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Global Dynamics and Optimal Control of Multi-Age Structured Vector Disease Model with Vaccination, Relapse and General Incidence

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Abstract

Vaccine effectiveness, disease recovery and recurrence are important issues that must be faced in the prevention and control of vector-borne infectious diseases. We develop, in this paper, a dynamical model of vector disease with multi-age-structure to describe the transmission of parasites (or bacteria) between vectors and hosts, where vaccination, relapse and general incidence are introduced to study how these factors influence the spread and control of disease. First, the accurate formulation of the basic reproduction number is gained, which determines the existence and local asymptotic stability of the disease-free and endemic steady states. Further, by utilizing the fluctuation theorem and the method of Lyapunov function, we verify that the disease-free steady state is globally asymptotically stable if the basic reproduction number is less than one. In addition, we also prove that the endemic steady state of this model without relapse is globally asymptotically stable if the basic reproduction number is greater than one. Moreover, the optimal control problem for our model is formulated and analyzed. Finally, some numerical simulations are conducted to explain these analytical results.

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References

  1. Malaria, https://www.who.int/news-room/fact-sheets/detail/malaria [30 September 2021]

  2. Ross, R.: The prevention of malaria, 2nd edn. Murray, London (1911)

    Google Scholar 

  3. Dietz, K.: Models for parasitic disease control. Bull. Inst. Internat. Statist. 46, 531–544 (1975)

    MATH  Google Scholar 

  4. Macdonald, G.: The epidemiology and control of malaria. Oxford University Press, London (1957)

    Google Scholar 

  5. Niger, A.M., Gumel, A.B.: Mathematical analysis of the role of repeated exposure on malaria transmission dynamics. Differ. Equat. Dyn. Sys. 16(3), 251–287 (2008)

    Article  MATH  Google Scholar 

  6. Chitnis, N., Cushing, J.M., Hyman, J.M.: Bifurcation analysis of a mathematical model for malaria transmission. SIAM J. Appl. Math. 67(1), 24–45 (2006)

    Article  MATH  Google Scholar 

  7. Osman, M.A., Li, J.H: Analysis of a vector-bias malaria transmission model with application to Mexico, Sudan and Democratic Republic of the Congo. J. Theor. Biol. 464, 72–84 (2019)

  8. Zheng, T.T., Nie, L.F., Teng, Z.D., Luo, Y.T.: Competitive exclusion in a multi-strain malaria transmission model with incubation period. Chaos Soliton. Fract. 131, 109545 (2020)

    Article  MATH  Google Scholar 

  9. Martcheva, M.: An introduction to mathematical epidemiology. Springer, New York (2015)

    Book  MATH  Google Scholar 

  10. Duan, X.C., Yuan, S.L., Li, X.Z.: Global stability of an SVIR model with age of vaccination. Appl. Math. Comput. 226, 528–540 (2014)

    MATH  Google Scholar 

  11. Yang, J.Y., Chen, Y.M., Xu, F.: Effect of infection age on an SIS epidemic model on complex networks. J. Math. Biol. 73, 1227–1249 (2016)

    Article  MATH  Google Scholar 

  12. Yang, J.Y., Xu, R., Li, J.X.: Threshold dynamics of an age-space structured brucellosis disease model with Neumann boundary condition. Nonlinear Anal. Real 50, 192–217 (2019)

    Article  MATH  Google Scholar 

  13. Hathout, F.Z., Touaoula, T.M., Djilali, S.: Mathematical analysis of a triple age dependent epidemiological model with including a protection strategy. Discrete Cont. Dyn. B. 27, 7409–7443 (2022)

    Article  MATH  Google Scholar 

  14. Wang, S.F., Nie, L.F.: Global dynamics for a vector-borne disease model with class-age-dependent vaccination, latency and general incidence rate. Qual. Theor. Dyn. Syst. 19, 1–34 (2020)

    Article  MATH  Google Scholar 

  15. Wang, X., Chen, Y.M., Liu, S.Q.: Dynamics of an age-structured host-vector model for malaria transmission. Math. Methods Appl. Sci. 41, 1966–1987 (2018)

    Article  MATH  Google Scholar 

  16. Liu, L.L., Wang, J.L., Liu, X.N.: Global stability of an SEIR epidemic model with age-dependent latency and relapse. Nonlinear Anal. Real 24, 18–35 (2015)

