty, Monika Shrivastav, Jialu You, Nayana Bannur, Ayush Deva, Sansiddh Jain, Mihir Kulkarni, Srujana Merugu, Alpan Raval, Siddhant Shingi, Avtansh Tiwari, Jerome White, Aniruddha Adiga, Benjamin Hurt, Bryan Lewis, Madhav Marathe, Akhil Sai Peddireddy, Przemyslaw Porebski, Srinivasan Venkatramanan, Lijing Wang, Maytal Dahan, Spencer J. Fox, Kelly Gaither, Michael Lachmann, Lauren Ancel Meyers, James G. Scott, Mauricio Tec, Spencer Woody, Ajitesh Srivastava, Tianjian Xu, Jeffrey C. Cegan, Ian Dettwiller, William P. England, Matthew W. Farthing, Glover George, Robert H. Hunter, Brandon J. Lafferty, Igor Linkov, Michael L. Mayo, Matthew Parno, Michael A. Rowland, Benjamin D. Trump, Samuel Chen, Stephen V. Faraone, Jonathan Hess, Christopher P. Morley, Asif Salekin, Dongliang Wang, Yanli Zhang‐James, T. M. Baer, Sabrina Corsetti, Marisa C. Eisenberg, Karl Falb, Yitao Huang, Emily T. Martin, Ella McCauley, Robert L. Myers, Tom Schwarz, Graham Gibson, Daniel Sheldon, Liyao Gao, Yi-An Ma, Dongxia Wu, Rose Yu, Xiaoyong Jin, Yuxiang Wang, Xifeng Yan, YangQuan Chen, Lihong Guo, Yanting Zhao, Jinghui Chen, Quanquan Gu, Lingxiao Wang, Pan Xu, Weitong Zhang, Difan Zou, Ishanu Chattopadhyay, Yi Huang, Guoqing Lu, Ruth M. Pfeiffer, T. J. Sumner, Dongdong Wang, Liqiang Wang, Shunpu Zhang, Zihang Zou, Hannah Biegel, J. Lega, Fazle Hussain, Zeina S. Khan, Frank Van Bussel, Steve McConnell, Stephanie Guertin, Christopher Hulme-Lowe, VP Nagraj, Stephen Turner, Benjamı́n Béjar, Christine Choirat, Antoine Flahault, Ekaterina Krymova, Gavin Lee, Elisa Manetti, Kristen Namigai, Guillaume Obozinski, Tao Sun, Dorina Thanou, Xuegang Ban, Yunfeng Shi, Robert Walraven, Qi‐Jun Hong, Axel van de Walle, M. Ben-Nun, Steven Riley, Pete Riley, James Turtle, Duy Cao, Joseph Galasso, Jae H. Cho, A-Reum Jo, David DesRoches, Pedro Forli, Bruce H. Hamory, Ugur Koyluoglu, Christina Kyriakides, Helen Leis, John Milliken, Michael Moloney, James P. Morgan, Ninad Nirgudkar, Gokce Ozcan, Noah Piwonka, Matt Ravi, Chris Schrader, Elizabeth A. Shakhnovich, Daniel M. Siegel, Ryan Spatz, Chris Stiefeling, Barrie Wilkinson, Alexander Wong, Sean Cavany, Guido España, Sean M. Moore, Rachel J. Oidtman, T. Alex Perkins, Julie S. Ivy, María E. Mayorga, Jessica Mele, Erik Rosenstrom, Julie Swann, Andrea Kraus, David Kraus, Jiang Bian, Wei Cao, Zhifeng Gao, Juan Lavista Ferres, Chaozhuo Li, Tie‐Yan Liu, Xing Xie, Shun Zhang, Shun Zheng, Matteo Chinazzi, Alessandro Vespignani, Xinyue Xiong, Jessica T. Davis, Kunpeng Mu, Ana Pastore y Piontti, Jackie Baek, Vivek F. Farias, Andreea Georgescu, Retsef Levi, Deeksha Sinha, Joshua Wilde, Andrew Zheng, Omar Skali Lami, Amine Bennouna, David Nze Ndong, Georgia Perakis, Divya Singhvi, Ιoannis Spantidakis, Leann Thayaparan, Asterios Tsiourvas, Shane Weisberg, Ali Jadbabaie, Arnab Sarker, Devavrat Shah, Leo Celi, Nicolás Della Penna, Saketh Sundar, Abraham Berlin, Parth D. Gandhi, Thomas McAndrew, Matthew Piriya, Ye Chen, William S. Hlavacek, Yen Ting Lin, Abhishek Mallela, Ely Miller, Jacob Neumann, Richard A. Posner, Russ Wolfinger, Lauren Castro, Geoffrey Fairchild, Isaac Michaud, Dave Osthus, Daniel Wolffram, D. Karlen, Mark J. Panaggio, Matt Kinsey, Luke C. Mullany, Kaitlin Rainwater‐Lovett, Lauren Shin, Katharine Tallaksen, Shelby Wilson, Michael P. Brenner, Marc Coram, Jessie K. Edwards, Keya Joshi, Ellen R. Klein, Juan Dent Hulse, Kyra H. Grantz, Alison L. Hill, Kathryn Kaminsky, Joshua Kaminsky, Lindsay T. Keegan, Stephen A. Lauer, Elizabeth C. Lee, Joseph C. Lemaitre, Justin Lessler, Hannah R. Meredith, Javier Perez‐Saez, Sam Shah, Claire P. Smith, Shaun Truelove, Josh Wills, Lauren Gardner, Maximilian Marshall, Kristen Nixon, John C. Burant, Jozef Budzinski, Wen-Hao Chiang, George Mohler, Junyi Gao, Lucas M. Glass, Qian Cheng, Justin Romberg, Rakshith Sharma, Jeffrey Spaeder, Jimeng Sun, Cao Xiao, Lei Gao, Zhiling Gu, Myungjin Kim, Xinyi Li, Yueying Wang, Guannan Wang, Li Wang, Shan Yu, Chaman Jain, Sangeeta Bhatia, Pierre Nouvellet, Ryan M Barber, Emmanuela Gaikedu, Simon I Hay, Steve Lim, Chris Murray, David M. Pigott, Robert C. Reiner, Prasith Baccam, Heidi Gurung, Steven A. Stage, Bradley T. Suchoski, Chung-Yan Fong, Dit‐Yan Yeung, Bijaya