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Year 2025, Volume: 8 Issue: 2, 56 - 74, 28.06.2025
https://doi.org/10.33187/jmsm.1659843

Abstract

References

  • [1] A. B. Gumel, E. A. Iboi, C. N. Ngonghala, et al., A primer on using mathematics to understand COVID-19 dynamics: Modeling, analysis and simulations, Infect. Dis. Model., 6 (2021), 148-168. https://doi.org/10.1016/j.idm.2020.11.005
  • [2] W. O. Kermack, A. G. McKendrick, A contribution to the mathematical theory of epidemics, Proc. R. Soc. Lond. A, 115(772) (1927), 700-721. https://doi.org/10.1098/rspa.1927.0118
  • [3] T. D. Frank, COVID-19 interventions in some European countries induced bifurcations stabilizing low death states against high death states: An eigenvalue analysis based on the order parameter concept of synergetics, Chaos Solitons Fractals, 140 (2020), Article ID 110194. https://doi.org/10.1016/j.chaos.2020.110194
  • [4] N. Piovella, Analytical solution of SEIR model describing the free spread of the COVID-19 pandemic, Chaos Solitons Fractals, 140 (2020), Article ID 110243, 6 pages. https://doi.org/10.1016/j.chaos.2020.110243
  • [5] A. Din, Y. Li, T. Khan, et al., Mathematical analysis of spread and control of the novel corona virus (COVID-19) in China, Chaos Solitons Fractals, 141 (2020), Article ID 110286. https://doi.org/10.1016/j.chaos.2020.110286
  • [6] A. E. Matouk, Complex dynamics in susceptible-infected models for COVID-19 with multi-drug resistance, Chaos Solitons Fractals, 140 (2020), Article ID 110257, 13 pages. https://doi.org/10.1016/j.chaos.2020.110257
  • [7] H. Bulut, M. Gölgeli, F. M. Atay, Modelling personal cautiousness during the COVID-19 pandemic: A case study for Turkey and Italy, Nonlinear Dyn., 105 (2021), 957-969. https://doi.org/10.1007/s11071-021-06320-7
  • [8] Ö. F. Gözükızıl, N. Köker, Application of an epidemic model to Turkey data and stability analysis for the COVID-19 pandemic, Sakarya Univ. J. Sci., 25(6) (2021), 1438-1445. https://doi.org/10.16984/saufenbilder.980797
  • [9] S. Boulaaras, R. Ramalingam, A. J. Gnanaprakasam, SEIR model for COVID-19: Stability of the standard coronavirus factor and control mechanism, Eur. Phys. J. Spec. Top., 232 (2023), 2485-2495. https://doi.org/10.1140/epjs/s11734-023-00915-4
  • [10] C. Xu, M. Farman, A. Hasan, et al., Lyapunov stability and wave analysis of Covid-19 omicron variant of real data with fractional operator, Alex. Eng. J., 61(12) (2022), 11787-11802. https://doi.org/10.1016/j.aej.2022.05.025
  • [11] S. Jamil, M. Farman, A, Akgül, et al., Fractional order age dependent Covid-19 model: An equilibria and quantitative analysis with modeling, Results Phys., 53 (2023) Article ID 106928, 17 pages. https://doi.org/10.1016/j.rinp.2023.106928
  • [12] U. A. P. de Le´on, E. Avila-Vales, K. L. Huang, Modeling COVID-19 dynamic using a two-strain model with vaccination, Chaos Solitons Fractals, 157 (2022), Article ID 111927, 17 pages. https://doi.org/10.1016/j.chaos.2022.111927
  • [13] S. Bugalia, J. P. Tripathi, H. Wang, Mutations make pandemics worse or better: Modeling SARS-CoV-2 variants and imperfect vaccination, J. Math. Biol., 88 (2024), Article ID 45, 50 pages. https://doi.org/10.1007/s00285-024-02068-x
  • [14] Z. Yaagoub, A. Karam, Global stability of multi-strain SEIR epidemic model with vaccination strategy, Math. Comput. Appl., 28(1) (2023), 18 pages. https://doi.org/10.3390/mca28010009
