Research article

Mathematical study of transmission dynamics of SARS-CoV-2 with waning immunity

  • Received: 29 April 2022 Revised: 05 June 2022 Accepted: 13 June 2022 Published: 28 June 2022
  • MSC : 92-10, 92D30, 34D23

  • The aim of this work is to provide a new mathematical model that studies transmission dynamics of Coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The model captures the dynamics of the disease taking into consideration some measures and is represented by a system of nonlinear ordinary differential equations including seven classes, which are susceptible class (S), exposed class (E), asymptomatic infected class (A), severely infected class (V), hospitalized class (H), hospitalized class but in ICU (C) and recovered class (R). We prove positivity and boundedness of solutions, compute the basic reproduction number, and investigate asymptotic stability properties of the proposed model. As a consequence, dynamical properties of the model are established fully and some mitigation and prevention measures of COVID-19 outbreaks are also suggested. Furthermore, the model is fitted to COVID-19 confirmed cases in South Africa during the Omicron wave from November 27, 2021 to January 20, 2022 which helped determine the model parameters value for our numerical simulation. A set of numerical experiments using real data is conducted to support and illustrate the theoretical findings. Numerical simulation results show that fast waning of infection-induced immunity can increase the occurrence of outbreaks.

    Citation: Oluwaseun F. Egbelowo, Justin B. Munyakazi, Manh Tuan Hoang. Mathematical study of transmission dynamics of SARS-CoV-2 with waning immunity[J]. AIMS Mathematics, 2022, 7(9): 15917-15938. doi: 10.3934/math.2022871

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  • The aim of this work is to provide a new mathematical model that studies transmission dynamics of Coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The model captures the dynamics of the disease taking into consideration some measures and is represented by a system of nonlinear ordinary differential equations including seven classes, which are susceptible class (S), exposed class (E), asymptomatic infected class (A), severely infected class (V), hospitalized class (H), hospitalized class but in ICU (C) and recovered class (R). We prove positivity and boundedness of solutions, compute the basic reproduction number, and investigate asymptotic stability properties of the proposed model. As a consequence, dynamical properties of the model are established fully and some mitigation and prevention measures of COVID-19 outbreaks are also suggested. Furthermore, the model is fitted to COVID-19 confirmed cases in South Africa during the Omicron wave from November 27, 2021 to January 20, 2022 which helped determine the model parameters value for our numerical simulation. A set of numerical experiments using real data is conducted to support and illustrate the theoretical findings. Numerical simulation results show that fast waning of infection-induced immunity can increase the occurrence of outbreaks.



