Investigating the impact of stochasticity on HIV infection dynamics in CD4+T cells using a reaction-diffusion model

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dc.contributor.authorAhmed, Nauman
dc.contributor.authorYasin, Muhammad W.
dc.contributor.authorAli, Syed Mansoor
dc.contributor.authorAkgul, Ali
dc.contributor.authorRaza, Ali
dc.contributor.authorRafiq, Muhammad
dc.contributor.authorMuhammad, Shah
dc.date.accessioned2024-12-24T19:27:59Z
dc.date.available2024-12-24T19:27:59Z
dc.date.issued2024
dc.departmentSiirt Üniversitesi
dc.description.abstractThe disease dynamics affect the human life. When one person is affected with a disease and if it is not treated well, it can weaken the immune system of the body. Human Immunodeficiency Virus (HIV) is a virus that attacks the immune system, of the body which is the defense line against diseases. If it is not treated well then HIV progresses to its advanced stages and it is known as Acquired Immunodeficiency Syndrome (AIDS). HIV is typically a disease that can transferred from one person to another in several ways such as through blood, breastfeeding, sharing needles or syringes, and many others. So, the need of the hour is to consider such important disease dynamics and that will help mankind to save them from such severe disease. For the said purpose the reaction-diffusion HIV CD4+ T cell model with drug therapy under the stochastic environment is considered. The underlying model is numerically investigated with two time-efficient schemes and the effects of various parameters used in the model are analyzed and explained in a real-life scenario. Additionally, the obtained results will help the decision-makers to avoid such diseases. The random version of the HIV model is numerically investigated under the influence of time noise in Ito<^> sense. The proposed stochastic backward Euler (SBE) scheme and proposed stochastic Implicit finite difference (SIFD) scheme are developed for the computational study of the underlying model. The consistency of the schemes is proven in the mean square sense and the given system of equations is compatible with both schemes. The stability analysis proves that both schemes and schemes are unconditionally stable. The given system of equations has two equilibria, one is disease-free equilibrium (DFE) and the other is endemic equilibrium. The simulations are drawn for the different values of the parameters. The proposed SBE scheme showed the convergent behavior towards the equilibria for the given values of the parameters but also showed negative behavior that is not biological. The proposed SIFD scheme showed better results as compared with the stochastic SBE scheme. This scheme has convergent and positive behavior towards the equilibria points for the given values of the parameters. The effect of various parameters is also analyzed. Simulations are drawn to evaluate the efficacy of the schemes.
dc.description.sponsorshipKing Saud University, Riyadh, Saudi Arabia [RSPD2024R733]
dc.description.sponsorshipThis research is funded by Researchers Supporting Project number (RSPD2024R733), King Saud University, Riyadh, Saudi Arabia.
dc.identifier.doi10.1038/s41598-024-66843-y
dc.identifier.issn2045-2322
dc.identifier.issue1
dc.identifier.pmid39414814
dc.identifier.scopus2-s2.0-85206593466
dc.identifier.scopusqualityQ1
dc.identifier.urihttps://doi.org/10.1038/s41598-024-66843-y
dc.identifier.urihttps://hdl.handle.net/20.500.12604/6863
dc.identifier.volume14
dc.identifier.wosWOS:001336670300011
dc.identifier.wosqualityN/A
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.indekslendigikaynakPubMed
dc.language.isoen
dc.publisherNature Portfolio
dc.relation.ispartofScientific Reports
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_20241222
dc.subjectStochastic HIV model
dc.subjectProposed SBE scheme
dc.subjectProposed SIFD scheme
dc.subjectAnalysis of schemes
dc.subjectSimulations
dc.titleInvestigating the impact of stochasticity on HIV infection dynamics in CD4+T cells using a reaction-diffusion model
dc.typeArticle

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