An international initiative of predicting the SARS-CoV-2 pandemic using ensemble data assimilation
type: Item
instance of: scholarly article
title: An international initiative of predicting the SARS-CoV-2 pandemic using ensemble data assimilation
zbMATH Open document ID: 1482.92096
author: Geir Evensen
author: Javier Amezcua
author: M. Bocquet
author: Alberto Carrassi
author: Alban Farchi
author: Alison Fowler
author: Pieter L. Houtekamer
author: Christopher K. R. T. Jones
author: Rafael J. de Moraes
author: Christian Sampson
author: Femke C. Vossepoel
author: M. Pulido
published in: Foundations of Data Science
publication date: 2022-01-26T00:00:00Z
Mathematics Subject Classification ID: 92D30
Mathematics Subject Classification ID: 35Q92
zbMATH DE Number: 7464711
zbMATH Keywords: SARS-CoV-2
zbMATH Keywords: ensemble data assimilation
zbMATH Keywords: ESMDA
zbMATH Keywords: parameter estimation
zbMATH Keywords: model calibration
describes a project that uses: EnKF
MaRDI profile type: Publication
full work available at URL: fods.2021001
OpenAlex ID: W3114724032
cites work: Data Assimilation
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cites work: Analysis of iterative ensemble smoothers for solving inverse problems
cites work: The ensemble Kalman filter for combined state and parameter estimation
cites work: Formulating the history matching problem with consistent error statistics
cites work: Ensemble Kalman methods for inverse problems
cites work: Variational data assimilation with epidemic models
DOI: 10.3934/FODS.2021001
description: scientific article; zbMATH DE number 7464711