Numerical Approximation for the Solution of COVID-19 Pandemic Model

dc.contributor.authorFariha Nawaz
dc.contributor.authorFA19-RMT-106
dc.contributor.authorDr. Sadia Arshad
dc.contributor.authorLHR TP 7393
dc.date.accessioned2026-05-05T13:06:41Z
dc.date.issued2021
dc.description.abstractMany mathematical models give different strategy for controlling specific diseases. The method of constructing a mathematical model is known as mathematical modelling. Fin- ishing the global disease lacks the use of different measures, such as civil separation, inter- action, and screening. we construct various models to predict the progression of COVID- 19 infection using data from Tunisia and Wuhan in fractional order ν. This epidemiologic analysis model suggests how we may predict the spread of COVID-19 by applying the basic reproduction number R0. The susceptibility of threshold number R0 regarding the frame- work of the epidemic model. R0 can be calculated from different approximative methods in COVID-19 epidemic models. The global elements of equilibria, positivity, boundedness, and stability analysis are all given particular attention. For stability if R0 < 1, an infected person transfer the disease individually in new infected is less than one on average over the lifetime of its infectious time, and the infection cannot spread, while R0 > 1 then disease transfer from each infected person to more than one new infection on average and system will unstable. Using Adams-Bashforth-Moulton method, we get numerical result of the proposed model. In graphical work, we compare actual results with approximate solutions.
dc.identifier.urihttps://repository.cuilahore.edu.pk/123456789/3839
dc.language.isoen
dc.publisherLibrary Information Services, COMSATS University Islamabad, Lahore Campus
dc.relation.ispartofseriesLHR TP 7393
dc.subjectDepartment of Mathematics
dc.subjectFA19
dc.subjectMathematics
dc.subjectCOVID-19 Model
dc.subjectNumerical Approximation
dc.subjectEpidemiological Model
dc.subjectDr. Sadia Arshad
dc.titleNumerical Approximation for the Solution of COVID-19 Pandemic Model
dc.typeThesis

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