STUDY OF TUBERCULOSIS TRANSMISSION DYNAMICS USING SIR EPIDEMIC MODEL WITH THREE TYPES OF POPULATION RECRUITMENT RATE

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dc.contributor.authorKalizhanova, Aigerim
dc.date.accessioned2024-05-03T13:51:32Z
dc.date.available2024-05-03T13:51:32Z
dc.date.issued2024-04-30
dc.description.abstractThis study considers the basic SIR model with three distinct population growth rates to model the tuberculosis transmission dynamics in Kazakhstan. The local stability of disease-free, endemic and extinction equilibrium points of the the models was examined, and the transcritical bifurcation scenario was observed. The effect of growth type on the epidemiological situation in the country was observed through the dynamical analysis and numerical simulations using the data retrieved from the Unified National Electronic Health System (UNEHS). Numerical results reported the basic reproduction number of each of the models to be R0 < 1. Numerical findings support theoretical results. It was revealed that tuberculosis dynamics is approaching its disease-free equilibrium state with R0 < 1, as it was verified by the stability analysis of all three models. SIR model with logistic recruitment rate was proven to give the most favorable results for the epidemic situation in the country.en_US
dc.identifier.citationKalizhanova, A. (2024) Study of tuberculosis transmission dynamics using SIR epidemic model with three types of population recruitment rate. Nazarbayev University School of Science and Humanitiesen_US
dc.identifier.urihttp://nur.nu.edu.kz/handle/123456789/7629
dc.language.isoenen_US
dc.publisherNazarbayev University School of Science and Humanitiesen_US
dc.rightsAttribution-NonCommercial-NoDerivs 3.0 United States*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/us/*
dc.subjectType of access: Embargoen_US
dc.subjectSIRen_US
dc.subjectTuberculosisen_US
dc.subjectKazakhstanen_US
dc.titleSTUDY OF TUBERCULOSIS TRANSMISSION DYNAMICS USING SIR EPIDEMIC MODEL WITH THREE TYPES OF POPULATION RECRUITMENT RATEen_US
dc.typeMaster's thesisen_US
workflow.import.sourcescience

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