Stochastic analysis of a COVID-19 model with effects of vaccination and different transition rates: Real data approach

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dc.contributor.authorXu, Changjin
dc.contributor.authorLiu, Zixin
dc.contributor.authorPang, Yicheng
dc.contributor.authorAkgul, Ali
dc.date.accessioned2024-12-24T19:25:28Z
dc.date.available2024-12-24T19:25:28Z
dc.date.issued2023
dc.departmentSiirt Üniversitesi
dc.description.abstractThis paper presents a stochastic model for COVID-19 that takes into account factors such as incubation times, vaccine effectiveness, and quarantine periods in the spread of the virus in symptomatically contagious populations. The paper outlines the conditions necessary for the existence and uniqueness of a global solution for the stochastic model. Additionally, the paper employs nonlinear analysis to demonstrate some results on the ergodic aspect of the stochastic model. The model is also simulated and compared to deterministic dynamics. To validate and demonstrate the usefulness of the proposed system, the paper compares the results of the infected class with actual cases from Iraq, Bangladesh, and Croatia. Furthermore, the paper visualizes the impact of vaccination rates and transition rates on the dynamics of infected people in the infected class.
dc.description.sponsorshipNational Natural Science Foundation of China [12261015, 62062018]; Project of High-level InnovativeTalents of Guizhou Province [[2016] 5651]; University Science and Technology Top Talents Project of Guizhou Province [[2017] 5736-019]; Foundation of Science and Technology of Guizhou Province [2018XZD01]; Guizhou University of Finance and Economics [2019XYB11]; Guizhou University of Finance and Economics Project Funding [KY [2018] 047]; Guizhou Science and Technology Platform Talents; [[2019] 1051]
dc.description.sponsorshipThis work is supported by National Natural Science Foundation of China (No.12261015, No.62062018) , Project of High-level InnovativeTalents of Guizhou Province ( [2016] 5651) , University Science and Technology Top Talents Project of Guizhou Province (KY [2018] 047) , Foundation of Science and Technology of Guizhou Province ( [2019] 1051) , Guizhou University of Finance and Economics (2018XZD01) , Guizhou University of Finance and Economics Project Funding (No.2019XYB11) , Guizhou Science and Technology Platform Talents ( [2017] 5736-019) .
dc.identifier.doi10.1016/j.chaos.2023.113395
dc.identifier.issn0960-0779
dc.identifier.issn1873-2887
dc.identifier.pmid37009628
dc.identifier.scopus2-s2.0-85151482103
dc.identifier.scopusqualityQ1
dc.identifier.urihttps://doi.org/10.1016/j.chaos.2023.113395
dc.identifier.urihttps://hdl.handle.net/20.500.12604/6423
dc.identifier.volume170
dc.identifier.wosWOS:001030045200001
dc.identifier.wosqualityQ1
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.indekslendigikaynakPubMed
dc.language.isoen
dc.publisherPergamon-Elsevier Science Ltd
dc.relation.ispartofChaos Solitons & Fractals
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_20241222
dc.subjectErgodic theory
dc.subjectGlobal solution
dc.subjectStochastic differential equations
dc.subjectExtinction
dc.titleStochastic analysis of a COVID-19 model with effects of vaccination and different transition rates: Real data approach
dc.typeArticle

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