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Likelihood-Free Dynamical Survival Analysis applied to the COVID-19 epidemic in Ohio

Klaus, Colin; Wascher, Matthew; KhudaBukhsh, Wasiur R.; Rempała, Grzegorz A.

Authors

Colin Klaus

Matthew Wascher

Grzegorz A. Rempała



Abstract

The Dynamical Survival Analysis (DSA) is a framework for modeling epidemics based on mean field dynamics applied to individual (agent) level history of infection and recovery. Recently, the DSA method has been shown to be an effective tool in analyzing complex non-Markovian epidemic processes that are otherwise difficult to handle using standard methods. One of the advantages of DSA is its representation of typical epidemic data in a simple although not explicit form that involves solutions of certain differential equations. In this work we describe how a complex non-Markovian DSA model may be applied to a specific data set with the help of appropriate numerical and statistical schemes. The ideas are illustrated with a data example of the COVID-19 epidemic in Ohio.

Citation

Klaus, C., Wascher, M., KhudaBukhsh, W. R., & Rempała, G. A. (2023). Likelihood-Free Dynamical Survival Analysis applied to the COVID-19 epidemic in Ohio. Mathematical Biosciences and Engineering, 20(2), 4103-4127. https://doi.org/10.3934/mbe.2023192

Journal Article Type Article
Acceptance Date Dec 12, 2022
Online Publication Date Dec 20, 2022
Publication Date Jan 1, 2023
Deposit Date Dec 23, 2022
Publicly Available Date Jan 9, 2023
Journal Mathematical Biosciences and Engineering
Print ISSN 1547-1063
Electronic ISSN 1551-0018
Publisher American Institute of Mathematical Sciences (AIMS)
Peer Reviewed Peer Reviewed
Volume 20
Issue 2
Pages 4103-4127
DOI https://doi.org/10.3934/mbe.2023192
Keywords Applied Mathematics; Computational Mathematics; General Agricultural and Biological Sciences; Modeling and Simulation; General Medicine; SIR epidemics; vaccination; PDE system; Nonlocal PDE; Nonlocal conservation laws; ABC method; Statistical inference; N
Public URL https://nottingham-repository.worktribe.com/output/15169540
Publisher URL http://www.aimspress.com/article/doi/10.3934/mbe.2023192

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