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Evolutionary computational method for tuberculosis model with fuzziness

dc.contributor.authorAlsaadi, Ateq
dc.contributor.authorDayan, Fazal
dc.contributor.authorAhmed, Nauman
dc.contributor.authorBaleanu, Dumitru
dc.contributor.authorRafiq, Muhammad
dc.contributor.authorRaza, Ali
dc.contributor.authorID56389tr_TR
dc.date.accessioned2023-12-07T12:31:22Z
dc.date.available2023-12-07T12:31:22Z
dc.date.issued2023
dc.departmentÇankaya Üniversitesi, Fen - Edebiyat Fakültesi, Matematik Bölümüen_US
dc.description.abstractThis work investigates the computational study of a six-compartmental mathematical model of tuberculosis disease dynamics with the impact of vaccination. Traditional mathematical models presume that all variables are precise and can be measured or calculated precisely. However, in many real-world scenarios, variables may need to be more accurate or easier to quantify, resulting in model uncertainty. Considering this, fuzziness is introduced into the model by taking the contact, recovery, and death rates due to disease as fuzzy membership functions. Two numerical computational schemes, forward Euler and nonstandard finite difference (NSFD), are designed to solve the model. The positivity and convergence for the developed method are investigated, which are significant characteristics of these dynamical models, and it is revealed that these features are preserved in the extended scheme. Numerical computations are performed to support the analytical results. The numerical and computational results indicate that the proposed NSFD method adequately represents the dynamics of the disease despite the uncertainty and heterogeneity. Moreover, the obtained method generates plausible predictions that regulators can use to design and develop control strategies to support decision-makingen_US
dc.description.publishedMonth8
dc.identifier.citationAlsaadi, Ateq...et.al. (2023). "Evolutionary computational method for tuberculosis model with fuzziness", AIP Advances, Vol.13, No.8.en_US
dc.identifier.doi10.1063/5.0165348
dc.identifier.issn2158-3226
dc.identifier.issue8en_US
dc.identifier.urihttp://hdl.handle.net/20.500.12416/6767
dc.identifier.volume13en_US
dc.language.isoenen_US
dc.relation.ispartofAIP Advancesen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectComputational Methodsen_US
dc.subjectComputer Simulationen_US
dc.subjectMathematical Modelingen_US
dc.subjectNumerical Differentiationen_US
dc.subjectFuzzy Numbersen_US
dc.subjectDiseases And Conditionsen_US
dc.subjectOrgansen_US
dc.subjectBacteriaen_US
dc.subjectEpidemiologyen_US
dc.subjectImmune Systemen_US
dc.titleEvolutionary computational method for tuberculosis model with fuzzinesstr_TR
dc.titleEvolutionary Computational Method for Tuberculosis Model With Fuzzinessen_US
dc.typeArticleen_US
dspace.entity.typePublication

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