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Comparing trained and untrained probabilistic ensemble forecasts of COVID-19 cases and deaths in the United States.

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Bibliographic Details
Title: Comparing trained and untrained probabilistic ensemble forecasts of COVID-19 cases and deaths in the United States.
Authors: Ray EL; School of Public Health and Health Sciences, University of Massachusetts Amherst, United States of America., Brooks LC; Machine Learning Department, Carnegie Mellon University, United States of America., Bien J; Department of Data Sciences and Operations, University of Southern California, United States of America., Biggerstaff M; COVID-19 Response, U.S. Centers for Disease Control and Prevention, United States of America., Bosse NI; London School of Hygiene & Tropical Medicine, United Kingdom., Bracher J; Chair of Statistical Methods and Econometrics, Karlsruhe Institute of Technology, Germany.; Computational Statistics Group, Heidelberg Institute for Theoretical Studies, Germany., Cramer EY; School of Public Health and Health Sciences, University of Massachusetts Amherst, United States of America., Funk S; London School of Hygiene & Tropical Medicine, United Kingdom., Gerding A; School of Public Health and Health Sciences, University of Massachusetts Amherst, United States of America., Johansson MA; COVID-19 Response, U.S. Centers for Disease Control and Prevention, United States of America., Rumack A; Machine Learning Department, Carnegie Mellon University, United States of America., Wang Y; School of Public Health and Health Sciences, University of Massachusetts Amherst, United States of America., Zorn M; School of Public Health and Health Sciences, University of Massachusetts Amherst, United States of America., Tibshirani RJ; Machine Learning Department, Carnegie Mellon University, United States of America., Reich NG; School of Public Health and Health Sciences, University of Massachusetts Amherst, United States of America.
Source: International journal of forecasting [Int J Forecast] 2023 Jul-Sep; Vol. 39 (3), pp. 1366-1383. Date of Electronic Publication: 2022 Jul 01.
Publication Type: Journal Article
Journal Info: Publisher: North-Holland Country of Publication: Netherlands NLM ID: 101084857 Publication Model: Print-Electronic Cited Medium: Print ISSN: 0169-2070 (Print) Linking ISSN: 01692070 NLM ISO Abbreviation: Int J Forecast Subsets: PubMed not MEDLINE
Database: MEDLINE Ultimate
Description
ISSN:0169-2070
DOI:10.1016/j.ijforecast.2022.06.005