Analysis of SVEIL Model of Tuberculosis Disease Spread with Imperfect Vaccination

Authors

  • Handika Lintang Saputra Department of Mathematics, Faculty of Mathematics and Natural Sciences, University of Brawijaya, Malang
  • Isnani Darti Department of Mathematics, Faculty of Mathematics and Natural Sciences, University of Brawijaya, Malang
  • Agus Suryanto Department of Mathematics, Faculty of Mathematics and Natural Sciences, University of Brawijaya, Malang

DOI:

https://doi.org/10.31764/jtam.v7i1.11033

Keywords:

Center manifold theorem, Effective reproduction number, Imperfect vaccination, Tuberculosis.

Abstract

This study proposes a SVEIL model of tuberculosis disease spread with imperfect vaccination. Susceptible individuals can receive imperfect vaccination, but over the time the vaccine efficacy will decrease. Vaccinated individuals are in vulnerable class since they still have probability to get reinfected. The proposed model includes treatment for both high-risk latent and active TB patients. In fact, after getting appropriate treatment (get recovered) the individuals still have bacteria in their body and it is classified to low-risk laten class. Dynamical behaviour of the model is analyzed to understand the local stability equilibrium. The Routh-Hurwitz criterion is used to analyze the local stability equilibrium in disease free equilibrium (DFE) point and Center Manifold theorem is used to prove the local stability of the endemic equilibrium (EE) point. The local stability equilibrium state totally depends on the effective reproduction number R_v. If R_v<1 , then the DFE point is locally asymtotically stable, while if R_v>1 the EE point is locally asymptotically stable . The parameter used in this paper is based on the previous researches related to TB and the initial subpopulations are assumed. Numerical simulations show that the disease transmission rate affect the effective reproduction number, therefore it influences the stability of equilibrium points..

 

Author Biography

Handika Lintang Saputra, Department of Mathematics, Faculty of Mathematics and Natural Sciences, University of Brawijaya, Malang

Mathematics and Natural Science Faculty

References

Aspatwar, A., Gong, W., Wang, S., Wu, X., & Parkkila, S. (2022). Tuberculosis vaccine BCG: the magical effect of the old vaccine in the fight against the COVID-19 pandemic. In International Reviews of Immunology. 41(2), 283–296. Taylor and Francis Ltd. https://doi.org/10.1080/08830185.2021.1922685

Bhargava, S., Choubey, S., & Mishra, S. (2016). Vaccines against tuberculosis: A review. In Indian Journal of Tuberculosis, 63(1), 13-18. https://doi.org/10.1016/j.ijtb.2016.02.005

Brennan, M. J., Stone, M. R., & Evans, T. (2012). A rational vaccine pipeline for tuberculosis [State of the art series. New tools. Number 5 in the series]. The International Journal of Tuberculosis and Lung Disease, 16(12), 1566-1573. https://doi.org/10.5588/ijtld.12.0569

Buonomo, B., & della Marca, R. (2019). Oscillations and hysteresis in an epidemic model with information-dependent imperfect vaccination. Mathematics and Computers in Simulation, 162, 97-114. https://doi.org/10.1016/j.matcom.2019.01.005

Buonomo, B., & Lacitignola, D. (2011). On the backward bifurcation of a vaccination model with nonlinear incidence. Nonlinear Analysis: Modelling and Control, 16(1), 30-46. https://doi.org/10.15388/na.16.1.14113

Castillo-Chavez, C., & Song, B. (2004). Dynamical Models of Tuberculosis and Their Applications. Mathematical Biosciences and Engineering, 1(2), 361–404. http://math.asu.edu/˜mbe/

Dinas Kesehatan Daerah Istimewa Yogyakarta. (2021, February 28). Dampak pandemi covid-19 terhadap penanggulanagan TBC. Https://Www.Dinkes.Jogjaprov.Go.Id/Berita/Detail/Dampak-Pandemi-Covid-19-Terhadap-Penanggulangan-Tbc.

