Dynamical System of the Mathematical Model for Tuberculosis with Vaccination

Authors

  • Dian Grace Ludji IPB University
  • Paian Sianturi IPB University
  • Endar Nugrahani IPB University

DOI:

https://doi.org/10.21512/comtech.v10i2.5686

Keywords:

dynamical system, mathematical model, tuberculosis, vaccination

Abstract

This research focused on the modification of deterministic mathematical models for tuberculosis with vaccination. It also aimed to see the effect of giving the vaccine. It was done by adding vaccine compartments to people who were given the vaccine in the susceptible compartment. The population was divided into nine different groups. Those were susceptible individuals (S), vaccine (V), new latently infected (E1), diagnosed latently infected (E2), undiagnosed latently infected (E3), undiagnosed actively infected (l), diagnosed actively infected with prompt treatment (Dr), diagnosed actively infected with delay treatment (Dp), and treated (T). Basic reproduction number was constructed using next-generation matrix. Sensitivity analysis was also conducted. The results show that the model comprises two equilibriums: diseasefree equilibrium (T0) and endemic equilibrium (T*). It also shows that there is a relationship between R0 and two equilibriums. Moreover, the disease-free equilibrium point is asymptotically stable local when it is R0 < 1. Then, the disease-endemic equilibrium point is asymptotically stable local when it is R0 > 1. Furthermore, the parameters of β, ρ, and γ are the most important parameter.

Dimensions

Plum Analytics

Author Biography

Dian Grace Ludji, IPB University

Applied of Mathematics

References

Al-Darraji, H. A. A., Altice, F. L., & Kamarulzaman, A. (2016). Undiagnosed pulmonary tuberculosis among prisoners in Malaysia: An overlooked risk for tuberculosis in the community. Tropical Medicine and International Health, 21(8), 1049-1058. https://doi.org/10.1111/tmi.12726

Anderson, R. M., & May, R. M. (1991). Infectious diseases of humans: Dynamics and control. New York: Oxford University Press.

Aparicio, J., & Casstillo-Chavez, C. (2009). Mathematical modelling of tuberculosis epidemics. Mathematical Biosciences and Engineering, 6(2), 209-237. https://doi.org/10.3934/mbe.2009.6.209

Apriliani, V. (2016). Sistem dinamik model penyebaran penyakit tuberkulosis dengan dua kelompok populasi terinfeksi (Master’s thesis). Institut Pertanian Bogor.

Castillo-Chavez, C., & Song, B. (2004). Dynamical models of tuberculosis and their applications. Mathematical Biosciences and Engineering, 1(2), 361-404. https://doi.org/10.3934/mbe.2004.1.361

Chitnis, N., Hyman, J. M., & Cushing, J. M. (2008). Determining important parameters in the spread of malaria through the sensitivity analysis of a mathematical model. Bulletin of Mathematical Biology, 70(5), 1272-1296. https://doi.org/10.1007/s11538-008-9299-0

Dushoff, J., Huang, W., & Castillo-Chavez, C. (1998). Backwards bifurcations and catastrophe in simple models of fatal diseases. Journal of Mathematical Biology, 36(3), 227-248. https://doi.org/10.1007/s002850050099

Edelstein-Keshet, L. (2005). Mathematical models in biology. Philadelphia: Society for Industrial and Applied Mathematics.

Egonmwan, A. O., & Okuonghae, D. (2019). Analysis of a mathematical model for tuberculosis with diagnosis. Journal of Applied Mathematics and Computing, 59(1–2), 129-162. https://doi.org/10.1007/s12190-018-1172-1

Esmail, H., Barry, C. E., Young, D. B., & Wilkinson, R. J. (2014). The ongoing challenge of latent tuberculosis. Philosophical Transactions of the Royal Society B: Biological Sciences, 369, 1-14. https://doi.org/10.1098/rstb.2013.0437

Kalu, A. U., & Inyama, S. C. (2012). Mathematical model of the role of vaccination and treatment on the transmission dynamics of Tuberculosis. Gen. Math. Notes, 11(1), 10-23.

Kermack, W.O., & McKendrick, A.G. (1927). A contribution to the mathematical theory of epidemics. Proceedings of The Royal Society A: Mathematical, Physical and Engineering Sciences, 115(772),700-721. https://doi.org/10.1098/rspa.1927.0118

Lawn, S. D., & Zumla, A. I. (2011). Tuberculosis. The Lancet., 378 (9785), 57-72. https://doi.org/10.1016/S0140-6736(10)62173-3

Nainggolan, J., Supian, S., Supriatna, A. K., & Anggriani, N. (2013). Mathematical model of tuberculosis transmission with reccurent infection and vaccination. Journal of Physics: Conference Series, 423(1), 1-8. https://doi.org/10.1088/1742-6596/423/1/012059

Okuonghae, D. (2013). A mathematical model of tuberculosis transmission with heterogeneity in disease susceptibility and progression under a treatment regime for infectious cases. Applied Mathematical Modelling, 37(10–11), 6786-6808. https://doi.org/10.1016/j.apm.2013.01.039

Okuonghae, D., & Ikhimwin, B. O. (2016). Dynamics of a mathematical model for tuberculosis with variability in susceptibility and disease progressions due to difference in awareness level. Frontiers in Microbiology, 6, 1-23. https://doi.org/10.3389/fmicb.2015.01530

Okuonghae, D., & Omosigho, S. E. (2011). Analysis of a mathematical model for tuberculosis: What could be done to increase case detection. Journal of Theoretical Biology, 269(1), 31-45. https://doi.org/10.1016/j.jtbi.2010.09.044

Robinson, J. C. (2004). An introduction to ordinary differential equations (Cambridge texts in applied mathematics). Cambridge University Press.

Roni, T. P. (2011). Kestabilan lokal bebas penyakit model epidemi SEIR dengan kumpulan infeksi pada periode laten. Padang: Politeknik Negeri Padang.

Side, S., Sanusi, W., & Setiawan, N. F. (2016). Analisis dan simulasi model SITR pada penyebaran penyakit Tuberkulosis di Kota Makassar. Jurnal Sainsmat, 5(2), 191-204.

Trauer, J. M., Denholm, J. T., & McBryde, E. S. (2014). Construction of a mathematical model for tuberculosis transmission in highly endemic regions of the Asia-Pacific. Journal of Theoretical Biology, 358(October), 74-84. https://doi.org/10.1016/j.jtbi.2014.05.023

World Health Organization (WHO). (2018). Global tuberculosis report 2018. Retrieved from https://www.who.int/tb/publications/global_report/en/

Downloads

Published

2019-12-31

Issue

Section

Articles
Abstract 1558  .
PDF downloaded 366  .