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Comparing trained and untrained probabilistic ensemble forecasts of COVID-19 cases and deaths in the United States.
Ray, Evan L; Brooks, Logan C; Bien, Jacob; Biggerstaff, Matthew; Bosse, Nikos I; Bracher, Johannes; Cramer, Estee Y; Funk, Sebastian; Gerding, Aaron; Johansson, Michael A; Rumack, Aaron; Wang, Yijin; Zorn, Martha; Tibshirani, Ryan J; Reich, Nicholas G.
Afiliación
  • 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.
  • Cramer EY; Computational Statistics Group, Heidelberg Institute for Theoretical Studies, Germany.
  • Funk S; School of Public Health and Health Sciences, University of Massachusetts Amherst, United States of America.
  • Gerding A; London School of Hygiene & Tropical Medicine, United Kingdom.
  • Johansson MA; School of Public Health and Health Sciences, University of Massachusetts Amherst, United States of America.
  • Rumack A; COVID-19 Response, U.S. Centers for Disease Control and Prevention, United States of America.
  • Wang Y; Machine Learning Department, Carnegie Mellon University, United States of America.
  • Zorn M; School of Public Health and Health Sciences, University of Massachusetts Amherst, United States of America.
  • Tibshirani RJ; School of Public Health and Health Sciences, University of Massachusetts Amherst, United States of America.
  • Reich NG; Machine Learning Department, Carnegie Mellon University, United States of America.
Int J Forecast ; 39(3): 1366-1383, 2023.
Article en En | MEDLINE | ID: mdl-35791416
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Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Tipo de estudio: Prognostic_studies Idioma: En Revista: Int J Forecast Año: 2023 Tipo del documento: Article País de afiliación: Estados Unidos
Texto completo
Imprimir
XML
PubMed Links
Buscar en Google

Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Tipo de estudio: Prognostic_studies Idioma: En Revista: Int J Forecast Año: 2023 Tipo del documento: Article País de afiliación: Estados Unidos