Egonmwan, A. O., & Okuonghae, D. (2019). Mathematical analysis of a tuberculosis model with imperfect vaccine. International Journal of Biomathematics, 12(7), 1-30. https://doi.org/10.1142/S1793524519500736

Fletcher, H. A., Voss, G., Casimiro, D., Neyrolles, O., Williams, A., Kaufmann, S. H. E., McShane, H., & Hatherill, M. (2018). Progress and challenges in TB vaccine development. In F1000Research, 7(199), 1-14. https://doi.org/10.12688/f1000research.13588.1

Harris, R. C., Sumner, T., Knight, G. M., & White, R. G. (2016). Systematic review of mathematical models exploring the epidemiological impact of future TB vaccines. In Human Vaccines and Immunotherapeutics, 12(11), 2813–2832. Taylor and Francis Inc. https://doi.org/10.1080/21645515.2016.1205769

Ika. (2021, March 29). Excessive Focus to Covid-19 Makes TB Cases Less Discovered. Https://Www.Ugm.Ac.Id/En/News/20922-Excessive-Focus-to-Covid-19-Makes-Tb-Cases-Less-Discovered.

Kar, T. K., & Mondal, P. K. (2012). Global Dynamics of a Tuberculosis Epidemic Model and the Influence of Backward Bifurcation. Journal of Mathematical Modelling and Algorithms, 11(4), 433-459. https://doi.org/10.1007/s10852-012-9210-8

Kaufmann, S. H. E., Weiner, J., & von Reyn, C. F. (2017). Novel approaches to tuberculosis vaccine development. In International Journal of Infectious Diseases, 56, 263–267. Elsevier B.V. https://doi.org/10.1016/j.ijid.2016.10.018

Mengistu, A. K., & Witbooi, P. J. (2019). Modeling the Effects of Vaccination and Treatment on Tuberculosis Transmission Dynamics. Journal of Applied Mathematics, 2019, 1-9. https://doi.org/10.1155/2019/7463167

Mangtani, P., Abubakar, I., Ariti, C., Beynon, R., Pimpin, L., Fine, P. E. M., Rodrigues, L. C., Smith, P. G., Lipman, M., Whiting, P. F., & Sterne, J. A. (2014). Protection by BCG vaccine against tuberculosis: A systematic review of randomized controlled trials. Clinical Infectious Diseases, 58(4), 470–480. https://doi.org/10.1093/cid/cit790

Martin, C., Aguilo, N., Marinova, D., & Gonzalo-Asensio, J. (2020). Update on TB vaccine pipeline. Applied Sciences (Switzerland), 10(7), 1-15. https://doi.org/10.3390/app10072632

Murray, J. D. D. (2007). Mathematical Biology: I. An Introduction (Interdisciplinary Applied Mathematics) (Pt. 1). In Interdisciplinary Applied Mathematics (Vol. 1, Issue 1).

Sari, N. P., & Rachmawati, A. S. (2019). Pendidikan Kesehatan Tuberkulosis “TOSS TB (Temukan Obati Sampai Sembuh).†ABDIMAS: Jurnal Pengabdian Masyarakat, 2(1), 103-107. https://doi.org/10.35568/abdimas.v2i1.338

Sulayman, F., Abdullah, F. A., & Mohd, M. H. (2021). An sveire model of tuberculosis to assess the effect of an imperfect vaccine and other exogenous factors. Mathematics, 9(4), 1–23. https://doi.org/10.3390/math9040327

Whang, S., Choi, S., & Jung, E. (2011). A dynamic model for tuberculosis transmission and optimal treatment strategies in South Korea. Journal of Theoretical Biology, 279(1), 120-131. https://doi.org/10.1016/j.jtbi.2011.03.009

World Health Organization. (2021). Global Tuberculosis Report 2021. In Global Tuberculosis Report.

Published

2023-01-12

Issue

Section

Articles