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Adaptive SIR model with vaccination: simultaneous identification of rates and functions illustrated with COVID-19
An Adaptive Susceptible-Infected-Removed-Vaccinated (A-SIRV) epidemic model with time-dependent transmission and removal rates is constructed for investigating the dynamics of an epidemic disease such as the COVID-19 pandemic. Real data of COVID-19 spread is used for the simultaneous identification...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9486803/ https://www.ncbi.nlm.nih.gov/pubmed/36127380 http://dx.doi.org/10.1038/s41598-022-20276-7 |
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author | Marinov, Tchavdar T. Marinova, Rossitza S. |
author_facet | Marinov, Tchavdar T. Marinova, Rossitza S. |
author_sort | Marinov, Tchavdar T. |
collection | PubMed |
description | An Adaptive Susceptible-Infected-Removed-Vaccinated (A-SIRV) epidemic model with time-dependent transmission and removal rates is constructed for investigating the dynamics of an epidemic disease such as the COVID-19 pandemic. Real data of COVID-19 spread is used for the simultaneous identification of the unknown time-dependent rates and functions participating in the A-SIRV system. The inverse problem is formulated and solved numerically using the Method of Variational Imbedding, which reduces the inverse problem to a problem for minimizing a properly constructed functional for obtaining the sought values. To illustrate and validate the proposed solution approach, the present study used available public data for several countries with diverse population and vaccination dynamics—the World, Israel, The United States of America, and Japan. |
format | Online Article Text |
id | pubmed-9486803 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-94868032022-09-21 Adaptive SIR model with vaccination: simultaneous identification of rates and functions illustrated with COVID-19 Marinov, Tchavdar T. Marinova, Rossitza S. Sci Rep Article An Adaptive Susceptible-Infected-Removed-Vaccinated (A-SIRV) epidemic model with time-dependent transmission and removal rates is constructed for investigating the dynamics of an epidemic disease such as the COVID-19 pandemic. Real data of COVID-19 spread is used for the simultaneous identification of the unknown time-dependent rates and functions participating in the A-SIRV system. The inverse problem is formulated and solved numerically using the Method of Variational Imbedding, which reduces the inverse problem to a problem for minimizing a properly constructed functional for obtaining the sought values. To illustrate and validate the proposed solution approach, the present study used available public data for several countries with diverse population and vaccination dynamics—the World, Israel, The United States of America, and Japan. Nature Publishing Group UK 2022-09-20 /pmc/articles/PMC9486803/ /pubmed/36127380 http://dx.doi.org/10.1038/s41598-022-20276-7 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Article Marinov, Tchavdar T. Marinova, Rossitza S. Adaptive SIR model with vaccination: simultaneous identification of rates and functions illustrated with COVID-19 |
title | Adaptive SIR model with vaccination: simultaneous identification of rates and functions illustrated with COVID-19 |
title_full | Adaptive SIR model with vaccination: simultaneous identification of rates and functions illustrated with COVID-19 |
title_fullStr | Adaptive SIR model with vaccination: simultaneous identification of rates and functions illustrated with COVID-19 |
title_full_unstemmed | Adaptive SIR model with vaccination: simultaneous identification of rates and functions illustrated with COVID-19 |
title_short | Adaptive SIR model with vaccination: simultaneous identification of rates and functions illustrated with COVID-19 |
title_sort | adaptive sir model with vaccination: simultaneous identification of rates and functions illustrated with covid-19 |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9486803/ https://www.ncbi.nlm.nih.gov/pubmed/36127380 http://dx.doi.org/10.1038/s41598-022-20276-7 |
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