An international initiative of predicting the SARS-CoV-2 pandemic using ensemble data assimilation

DOI10.3934/fods.2021001zbMath1482.92096OpenAlexW3114724032MaRDI QIDQ2072647

Christian Sampson, Alberto Carrassi, Christopher K. R. T. Jones, Geir Evensen, Rafael J. de Moraes, Alban Farchi, Marc Bocquet, Femke C. Vossepoel, Pieter L. Houtekamer, Alison Fowler, Javier Amezcua, Manuel A. Pulido

Publication date: 26 January 2022

Published in: Foundations of Data Science (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.3934/fods.2021001

zbMATH Keywords

parameter estimationmodel calibrationSARS-CoV-2ensemble data assimilationESMDA

Mathematics Subject Classification ID

Epidemiology (92D30) PDEs in connection with biology, chemistry and other natural sciences (35Q92)

Related Items

Disentangling the role of virus infectiousness and awareness-based human behavior during the early phase of the COVID-19 pandemic in the European Union, Predicting the behavior of sparsely-sampled systems across neurobiology and epidemiology, Analysis of COVID-19 in Japan with extended SEIR model and ensemble Kalman filter

Uses Software

• EnKF

Cites Work

• Variational data assimilation with epidemic models
• Levenberg-Marquardt forms of the iterative ensemble smoother for efficient history matching and uncertainty quantification
• Analysis of iterative ensemble smoothers for solving inverse problems
• The discrete age-structured SEIT model with application to tuberculosis transmission in China
• Formulating the history matching problem with consistent error statistics
• Sequential data assimilation of the stochastic SEIR epidemic model for regional COVID-19 dynamics
• Accounting for model errors in iterative ensemble smoothers
• Ensemble Kalman methods for inverse problems
• Parameterizations for ensemble Kalman inversion
• Data Assimilation
• The ensemble Kalman filter for combined state and parameter estimation