  • [15] Y. R. Kim, Y. Min, J. N. Okogun-Odompley, A mathematical model of COVID-19 with multiple variants of the virus under optimal control in Ghana, Plos One, 19(7) (2024), Article ID e0303791, 30 pages. https://doi.org/10.1371/journal.pone.0303791
  • [16] K. Chen, F. Wei, X. Zhang, et al., Dynamics of an SVEIR transmission model with protection awareness and two strains, Infect. Dis. Model., 10(1) (2025), 207-228. https://doi.org/10.1016/j.idm.2024.10.001
  • [17] A. B. Gumel, Causes of backward bifurcations in some epidemiological models, J. Math. Anal. Appl., 395(1) (2012), 355-365. https://doi.org/10.1016/j.jmaa.2012.04.077
  • [18] S. I. Rwat, A. A. A. Noor, Backward bifurcation and hysteresis in a mathematical model of COVID19 with imperfect vaccine, Matematika, 39(1) (2023), 87-99. https://doi.org/10.11113/matematika.v39.n1.1458
  • [19] B. I. Omede, S. A. Jose, J. Anuwat, et al., Mathematical analysis on the transmission dynamics of delta and omicron variants of COVID-19 in the United States, Model. Earth Syst. Environ., 10 (2024), 7383-7420. https://doi.org/10.1007/s40808-024-02101-4
  • [20] H. A. Fatahillah, A. Dipo, Forward and backward bifurcation analysis from an imperfect vaccine efficacy model with saturated treatment and saturated infection, Jambura J. Biomathematics, 5(2) (2024), 132-143. https://doi.org/10.37905/jjbm.v5i2.28810
  • [21] I. M. Foppa, A Historical Introduction to Mathematical Modeling of Infectious Diseases: Seminal Papers in Epidemiology, Academic Press, Amsterdam, 2017.
  • [22] O. Diekmann, J. A. P. Heesterbeek, J. A. J. Metz, On the definition and the computation of the basic reproduction ratio R0 in models for infectious diseases in heterogeneous populations, J. Math. Biol., 28 (1990), 365-382. https://doi.org/10.1007/BF00178324
  • [23] M. Martcheva, An Introduction to Mathematical Epidemiology, Springer, New York, 2015.
  • [24] M. Y. Li, An Introduction to Mathematical Modeling of Infectious Diseases, Springer Cham, 2018. https://doi.org/10.1007/978-3-319-72122-4
  • [25] Turkish Statistical Institute, Address-based population registration system results database, (2022). Available at: https://data.tuik.gov.tr/Bulten/Index?p=The-Results-of-Address-Based-Population-Registration-System-2022-49685&dil=2. Accessed September 10, 2024.
  • [26] Turkish Statistical Institute, Life tables, (2023). Available at: https://data.tuik.gov.tr/Bulten/Index?p=Hayat-Tablolari-2020-2022-49726. Accessed September 2, 2024.
  • [27] S. Dayan, COVID-19 ve as¸ı, Dicle Med. J., 48(1) (2021), 98-113. https://doi.org/10.5798/dicletip.1005040
  • [28] Z. Wang, F. Muecksch, D. Schaefer-Babajew, et al., Naturally enhanced neutralizing breadth against SARS-CoV-2 one year after infection, Nature, 595 (2021), 426-431. https://doi.org/10.1038/s41586-021-03696-9
  • [29] Republic of T¨urkiye Ministry of Health, COVID-19 information platform database. Available at: https://covid19.saglik.gov.tr/TR-66935/ genel-koronavirus-tablosu.html. Accessed August 4, 2024.
  • [30] A. Saltelli, M. Ratto, T. Andres, et al., Global Sensitivity Analysis: The Primer, Wiley, West Sussex, 2008. https://doi.org/10.1002/9780470725184
  • [31] S. Marino, I. B. Hogue, C. J. Ray, et al., A methodology for performing global uncertainty and sensitivity analysis in systems biology, J. Theor. Biol., 254(1) (2008), 178-196. https://doi.org/10.1016/j.jtbi.2008.04.011
  • [32] Y. M. Rangkuti, A. L. Firmansyah, Sensitivity analysis of SEIR epidemic model of COVID-19 spread in Indonesia, J. Phys. Conf. Ser., 2193 (2022), Article ID 012092, 6 pages. https://doi.org/10.1088/1742-6596/2193/1/012092

