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Can auxiliary indicators improve COVID-19 forecasting and hotspot prediction?
McDonald, Daniel J; Bien, Jacob; Green, Alden; Hu, Addison J; DeFries, Nat; Hyun, Sangwon; Oliveira, Natalia L; Sharpnack, James; Tang, Jingjing; Tibshirani, Robert; Ventura, Valérie; Wasserman, Larry; Tibshirani, Ryan J.
Afiliação
  • McDonald DJ; Department of Statistics, University of British Columbia, Vancouver, BC, Canada V6T 1Z4; daniel@stat.ubc.ca.
  • Bien J; Department of Data Sciences and Operations, University of Southern California, Los Angeles, CA 90089.
  • Green A; Department of Statistics & Data Science, Carnegie Mellon University, Pittsburgh, PA 15213.
  • Hu AJ; Department of Statistics & Data Science, Carnegie Mellon University, Pittsburgh, PA 15213.
  • DeFries N; Machine Learning Department, Carnegie Mellon University, Pittsburgh, PA 15213.
  • Hyun S; Machine Learning Department, Carnegie Mellon University, Pittsburgh, PA 15213.
  • Oliveira NL; Department of Data Sciences and Operations, University of Southern California, Los Angeles, CA 90089.
  • Sharpnack J; Department of Statistics & Data Science, Carnegie Mellon University, Pittsburgh, PA 15213.
  • Tang J; Machine Learning Department, Carnegie Mellon University, Pittsburgh, PA 15213.
  • Tibshirani R; Department of Statistics, University of California, Davis, CA 95616.
  • Ventura V; Computational Biology Department, Carnegie Mellon University, Pittsburgh, PA 15213.
  • Wasserman L; Department of Statistics, Stanford University, Stanford, CA 94305.
  • Tibshirani RJ; Department of Biomedical Data Science, Stanford University, Stanford, CA 94305.
Proc Natl Acad Sci U S A ; 118(51)2021 12 21.
Article em En | MEDLINE | ID: mdl-34903655
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Texto completo: 1 Base de dados: MEDLINE Assunto principal: Indicadores Básicos de Saúde / Modelos Estatísticos / COVID-19 Tipo de estudo: Etiology_studies / Prognostic_studies / Risk_factors_studies Limite: Humans País/Região como assunto: America do norte Idioma: En Revista: Proc Natl Acad Sci U S A Ano de publicação: 2021 Tipo de documento: Article
Texto completo
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PubMed Links
Buscar no Google

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Indicadores Básicos de Saúde / Modelos Estatísticos / COVID-19 Tipo de estudo: Etiology_studies / Prognostic_studies / Risk_factors_studies Limite: Humans País/Região como assunto: America do norte Idioma: En Revista: Proc Natl Acad Sci U S A Ano de publicação: 2021 Tipo de documento: Article