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    [1] L. J. S. Allen, Introduction to mathematical biology, Pearson, 2016.
    [2] F. Brauer, C. Castillo-Chavez, Z. Feng, Mathematical models in epidemiology, 1 Ed., Springer, 2019.
    [3] F. Brauer, Mathematical epidemiology: Past, present, and future, Infect. Dis. Model., 2 (2017), 113–127. https://doi.org/10.1016/j.idm.2017.02.001 doi: 10.1016/j.idm.2017.02.001
    [4] F. Brauer, Compartmental models in epidemiology, In: Mathematical epidemiology, Springer, Berlin, Heidelberg, 2008. https://doi.org/10.1007/978-3-540-78911-6_2
    [5] N. C. Grassly, C. Fraser, Mathematical models of infectious disease transmission, Nat. Rev. Microbiol., 6 (2008), 477–487. https://doi.org/10.1038/nrmicro1845 doi: 10.1038/nrmicro1845
    [6] K. Hattaf, H. Dutta, Mathematical modelling and analysis of infectious diseases, Springer, 2020. https://doi.org/10.1007/978-3-030-49896-2
    [7] S. Tyagi, S. C. Martha, S. Abbas, A. Debbouch, Mathematical modeling and analysis for controlling the spread of infectious diseases, Chaos, Solitons Fract., 144 (2021), 110707. https://doi.org/10.1016/j.chaos.2021.110707 doi: 10.1016/j.chaos.2021.110707
    [8] M. Y. Li, An introduction to mathematical modeling of infectious diseases, 1 Ed., Springer, 2018.
    [9] M. Martcheva, An introduction to mathematical epidemiology, 1 Ed., Springer, 2015.
    [10] M. A. Nowak, R. M. May, Viral dynamics, Oxford: Oxford University Press, 2000.
    [11] Y. Xie, Z. Wang, Transmission dynamics, global stability and control strategies of a modified SIS epidemic model on complex networks with an infective medium, Math. Comput. Simul., 188 (2021), 23–34. https://doi.org/10.1016/j.matcom.2021.03.029 doi: 10.1016/j.matcom.2021.03.029
    [12] Y. Xie, Z. Wang, A ratio-dependent impulsive control of an SIQS epidemic model with non-linear incidence, Appl. Math. Comput., 423 (2022), 127018. https://doi.org/10.1016/j.amc.2022.127018 doi: 10.1016/j.amc.2022.127018
    [13] M. T. Hoang, O. F. Egbelowo, Nonstandard finite difference schemes for solving an SIS epidemic model with standard incidence, Rend. Circ. Mat. Palermo, Ser. II, 69 (2020), 753–769. https://doi.org/10.1007/s12215-019-00436-x doi: 10.1007/s12215-019-00436-x
    [14] M. T. Hoang, O. F. Egbelowo, On the global asymptotic stability of a hepatitis B epidemic model and its solutions by nonstandard numerical schemes, Bol. Soc. Mat. Mex., 26 (2020), 1113–1134. https://doi.org/10.1007/s40590-020-00275-2 doi: 10.1007/s40590-020-00275-2
    [15] M. T. Hoang, O. F. Egbelowo, Dynamics of a fractional-order hepatitis b epidemic model and its solutions by nonstandard numerical schemes, In: K. Hattaf, H. Dutta, Mathematical modelling and analysis of infectious diseases, Studies in Systems, Decision and Control, Springer, 2020. https://doi.org/10.1007/978-3-030-49896-2_5
    [16] WHO Director-General's opening remarks at the media briefing on COVID19-March 2020, 2020. Available from: https://www.who.int/director-general/speeches/detail/who-director-general-s-opening-remarks-at-the-media-briefing-on-covid-19---20-march-2020.
    [17] A. Anirudh, Mathematical modeling and the transmission dynamics in predicting the Covid-19-What next in combating the pandemic, Infect. Dis. Model., 5 (2020), 366–374. https://doi.org/10.1016/j.idm.2020.06.002 doi: 10.1016/j.idm.2020.06.002
    [18] M. Kimathi, S. Mwalili, V. Ojiambo, D. K. Gathungu, Age-structured model for COVID-19: Effectiveness of social distancing and contact reduction in Kenya, Infect. Dis. Model., 6 (2021), 15–23. https://doi.org/10.1016/j.idm.2020.10.012 doi: 10.1016/j.idm.2020.10.012
    [19] O. Iyiola, B. Oduro, T. Zabilowicz, B. Iyiola, D. Kenes, System of time fractional models for COVID-19: Modeling, analysis and solutions, Symmetry, 13 (2021), 787. https://doi.org/10.3390/sym13050787 doi: 10.3390/sym13050787
    [20] M. Kinyili, J. B. Munyakazi, A. Y. A. Mukhtar, Assessing the impact of vaccination on COVID-19 in South Africa using mathematical modeling, Appl. Math. Inf. Sci., 15 (2021), 701–716. https://doi.org/10.18576/amis/150604 doi: 10.18576/amis/150604
    [21] V. S. Panwar, P. S. Sheik Uduman, J. F. Gomez-Aguilar, Mathematical modeling of coronavirus disease COVID-19 dynamics using CF and ABC non-singular fractional derivatives, Chaos, Solitons Fract., 145 (2021), 110757. https://doi.org/10.1016/j.chaos.2021.110757 doi: 10.1016/j.chaos.2021.110757
    [22] R. K. Rai, S. Khajanchi, P. K. Tiwari, E. Venturino, A. K. Misra, Impact of social media advertisements on the transmission dynamics of COVID-19 pandemic in India, J. Appl. Math. Comput., 68 (2022), 19–44. https://doi.org/10.1007/s12190-021-01507-y doi: 10.1007/s12190-021-01507-y
    [23] A. ul Rehman, R. Singh, P. Agarwal, Modeling, analysis and prediction of new variants of COVID-19 and dengue co-infection on complex network, Chaos, Solitons Fract., 150 (2021), 111008. https://doi.org/10.1016/j.chaos.2021.111008 doi: 10.1016/j.chaos.2021.111008