Mathematical Model of COVID-19 with Imperfect Vaccine and Virus Mutation

Year 2025, Volume: 8 Issue: 2, 56 - 74, 28.06.2025
https://doi.org/10.33187/jmsm.1659843

Abstract

In this study, we investigated the effect of a partially protective vaccine on COVID-19 infection with the original and mutant viruses using a deterministic mathematical model. The model we developed consists of $S$ (susceptible), $V$ (vaccinated), $I_1$ (infected with the original virus), $I_2$ (infected with the mutant virus), and $R$ (recovered) subcompartments. In the model, we examined the effect of both artificial active immunity (vaccinated) and natural active immunity (infected). Since it is known that the recovery and mortality rates of the original virus and the mutant virus are different in COVID-19, we took this into account in the study. First of all, we obtained the basic reproduction number using the next-generation matrix method. We analyzed the local stability of the disease-free equilibrium point and the endemic equilibrium point of the model using the Routh-Hurwitz criterion and the global stability with the help of Lyapunov functions. Using the Castillo-Chavez and Song Bifurcation Theorem, we demonstrate the existence of a backward bifurcation that occurs when the vaccine is not effective enough, leading to the simultaneous existence of both disease-free and endemic equilibrium points, even when the basic reproduction number is below 1. We estimated the three model parameters by parameter estimation and identified the model-sensitive parameters by local sensitivity analysis. We found that the parameter representing vaccine efficacy is the most sensitive to the basic reproduction number, and that increasing vaccine efficacy will reduce the average number of secondary cases. The three different simulations we present to illustrate the basic mechanisms underlying the dynamics of our model and to support the analytical findings suggest that there is a strong relationship between vaccine efficacy and the course of the epidemic, and that it is necessary to produce vaccines with higher efficacy and increase the vaccination rate to reduce the average number of secondary cases and the likelihood that infected individuals will remain under the influence of the epidemic for a long time.

