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      Mathematical Model of COVID-19 Pandemic with Double Dose Vaccination

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          Abstract

          This paper is concerned with the formulation and analysis of an epidemic model of COVID-19 governed by an eight-dimensional system of ordinary differential equations, by taking into account the first dose and the second dose of vaccinated individuals in the population. The developed model is analyzed and the threshold quantity known as the control reproduction number \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\mathcal {R}_{0}$$\end{document} is obtained. We investigate the equilibrium stability of the system, and the COVID-free equilibrium is said to be locally asymptotically stable when the control reproduction number is less than unity, and unstable otherwise. Using the least-squares method, the model is calibrated based on the cumulative number of COVID-19 reported cases and available information about the mass vaccine administration in Malaysia between the 24th of February 2021 and February 2022. Following the model fitting and estimation of the parameter values, a global sensitivity analysis was performed by using the Partial Rank Correlation Coefficient (PRCC) to determine the most influential parameters on the threshold quantities. The result shows that the effective transmission rate \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$(\alpha )$$\end{document} , the rate of first vaccine dose \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$(\phi )$$\end{document} , the second dose vaccination rate \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$(\sigma )$$\end{document} and the recovery rate due to the second dose of vaccination \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$(\eta )$$\end{document} are the most influential of all the model parameters. We further investigate the impact of these parameters by performing a numerical simulation on the developed COVID-19 model. The result of the study shows that adhering to the preventive measures has a huge impact on reducing the spread of the disease in the population. Particularly, an increase in both the first and second dose vaccination rates reduces the number of infected individuals, thus reducing the disease burden in the population.

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          The epidemiology and pathogenesis of coronavirus disease (COVID-19) outbreak

          Coronavirus disease (COVID-19) is caused by SARS-COV2 and represents the causative agent of a potentially fatal disease that is of great global public health concern. Based on the large number of infected people that were exposed to the wet animal market in Wuhan City, China, it is suggested that this is likely the zoonotic origin of COVID-19. Person-to-person transmission of COVID-19 infection led to the isolation of patients that were subsequently administered a variety of treatments. Extensive measures to reduce person-to-person transmission of COVID-19 have been implemented to control the current outbreak. Special attention and efforts to protect or reduce transmission should be applied in susceptible populations including children, health care providers, and elderly people. In this review, we highlights the symptoms, epidemiology, transmission, pathogenesis, phylogenetic analysis and future directions to control the spread of this fatal disease.
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            A methodology for performing global uncertainty and sensitivity analysis in systems biology.

            Accuracy of results from mathematical and computer models of biological systems is often complicated by the presence of uncertainties in experimental data that are used to estimate parameter values. Current mathematical modeling approaches typically use either single-parameter or local sensitivity analyses. However, these methods do not accurately assess uncertainty and sensitivity in the system as, by default, they hold all other parameters fixed at baseline values. Using techniques described within we demonstrate how a multi-dimensional parameter space can be studied globally so all uncertainties can be identified. Further, uncertainty and sensitivity analysis techniques can help to identify and ultimately control uncertainties. In this work we develop methods for applying existing analytical tools to perform analyses on a variety of mathematical and computer models. We compare two specific types of global sensitivity analysis indexes that have proven to be among the most robust and efficient. Through familiar and new examples of mathematical and computer models, we provide a complete methodology for performing these analyses, in both deterministic and stochastic settings, and propose novel techniques to handle problems encountered during these types of analyses.
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              Reproduction numbers and sub-threshold endemic equilibria for compartmental models of disease transmission.

              A precise definition of the basic reproduction number, R0, is presented for a general compartmental disease transmission model based on a system of ordinary differential equations. It is shown that, if R0 1, then it is unstable. Thus, R0 is a threshold parameter for the model. An analysis of the local centre manifold yields a simple criterion for the existence and stability of super- and sub-threshold endemic equilibria for R0 near one. This criterion, together with the definition of R0, is illustrated by treatment, multigroup, staged progression, multistrain and vector-host models and can be applied to more complex models. The results are significant for disease control.
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                Author and article information

                Contributors
                peterjames4real@gmail.com
                Journal
                Acta Biotheor
                Acta Biotheor
                Acta Biotheoretica
                Springer Netherlands (Dordrecht )
                0001-5342
                1572-8358
                6 March 2023
                2023
                : 71
                : 2
                : 9
                Affiliations
                [1 ]Department of Mathematical and Computer Sciences, University of Medical Sciences, Ondo City, Ondo State Nigeria
                [2 ]Department of Epidemiology and Biostatistics, School of Public Health, University of Medical Sciences, Ondo City, Ondo State Nigeria
                [3 ]GRID grid.443316.7, ISNI 0000 0000 9015 269X, Department of Mathematics, , State University of Gorontalo, ; Bone Bolango, 96119 Indonesia
                [4 ]GRID grid.411257.4, ISNI 0000 0000 9518 4324, Department of Mathematical Sciences, , Federal University of Technology, ; Akure, Ondo State Nigeria
                [5 ]GRID grid.410877.d, ISNI 0000 0001 2296 1505, Department of Mathematical Sciences, , Universiti Teknologi Malaysia, ; Johor Bahru, Johor Malaysia
                [6 ]GRID grid.412801.e, ISNI 0000 0004 0610 3238, Department of Mathematical Sciences, , University of South Africa, ; Florida, South Africa
                [7 ]GRID grid.418190.5, ISNI 0000 0001 2187 0556, Microbiology Division, , Thermo Fisher Scientific, ; Lenexa, KS USA
                [8 ]GRID grid.442636.1, ISNI 0000 0004 1760 2083, Department of Mathematics, , Federal University of Technology, ; Minna, Niger State Nigeria
                Author information
                http://orcid.org/0000-0001-9448-1164
                Article
                9460
                10.1007/s10441-023-09460-y
                9986676
                36877326
                28998e7b-71c8-499d-ade3-d4c3945ddbac
                © Prof. Dr. Jan van der Hoeven stichting voor theoretische biologie 2023, Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

                This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.

                History
                : 18 June 2022
                : 14 February 2023
                Categories
                Regular Article
                Custom metadata
                © Prof. Dr. Jan van der Hoeven stichting voor theoretische biologie 2023

                Ecology
                mathematical model,covid-19,effective reproduction number,sensitivity analysis,92b05,91a40,93d20,34d23
                Ecology
                mathematical model, covid-19, effective reproduction number, sensitivity analysis, 92b05, 91a40, 93d20, 34d23

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