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A stochastic analysis for a triple delayed SIQR epidemic model with vaccination and elimination strategies
In this paper, a delayed SIQR epidemic model with vaccination and elimination hybrid strategies is analysed under a white noise perturbation. We prove the existence and the uniqueness of a positive solution. Afterwards, we establish a stochastic threshold [Formula: see text] in order to study the ex...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer Berlin Heidelberg
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7318735/ https://www.ncbi.nlm.nih.gov/pubmed/32837464 http://dx.doi.org/10.1007/s12190-020-01380-1 |
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author | El Fatini, Mohamed Pettersson, Roger Sekkak, Idriss Taki, Regragui |
author_facet | El Fatini, Mohamed Pettersson, Roger Sekkak, Idriss Taki, Regragui |
author_sort | El Fatini, Mohamed |
collection | PubMed |
description | In this paper, a delayed SIQR epidemic model with vaccination and elimination hybrid strategies is analysed under a white noise perturbation. We prove the existence and the uniqueness of a positive solution. Afterwards, we establish a stochastic threshold [Formula: see text] in order to study the extinction and persistence in mean of the stochastic epidemic system. Then we investigate the existence of a stationary distribution for the delayed stochastic model. Finally, some numerical simulations are presented to support our theoretical results. |
format | Online Article Text |
id | pubmed-7318735 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Springer Berlin Heidelberg |
record_format | MEDLINE/PubMed |
spelling | pubmed-73187352020-06-29 A stochastic analysis for a triple delayed SIQR epidemic model with vaccination and elimination strategies El Fatini, Mohamed Pettersson, Roger Sekkak, Idriss Taki, Regragui J Appl Math Comput Original Research In this paper, a delayed SIQR epidemic model with vaccination and elimination hybrid strategies is analysed under a white noise perturbation. We prove the existence and the uniqueness of a positive solution. Afterwards, we establish a stochastic threshold [Formula: see text] in order to study the extinction and persistence in mean of the stochastic epidemic system. Then we investigate the existence of a stationary distribution for the delayed stochastic model. Finally, some numerical simulations are presented to support our theoretical results. Springer Berlin Heidelberg 2020-06-26 2020 /pmc/articles/PMC7318735/ /pubmed/32837464 http://dx.doi.org/10.1007/s12190-020-01380-1 Text en © Korean Society for Informatics and Computational Applied Mathematics 2020 This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic. |
spellingShingle | Original Research El Fatini, Mohamed Pettersson, Roger Sekkak, Idriss Taki, Regragui A stochastic analysis for a triple delayed SIQR epidemic model with vaccination and elimination strategies |
title | A stochastic analysis for a triple delayed SIQR epidemic model with vaccination and elimination strategies |
title_full | A stochastic analysis for a triple delayed SIQR epidemic model with vaccination and elimination strategies |
title_fullStr | A stochastic analysis for a triple delayed SIQR epidemic model with vaccination and elimination strategies |
title_full_unstemmed | A stochastic analysis for a triple delayed SIQR epidemic model with vaccination and elimination strategies |
title_short | A stochastic analysis for a triple delayed SIQR epidemic model with vaccination and elimination strategies |
title_sort | stochastic analysis for a triple delayed siqr epidemic model with vaccination and elimination strategies |
topic | Original Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7318735/ https://www.ncbi.nlm.nih.gov/pubmed/32837464 http://dx.doi.org/10.1007/s12190-020-01380-1 |
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