References

  • [1] A. B. Gumel, E. A. Iboi, C. N. Ngonghala, et al., A primer on using mathematics to understand COVID-19 dynamics: Modeling, analysis and simulations, Infect. Dis. Model., 6 (2021), 148-168. https://doi.org/10.1016/j.idm.2020.11.005
  • [2] W. O. Kermack, A. G. McKendrick, A contribution to the mathematical theory of epidemics, Proc. R. Soc. Lond. A, 115(772) (1927), 700-721. https://doi.org/10.1098/rspa.1927.0118
  • [3] T. D. Frank, COVID-19 interventions in some European countries induced bifurcations stabilizing low death states against high death states: An eigenvalue analysis based on the order parameter concept of synergetics, Chaos Solitons Fractals, 140 (2020), Article ID 110194. https://doi.org/10.1016/j.chaos.2020.110194
  • [4] N. Piovella, Analytical solution of SEIR model describing the free spread of the COVID-19 pandemic, Chaos Solitons Fractals, 140 (2020), Article ID 110243, 6 pages. https://doi.org/10.1016/j.chaos.2020.110243
  • [5] A. Din, Y. Li, T. Khan, et al., Mathematical analysis of spread and control of the novel corona virus (COVID-19) in China, Chaos Solitons Fractals, 141 (2020), Article ID 110286. https://doi.org/10.1016/j.chaos.2020.110286
  • [6] A. E. Matouk, Complex dynamics in susceptible-infected models for COVID-19 with multi-drug resistance, Chaos Solitons Fractals, 140 (2020), Article ID 110257, 13 pages. https://doi.org/10.1016/j.chaos.2020.110257
  • [7] H. Bulut, M. Gölgeli, F. M. Atay, Modelling personal cautiousness during the COVID-19 pandemic: A case study for Turkey and Italy, Nonlinear Dyn., 105 (2021), 957-969. https://doi.org/10.1007/s11071-021-06320-7
  • [8] Ö. F. Gözükızıl, N. Köker, Application of an epidemic model to Turkey data and stability analysis for the COVID-19 pandemic, Sakarya Univ. J. Sci., 25(6) (2021), 1438-1445. https://doi.org/10.16984/saufenbilder.980797
  • [9] S. Boulaaras, R. Ramalingam, A. J. Gnanaprakasam, SEIR model for COVID-19: Stability of the standard coronavirus factor and control mechanism, Eur. Phys. J. Spec. Top., 232 (2023), 2485-2495. https://doi.org/10.1140/epjs/s11734-023-00915-4
  • [10] C. Xu, M. Farman, A. Hasan, et al., Lyapunov stability and wave analysis of Covid-19 omicron variant of real data with fractional operator, Alex. Eng. J., 61(12) (2022), 11787-11802. https://doi.org/10.1016/j.aej.2022.05.025
  • [11] S. Jamil, M. Farman, A, Akgül, et al., Fractional order age dependent Covid-19 model: An equilibria and quantitative analysis with modeling, Results Phys., 53 (2023) Article ID 106928, 17 pages. https://doi.org/10.1016/j.rinp.2023.106928
  • [12] U. A. P. de Le´on, E. Avila-Vales, K. L. Huang, Modeling COVID-19 dynamic using a two-strain model with vaccination, Chaos Solitons Fractals, 157 (2022), Article ID 111927, 17 pages. https://doi.org/10.1016/j.chaos.2022.111927
  • [13] S. Bugalia, J. P. Tripathi, H. Wang, Mutations make pandemics worse or better: Modeling SARS-CoV-2 variants and imperfect vaccination, J. Math. Biol., 88 (2024), Article ID 45, 50 pages. https://doi.org/10.1007/s00285-024-02068-x
  • [14] Z. Yaagoub, A. Karam, Global stability of multi-strain SEIR epidemic model with vaccination strategy, Math. Comput. Appl., 28(1) (2023), 18 pages. https://doi.org/10.3390/mca28010009
  • [15] Y. R. Kim, Y. Min, J. N. Okogun-Odompley, A mathematical model of COVID-19 with multiple variants of the virus under optimal control in Ghana, Plos One, 19(7) (2024), Article ID e0303791, 30 pages. https://doi.org/10.1371/journal.pone.0303791
  • [16] K. Chen, F. Wei, X. Zhang, et al., Dynamics of an SVEIR transmission model with protection awareness and two strains, Infect. Dis. Model., 10(1) (2025), 207-228. https://doi.org/10.1016/j.idm.2024.10.001
  • [17] A. B. Gumel, Causes of backward bifurcations in some epidemiological models, J. Math. Anal. Appl., 395(1) (2012), 355-365. https://doi.org/10.1016/j.jmaa.2012.04.077
  • [18] S. I. Rwat, A. A. A. Noor, Backward bifurcation and hysteresis in a mathematical model of COVID19 with imperfect vaccine, Matematika, 39(1) (2023), 87-99. https://doi.org/10.11113/matematika.v39.n1.1458
  • [19] B. I. Omede, S. A. Jose, J. Anuwat, et al., Mathematical analysis on the transmission dynamics of delta and omicron variants of COVID-19 in the United States, Model. Earth Syst. Environ., 10 (2024), 7383-7420. https://doi.org/10.1007/s40808-024-02101-4
  • [20] H. A. Fatahillah, A. Dipo, Forward and backward bifurcation analysis from an imperfect vaccine efficacy model with saturated treatment and saturated infection, Jambura J. Biomathematics, 5(2) (2024), 132-143. https://doi.org/10.37905/jjbm.v5i2.28810
  • [21] I. M. Foppa, A Historical Introduction to Mathematical Modeling of Infectious Diseases: Seminal Papers in Epidemiology, Academic Press, Amsterdam, 2017.
  • [22] O. Diekmann, J. A. P. Heesterbeek, J. A. J. Metz, On the definition and the computation of the basic reproduction ratio R0 in models for infectious diseases in heterogeneous populations, J. Math. Biol., 28 (1990), 365-382. https://doi.org/10.1007/BF00178324
  • [23] M. Martcheva, An Introduction to Mathematical Epidemiology, Springer, New York, 2015.
  • [24] M. Y. Li, An Introduction to Mathematical Modeling of Infectious Diseases, Springer Cham, 2018. https://doi.org/10.1007/978-3-319-72122-4
  • [25] Turkish Statistical Institute, Address-based population registration system results database, (2022). Available at: https://data.tuik.gov.tr/Bulten/Index?p=The-Results-of-Address-Based-Population-Registration-System-2022-49685&dil=2. Accessed September 10, 2024.
  • [26] Turkish Statistical Institute, Life tables, (2023). Available at: https://data.tuik.gov.tr/Bulten/Index?p=Hayat-Tablolari-2020-2022-49726. Accessed September 2, 2024.
  • [27] S. Dayan, COVID-19 ve as¸ı, Dicle Med. J., 48(1) (2021), 98-113. https://doi.org/10.5798/dicletip.1005040
  • [28] Z. Wang, F. Muecksch, D. Schaefer-Babajew, et al., Naturally enhanced neutralizing breadth against SARS-CoV-2 one year after infection, Nature, 595 (2021), 426-431. https://doi.org/10.1038/s41586-021-03696-9
  • [29] Republic of T¨urkiye Ministry of Health, COVID-19 information platform database. Available at: https://covid19.saglik.gov.tr/TR-66935/ genel-koronavirus-tablosu.html. Accessed August 4, 2024.
  • [30] A. Saltelli, M. Ratto, T. Andres, et al., Global Sensitivity Analysis: The Primer, Wiley, West Sussex, 2008. https://doi.org/10.1002/9780470725184
  • [31] S. Marino, I. B. Hogue, C. J. Ray, et al., A methodology for performing global uncertainty and sensitivity analysis in systems biology, J. Theor. Biol., 254(1) (2008), 178-196. https://doi.org/10.1016/j.jtbi.2008.04.011
  • [32] Y. M. Rangkuti, A. L. Firmansyah, Sensitivity analysis of SEIR epidemic model of COVID-19 spread in Indonesia, J. Phys. Conf. Ser., 2193 (2022), Article ID 012092, 6 pages. https://doi.org/10.1088/1742-6596/2193/1/012092
There are 32 citations in total.