    Article  MATH  Google Scholar 

  17. Magal, P.: Compact attractors for time-periodic age-structured population models. Electron. J. Differ. Equ. 65, 1–35 (2001)

    MATH  Google Scholar 

  18. Yang, J.Y., Modnak, C., Wang, J.: Dynamical analysis and optimal control simulation for an age-structured cholera transmission model. J. Franklin Inst. 356, 8438–8467 (2019)

    Article  MATH  Google Scholar 

  19. Dang, Y.X., Qiu, Z.P., Li, X.Z., Martcheva, M.: Global dynamics of a vector-host epidemic model with age of infection. Math. Biosci. Eng. 14, 1159–1186 (2017)

    Article  MATH  Google Scholar 

  20. Duan, X.C., Cheng, H.H., Martcheva, M., Yuan, S.L.: Dynamics of an age structured heroin transmission model with imperfect vaccination. Int. J. Bifurcat. Chaos 31, 2150157 (2021)

    Article  MATH  Google Scholar 

  21. Tumwiine, J., Mugisha, J.Y.T., Luboobi, L.S.: A mathematical model for the dynamics of malaria in a human host and mosquito vector with temporary immunity. Appl. Math. Comput. 189, 1953–1965 (2007)

    MATH  Google Scholar 

  22. Zhang, F.M., Qiu, Z.P., Huang, A.J., Zhao, X.: Optimal control and cost-effectiveness analysis of a Huanglongbing model with comprehensive interventions. Appl. Math. Model. 90, 719–741 (2021)

    Article  MATH  Google Scholar 

  23. Mohammed-Awel, J., Numfor, E., Zhao, R.J., Lenhart, S.: A new mathematical model studying imperfect vaccination: optimal control analysis. J. Math. Anal. Appl. 500, 125132 (2021)

    Article  MATH  Google Scholar 

  24. Jan, R., Xiao, Y.N.: Effect of partial immunity on transmission dynamics of dengue disease with optimal control. Math. Methods Appl. Sci. 42, 1967–1983 (2019)

    Article  MATH  Google Scholar 

  25. Tang, B., Xiao, Y.N., Tang, S.Y., Wu, J.H.: Modelling weekly vector control against dengue in the Guangdong Province of China. J. Theor. Biol. 410, 65–76 (2016)

    Article  MATH  Google Scholar 

  26. Jia, P.Q., Yang, J.Y., Li, X.Z.: Optimal control and cost-effective analysis of an age-structured emerging infectious disease model. Infect. Disease Model. 7, 149–169 (2022)

    Article  Google Scholar 

  27. Roop-O, P., Chinviriyasit, W., Chinviriyasit, S.: The effect of incidence function in backward bifurcation for malaria model with temporary immunity. Math. Biosci. 265, 47–64 (2015)

    Article  MATH  Google Scholar 

  28. Kokomo, E., Emvudu, Y.: Mathematical analysis and numerical simulation of an age-structured model of cholera with vaccination and demographic movements. Nonlinear Anal. Real 45, 142–156 (2019)

    Article  MATH  Google Scholar 

  29. Yang, Y., Xu, Y.C.: Global stability of a diffusive and delayed virus dynamics model with Beddington-DeAngelis incidence function and CTL immune response. Comput. Math. Appl. 71, 922–930 (2016)

    Article  MATH  Google Scholar 

  30. Tadmon, C., Foko, S., Rendall, A.D.: Global stability analysis of a delay cell-population model of hepatitis B infection with humoral immune response. Dynam. Syst. 36, 537–559 (2021)

    Article  MATH  Google Scholar 

  31. Kumar, A.: Nilam: Dynamic behavior of an SIR epidemic model along with time delay; Crowley-Martin type incidence rate and Holling type II treatment rate. Int. J. Nonlinear Sci. Num. 20, 757–771 (2019)

    Article  MATH  Google Scholar 

  32. Liu, W.M., Levin, S.A., Iwasa, Y.: Influence of nonlinear incidence rates upon the behavior of SIRS epidemiological models. J. Math. Biol. 23, 187–204 (1986)