Adhikari, Jiaming Cui, B. Aditya Prakash, Alexander Rodríguez, Anika Tabassum, Jiajia Xie, John Asplund, Arden Baxter, Pınar Keskinocak, Buse Eylul Oruc, Nicoleta Serban, Sercan Ö. Arık, Mike Dusenberry, Arkady Epshteyn, Elli Kanal, Long Tan Le, Chunliang Li, Tomas Pfister, Rajarishi Sinha, Thomas C. Tsai, Jinsung Yoon, Leyou Zhang, Daniel J. Wilson, Artur Belov, Carson C. Chow, Richard C. Gerkin, Osman N. Yoğurtçu, Mark Ibrahim, Timothée Lacroix, Matthew Le, Jason Liao, Maximilian Nickel, Levent Sagun, Sam Abbott, Nikos I Bosse, Sebastian Funk, Joel Hellewell, Sophie Meakin, Katharine Sherratt, Rahi Kalantari, Mingyuan Zhou, Morteza Karimzadeh, Benjamín Lucas, Thoại D. Ngô, Hamidreza Zoraghein, Behzad Vahedi, Zhongying Wang, Sen Pei, Jeffrey Shaman, Teresa K. Yamana, Dimitris Bertsimas, Michael Lingzhi Li, Soni Saksham, Hamza Tazi Bouardi, Madeline Adee, Turgay Ayer, Jagpreet Chhatwal, Özden O. Dalgıç, Mary A. Ladd, Benjamin P. Linas, Peter P. Mueller, Jade Xiao, Jürgen Bosch, Austin Wilson, Peter A. Zimmerman, Qinxia Wang, Yuanjia Wang, Shanghong Xie, Donglin Zeng, Jacob Bien, Logan Brooks, Alden Green, Addison J. Hu, Maria Jahja, Daniel J. McDonald, Balasubramanian Narasimhan, Collin A. Politsch, Samyak Rajanala, Aaron Rumack, Noah Simon, Ryan J. Tibshirani, Rob Tibshirani, Valérie Ventura, Larry Wasserman, John M. Drake, Eamon B. O’Dea, Yaser S. Abu‐Mostafa, Rahil Bathwal, Nicholas A. Chang, Pavan Chitta, Anne Erickson, Sumit Goel, Jethin Gowda, Qixuan Jin, HyeongChan Jo, Juhyun Kim, Pranav Kulkarni, Samuel M. Lushtak, Ethan E. Mann, Max Popken, Connor Soohoo, Kushal Tirumala, Albert Tseng, Vignesh Varadarajan, Jagath Vytheeswaran, Christopher Wang, Akshay Yeluri, Dominic Yurk, Michael Zhang, Alexander Zlokapa, Roberto Pagano, Chandini Jain, Vishal Tomar, Lam Si Tung Ho, Huong Huynh, Ngoc Quoc Tran, Velma K. Lopez, Jo Walker, Rachel B. Slayton, Michael A. Johansson, Matthew Biggerstaff
Bibliographic record
VenueScientific Data · 2022
Typearticle
Languageen
FieldMathematics
TopicCOVID-19 epidemiological studies
Canadian institutionsDalhousie UniversityUniversity of British ColumbiaUniversity of VictoriaTRIUMF
FundersOak Ridge National LaboratoryNatural Sciences and Engineering Research Council of CanadaQuest for Intelligence, Massachusetts Institute of TechnologyPlant Sciences Institute, Iowa State UniversityNational Institutes of HealthCenters for Disease Control and PreventionKlaus Tschira StiftungCenter for Emerging Infectious Diseases, University of IowaIowa State UniversityNorth Carolina State UniversityBundesministerium für Bildung und ForschungCouncil of State and Territorial EpidemiologistsInstitute for Health Metrics and EvaluationNational Institute of General Medical SciencesUniversity of Massachusetts AmherstJohns Hopkins Bloomberg School of Public HealthWellcome TrustU.S. Department of EnergyNational Institute of Diabetes and Digestive and Kidney DiseasesCalifornia Institute of TechnologyEunice Kennedy Shriver National Institute of Child Health and Human DevelopmentBill and Melinda Gates FoundationLos Alamos National LaboratoryJohns Hopkins UniversityGordon and Betty Moore FoundationIndiana University-Purdue University IndianapolisU.S. Department of Homeland SecurityNational Nuclear Security AdministrationLaboratory Directed Research and DevelopmentNational Science Foundation
KeywordsLeverage (statistics)DownloadCoronavirus disease 2019 (COVID-19)Government (linguistics)PandemicScale (ratio)Disease controlComputer scienceData scienceEconometricsBusinessGeographyInfectious disease (medical specialty)EconomicsWorld Wide WebEnvironmental healthMedicineMachine learning
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.032
Threshold uncertainty score0.063
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 0.010 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
Opus teacher head0.595
GPT teacher head0.495
Teacher spread0.099 · how far apart the two teachers sit on this one work
Validation status
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreDataset
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".
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Citations129
Published2022
Admission routes2
Has abstractyes