    [24] M. Serhani, H. Labbardi, Mathematical modeling of COVID-19 spreading with asymptomatic infected and interacting peoples, J. Appl. Math. Comput., 66 (2021), 1–20. https://doi.org/10.1007/s12190-020-01421-9 doi: 10.1007/s12190-020-01421-9
    [25] Y. Guo, T. Li, Modeling and dynamic analysis of novel coronavirus pneumonia (COVID-19) in China, J. Appl. Math. Comput., 2021. https://doi.org/10.1007/s12190-021-01611-z
    [26] S. He, Y. Peng, K. Sun, SEIR modeling of the COVID-19 and its dynamics, Nonlinear Dyn., 101 (2020), 1667–1680. https://doi.org/10.1007/s11071-020-05743-y doi: 10.1007/s11071-020-05743-y
    [27] S. Annas, M. I. Pratama, M. Rifandi, W. Sanusi, S. Side, Stability analysis and numerical simulation of SEIR model for pandemic COVID-19 spread in Indonesia, Chaos, Solitons Fract., 139 (2020), 110072. https://doi.org/10.1016/j.chaos.2020.110072 doi: 10.1016/j.chaos.2020.110072
    [28] B. A. Baba, B. Bilgehan, Optimal control of a fractional order model for the COVID-19 pandemic, Chaos, Solitons Fract., 144 (2021), 110678. https://doi.org/10.1016/j.chaos.2021.110678 doi: 10.1016/j.chaos.2021.110678
    [29] D. Dwomoh, S. Iddi, B. Adu, J. M. Aheto, K. M. Sedzro, J. Fobil, et al., Mathematical modeling of COVID-19 infection dynamics in Ghana: Impact evaluation of integrated government and individual level interventions, Infect. Dis. Model., 6 (2021), 381–397. https://doi.org/10.1016/j.idm.2021.01.008 doi: 10.1016/j.idm.2021.01.008
    [30] M. Higazy, Novel fractional order SIDARTHE mathematical model of COVID-19 pandemic, Chaos, Solitons Fract., 138 (2020), 110007. https://doi.org/10.1016/j.chaos.2020.110007 doi: 10.1016/j.chaos.2020.110007
    [31] M. M. Hikal, M. M. A. Elsheikh, W. K. Zahra, Stability analysis of COVID-19 model with fractional-order derivative and a delay in implementing the quarantine strategy, J. Appl. Math. Comput., 68 (2021), 295–321. https://doi.org/10.1007/s12190-021-01515-y doi: 10.1007/s12190-021-01515-y
    [32] J. M. Tchuenche, N. Dube, C. P. Bhunu, R. J. Smith, C. T. Bauch, The impact of media coverage on the transmission dynamics of human influenza, BMC Public Health, 11 (2011), 1–16. https://doi.org/10.1186/1471-2458-11-S1-S5 doi: 10.1186/1471-2458-11-S1-S5
    [33] A. R. Tuite, D. N. Fisman, A. L. Greer, Mathematical modelling of COVID-19 transmission and mitigation strategies in the population of Ontario, Canada, CMAJ, 192 (2020), E497–E505. https://doi.org/10.1503/cmaj.200476 doi: 10.1503/cmaj.200476
    [34] M. Wang, Y. Hu, L. Wu, Dynamic analysis of a SIQR epidemic model considering the interaction of environmental differences, J. Appl. Math. Comput., 2021. https://doi.org/10.1007/s12190-021-01628-4.
    [35] Z. Zhang, A. Zeb, O. F. Egbelowo, V. S. Erturk, Dynamics of a fractional order mathematical model for COVID-19 epidemic, Adv. Differ. Equ., 2020 (2020), 1–16. https://doi.org/10.1186/s13662-020-02873-w doi: 10.1186/s13662-020-02873-w
    [36] J. M. Garrido, D. Martínez-Rodríguez, F. Rodríguez-Serrano, J. M. Pérez-Villares, A. Ferreiro-Marzal, M. M. Jiménez-Quintana, et al., Mathematical model optimized for prediction and health care planning for COVID-19, Med. Intensiva, 46 (2022), 248–258. https://doi.org/10.1016/j.medin.2021.02.014 doi: 10.1016/j.medin.2021.02.014
    [37] P. van den Driessche, J. Watmough, Reproduction numbers and sub-threshold endemic equilibria for compartmental models of disease transmission, Math. Biosci., 180 (2002), 29–48. https://doi.org/10.1016/S0025-5564(02)00108-6 doi: 10.1016/S0025-5564(02)00108-6
    [38] S. Xia, K. Duan, Y. Zhang, D. Zhao, H. Zhang, Z. Xie, et al., Effect of an inactivated vaccine against SARS-CoV-2 on safety and immunogenicity outcomes: Interim analysis of 2 randomized clinical trials, JAMA, 324 (2020), 951–960. https://doi.org/10.1001/jama.2020.15543 doi: 10.1001/jama.2020.15543
    [39] G. Gonzalez-Parra, A. J. Arenas, Nonlinear dynamics of the introduction of a new SARS-CoV-2 variant with different infectiousness, Mathematics, 9 (2021), 1564. https://doi.org/10.3390/math9131564 doi: 10.3390/math9131564
    [40] H. L. Smith, P. E. Waltman, The theory of the chemostat: Dynamics of microbial competition, Cambridge University Press, 2008.
    [41] C. Castillo-Chavez, Z. Feng, W. Huang, On the computation of R0 and its role in global stability, Mathematical Approaches for Emerging and Re-emerging Infection Diseases: An Introduction, 125 (2002), 31–65.
    [42] E. A. Iboi, C. N. Ngonghala, A. B. Gumel, Will an imperfect vaccine curtail the COVID-19 pandemic in the U.S., Infect. Dis. Model., 5 (2020), 510–524. https://doi.org/10.1016/j.idm.2020.07.006 doi: 10.1016/j.idm.2020.07.006
    [43] World Health Organization. Available from: https://covid19.who.int/region/afro/country/za.
    [44] South Africa Coronavirus. Available from: https://sacoronavirus.co.za.
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