    Article  MATH  Google Scholar 

  33. Lu, M., Huang, J.C., Ruan, S.G., Yu, P.: Global dynamics of a susceptible-infectious-recovered epidemic model with a generalized nonmonotone incidence rate. J. Dynam. Differ. Equ. 33, 1625–1661 (2021)

    Article  MATH  Google Scholar 

  34. Hale, J.: Theory of functional differential equations. Springer-Verlag, New York (1971)

    Book  MATH  Google Scholar 

  35. Hirsch, W.M., Hanish, H., Gabriel, J.P.: Differential equation models of some parasitic infections: methods for the study of asymptotic behavior. Comm. Pure Appl. Math. 38, 733–753 (1985)

    Article  MATH  Google Scholar 

  36. Iannelli, M.: Mathematical theory of age-structured population dynamics. Giardini Editori E Stampatori, Pisa (1995)

    Google Scholar 

  37. Thieme, H.R.: Uniform persistence and permanence for non-autonomous semiflows in population biology. Math. Biosci. 166(2), 173–201 (2000)

    Article  MATH  Google Scholar 

  38. Castillo-Chavez, C., Thieme, H.R.: Asymptotically autonomous epidemic models. In: Mathematical Population Dynamics: Analysis of Heterogeneity, Vol. 1, Theory of Epidemics. O. Arino, D.E. Axelrod,M. Kimmel, M. Langlais, eds., Wuerz, Winnipeg, Canada, pp:33-50, (1995)

  39. Thieme, H.R.: Convergence results and a Poincaré-Bendixson trichotomy for asymptotically alltonomous differential equations. J. Math. Biol. 30, 755–463 (1992)

    Article  MATH  Google Scholar 

  40. Kang, Y.H.: Identification problem of two operators for nonlinear systems in Banach spaces. Nonlinear Anal. 70, 1443–1458 (2009)

    Article  MATH  Google Scholar 

  41. Fister, K.R., Gaff, H., Lenhart, S., Numfor, E., Schaefer, E., Wang, J.: Optimal control of vaccination in an age-structured cholera model. In: Chowell, G., Hyman, J.M. (eds.) Mathematical and statistical modeling for emerging and re-emerging infectious diseases, pp. 221–248. Springer, Switzerland (2016)

    Chapter  Google Scholar 

  42. Lenhart, S., Workman, J.T.: Optimal control applied to biological models. Chapman & Hall/Crc, London (2007)

    Book  MATH  Google Scholar 

  43. Smith, H.L., Thieme, H.R.: Dynamical systems and population persistence. Amer. Math. Soc, Providence, RI (2011)

  44. Wang, X., Zhang, Y., Song, X.Y.: An age-structured epidemic model with waning immunity and general nonlinear incidence rate. Int. J. Biomath. 11, 1850069 (2018)

    Article  MATH  Google Scholar 

  45. Wang, X., Chen, Y., Liu, S.: Global dynamics of a vector-borne disease model with infection ages and general incidence rates. Comp. Appl. Math. 37, 4055–4080 (2018)

    Article  MATH  Google Scholar 

  46. Chitnis, N., Hyman, J.M., Cushing, J.M.: Determining important parameters in the spread of malaria through the sensitivity analysis of a mathematical model. Bull Math Biol. 70, 1272–1296 (2008)

    Article  MATH  Google Scholar 

  47. Ngonghala, C.N., Mohammed-Awel, J., Zhao, R.J., Prosper, O.: Interplay between insecticide-treated bed-nets and mosquito demography: implications for malaria control. J. Theor. Biol. 397, 179–192 (2016)

    Article  MATH  Google Scholar 

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Correspondence to Lin-Fei Nie.

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This research is partially supported by the Natural Science Foundation of Xinjiang Uygur Autonomous Region (Grant No. 2021D01E12), the National Natural Science Foundation of China (Grant No. 11961066), the Scientific Research and Innovation Project of Outstanding doctoral students in Xinjiang University (Grant No. XJU2022BS022).

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Wang, SF., Nie, LF. Global Dynamics and Optimal Control of Multi-Age Structured Vector Disease Model with Vaccination, Relapse and General Incidence. Qual. Theory Dyn. Syst. 22, 24 (2023). https://doi.org/10.1007/s12346-022-00724-5

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