A surrogate Bayesian framework for a SARS-CoV-2 data driven stochastic model

M. Ganesh; S. C. Hawkins

doi:10.1515/cmb-2022-0131

A surrogate Bayesian framework for a SARS-CoV-2 data driven stochastic model

M. Ganesh
, S. C. Hawkins

School of Mathematical and Physical Sciences

Research output: Contribution to journal › Article › peer-review

1 Citation (Scopus)

42 Downloads (Pure)

Abstract

Dynamic compartmentalized data (DCD) and compartmentalized differential equations (CDEs) are key instruments for modeling transmission of pathogens such as the SARS-CoV-2 virus. We describe an efficient nowcasting algorithm for modeling transmission of SARS-CoV-2 with uncertainty quantification for the COVID-19 impact. A key concern for transmission of SARS-CoV-2 is under-reporting of cases, and this is addressed in our data-driven model by providing an estimate for the detection rate. Our novel top-down model is based on CDEs with stochastic constitutive parameters obtained from the DCD using Bayesian inference. We demonstrate the robustness of our algorithm for simulation studies using synthetic DCD, and nowcasting COVID-19 using real DCD from several regions across five continents.

Original language	English
Pages (from-to)	34-67
Number of pages	34
Journal	Computational and Mathematical Biophysics
Volume	10
Issue number	1
DOIs	https://doi.org/10.1515/cmb-2022-0131
Publication status	Published - Jan 2022

Bibliographical note

Copyright the Author(s) 2022. Version archived for private and non-commercial use with the permission of the author/s and according to publisher conditions. For further rights please contact the publisher.

Keywords

SARS-CoV-2
COVID-19
uncertainty quantification
Bayesian

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10.1515/cmb-2022-0131Licence: CC BY

Publisher version (open access)Final published version, 1.22 MBLicence: CC BY

Cite this

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A surrogate Bayesian framework for a SARS-CoV-2 data driven stochastic model

Abstract

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Keywords

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