Details

Primary Language English
Subjects Applied Mathematics (Other)
Journal Section Articles
Authors

Ceren Gürbüz Can 0009-0009-6017-4815

Sebaheddin Şevgin 0000-0002-2163-9896

Early Pub Date May 27, 2025
Publication Date June 28, 2025
Submission Date March 17, 2025
Acceptance Date May 7, 2025
Published in Issue Year 2025 Volume: 8 Issue: 2

Cite

APA Gürbüz Can, C., & Şevgin, S. (2025). Mathematical Model of COVID-19 with Imperfect Vaccine and Virus Mutation. Journal of Mathematical Sciences and Modelling, 8(2), 56-74. https://doi.org/10.33187/jmsm.1659843
AMA Gürbüz Can C, Şevgin S. Mathematical Model of COVID-19 with Imperfect Vaccine and Virus Mutation. Journal of Mathematical Sciences and Modelling. June 2025;8(2):56-74. doi:10.33187/jmsm.1659843
Chicago Gürbüz Can, Ceren, and Sebaheddin Şevgin. “Mathematical Model of COVID-19 With Imperfect Vaccine and Virus Mutation”. Journal of Mathematical Sciences and Modelling 8, no. 2 (June 2025): 56-74. https://doi.org/10.33187/jmsm.1659843.
EndNote Gürbüz Can C, Şevgin S (June 1, 2025) Mathematical Model of COVID-19 with Imperfect Vaccine and Virus Mutation. Journal of Mathematical Sciences and Modelling 8 2 56–74.
IEEE C. Gürbüz Can and S. Şevgin, “Mathematical Model of COVID-19 with Imperfect Vaccine and Virus Mutation”, Journal of Mathematical Sciences and Modelling, vol. 8, no. 2, pp. 56–74, 2025, doi: 10.33187/jmsm.1659843.
ISNAD Gürbüz Can, Ceren - Şevgin, Sebaheddin. “Mathematical Model of COVID-19 With Imperfect Vaccine and Virus Mutation”. Journal of Mathematical Sciences and Modelling 8/2 (June 2025), 56-74. https://doi.org/10.33187/jmsm.1659843.
JAMA Gürbüz Can C, Şevgin S. Mathematical Model of COVID-19 with Imperfect Vaccine and Virus Mutation. Journal of Mathematical Sciences and Modelling. 2025;8:56–74.
MLA Gürbüz Can, Ceren and Sebaheddin Şevgin. “Mathematical Model of COVID-19 With Imperfect Vaccine and Virus Mutation”. Journal of Mathematical Sciences and Modelling, vol. 8, no. 2, 2025, pp. 56-74, doi:10.33187/jmsm.1659843.
Vancouver Gürbüz Can C, Şevgin S. Mathematical Model of COVID-19 with Imperfect Vaccine and Virus Mutation. Journal of Mathematical Sciences and Modelling. 2025;8(2):56-74.

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