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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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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
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  Data: Comparing trained and untrained probabilistic ensemble forecasts of COVID-19 cases and deaths in the United States.
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  Data: <searchLink fieldCode="AU" term="%22Ray+EL%22">Ray EL</searchLink>; School of Public Health and Health Sciences, University of Massachusetts Amherst, United States of America.<br /><searchLink fieldCode="AU" term="%22Brooks+LC%22">Brooks LC</searchLink>; Machine Learning Department, Carnegie Mellon University, United States of America.<br /><searchLink fieldCode="AU" term="%22Bien+J%22">Bien J</searchLink>; Department of Data Sciences and Operations, University of Southern California, United States of America.<br /><searchLink fieldCode="AU" term="%22Biggerstaff+M%22">Biggerstaff M</searchLink>; COVID-19 Response, U.S. Centers for Disease Control and Prevention, United States of America.<br /><searchLink fieldCode="AU" term="%22Bosse+NI%22">Bosse NI</searchLink>; London School of Hygiene & Tropical Medicine, United Kingdom.<br /><searchLink fieldCode="AU" term="%22Bracher+J%22">Bracher J</searchLink>; Chair of Statistical Methods and Econometrics, Karlsruhe Institute of Technology, Germany.; Computational Statistics Group, Heidelberg Institute for Theoretical Studies, Germany.<br /><searchLink fieldCode="AU" term="%22Cramer+EY%22">Cramer EY</searchLink>; School of Public Health and Health Sciences, University of Massachusetts Amherst, United States of America.<br /><searchLink fieldCode="AU" term="%22Funk+S%22">Funk S</searchLink>; London School of Hygiene & Tropical Medicine, United Kingdom.<br /><searchLink fieldCode="AU" term="%22Gerding+A%22">Gerding A</searchLink>; School of Public Health and Health Sciences, University of Massachusetts Amherst, United States of America.<br /><searchLink fieldCode="AU" term="%22Johansson+MA%22">Johansson MA</searchLink>; COVID-19 Response, U.S. Centers for Disease Control and Prevention, United States of America.<br /><searchLink fieldCode="AU" term="%22Rumack+A%22">Rumack A</searchLink>; Machine Learning Department, Carnegie Mellon University, United States of America.<br /><searchLink fieldCode="AU" term="%22Wang+Y%22">Wang Y</searchLink>; School of Public Health and Health Sciences, University of Massachusetts Amherst, United States of America.<br /><searchLink fieldCode="AU" term="%22Zorn+M%22">Zorn M</searchLink>; School of Public Health and Health Sciences, University of Massachusetts Amherst, United States of America.<br /><searchLink fieldCode="AU" term="%22Tibshirani+RJ%22">Tibshirani RJ</searchLink>; Machine Learning Department, Carnegie Mellon University, United States of America.<br /><searchLink fieldCode="AU" term="%22Reich+NG%22">Reich NG</searchLink>; School of Public Health and Health Sciences, University of Massachusetts Amherst, United States of America.
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  Data: <searchLink fieldCode="JN" term="%22101084857%22">International journal of forecasting</searchLink> [Int J Forecast] 2023 Jul-Sep; Vol. 39 (3), pp. 1366-1383. <i>Date of Electronic Publication: </i>2022 Jul 01.
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        Value: 10.1016/j.ijforecast.2022.06.005
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              Text: 2023 Jul-Sep
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