A study of disproportionately affected populations by race/ethnicity during the SARS-CoV-2 pandemic using multi-population SEIR modeling and ensemble data assimilation

DOI10.3934/fods.2021022zbMath1481.92137OpenAlexW3200018001MaRDI QIDQ2072653

Geir Evensen, Sophia Marx, Christian Sampson, Tayler Fernandes-Nunez, Emmanuel Fleurantin, Daniel Paul Maes, Justin Bennett

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.2021022

zbMATH Keywords

parameter estimationmodel calibrationdata assimilationeffective reproduction numberCOVID-19SARS-CoV-2ensemble smoothersage stratificationESMDAmulti-population SEIR model

Mathematics Subject Classification ID

Epidemiology (92D30) PDEs in connection with biology, chemistry and other natural sciences (35Q92) Mathematical geography and demography (91D20)

Cites Work

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• Analysis of iterative ensemble smoothers for solving inverse problems
• Predictability and identifiability assessment of models for prostate cancer under androgen suppression therapy
• Implicit upwind residual-distribution Euler and Navier-Stokes solver on unstructured meshes
• Data Assimilation

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