Mathematical Analysis of Tuberculosis Transmission Model with Multidrug and Extensively Drug-resistant Incorporating Chemoprophylaxis Treatment

Damtew Bewket Kitaro, Boka Kumsa Bole, Koya Purnachandra Rao

Abstract


Tuberculosis has remained the principal cause of mortality worldwide, and one of the major sources of concern is drug-resistant TB. The increasing emergence of extensively drug-resistant and multidrug-resistant TB has further increased the TB epidemic. In this current work, we suggest a model to study the transmission of TB with extensively drug-resistant and multidrug-resistant compartments, incorporating chemoprophylaxis treatment. In the theoretical analysis, the concept of the next-generation matrix and the Jacobian method are applied to obtain a formula that states the reproductive number. The existence of endemic and disease-free equilibrium points was checked, and their stability has been analyzed using the Lyapunov method. The qualitative-based analysis indicated the local asymptotic stability of the disease-free-state for R0 < 1, whereas the endemic state is globally asymptotically stable if R0 > 1. Moreover, sensitivity analysis was carefully done using normalized forward sensitivity, and numerical simulation was carried out. Based on the results of numerical simulation and sensitivity analysis, chemoprophylaxis treatment was found to drastically minimize the progression of exposed individuals to infectious classes and also reduce the progression to extensively drug-resistant and multidrug-resistant classes, which decreases disease transmission.

Keywords


Mathematical Modeling; Extensively Drug-resistant; Numerical Simulation; Sensitivity Analysis; Equilibrium Point

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References


D. Young, J. Stark, and D. Kirschner, “Systems biology of persistent infection: tuberculosis as a case study,” Nature Reviews Microbiology, vol. 6, no. 7, pp. 520–528, 2008, doi: 10.1038/nrmicro1919.

V. K. Gupta, S. K. TIWAR ˙ I, S. Sharma, and L. Nagar, “Mathematical model of tuberculosis with drug resistance to the first and second line of treatment,” Journal of New Theory, no. 21, pp. 94–106, 2018.

D. Pal, D. Ghosh, P. Santra, and G. Mahapatra, “Mathematical analysis of a covid-19 epidemic model by using data driven epidemiological parameters of diseases spread in india,” Biophysics, vol. 67, no. 2, pp. 231–244, 2022.

P. Santra, D. Ghosh, G. Mahapatra, E. Bonyah et al., “Mathematical analysis of two waves of covid-19 disease with impact of vaccination as optimal control,” Computational and Mathematical Methods in Medicine, vol. 2022, 2022.

H. Waaler, A. Geser, and S. Andersen, “The use of mathematical models in the study of the epidemiology of tuberculosis,” American Journal of Public Health and the Nations Health, vol. 52, no. 6, pp. 1002–1013, 1962.

A. Egonmwan and D. Okuonghae, “Analysis of a mathematical model for tuberculosis with diagnosis,” Journal of applied mathematics and computing, vol. 59, pp. 129–162, 2019, doi: 10.1007/s12190-018-1172-1.

A. Kelemu Mengistu and P. J. Witbooi, “Modeling the effects of vaccination and treatment on tuberculosis transmission dynamics,” Journal of Applied Mathematics, vol. 2019, pp. 1–9, 2019, doi: 10.1155/2019/7463167.

C. Bhunu, W. Garira, Z. Mukandavire, and M. Zimba, “Tuberculosis transmission model with chemoprophylaxis and treatment,” Bulletin of Mathematical Biology, vol. 70, pp. 1163–1191, 2008, doi: 10.1007/s11538-008-9295-4.

S. Side, A. Utami, M. Pratama et al., “Numerical solution of sir model for transmission of tuberculosis by runge-kutta method,” in Journal of Physics: Conference Series, vol. 1040, no. 1. IOP Publishing, 2018, p. 012021, doi: 10.1088/1742-6596/1040/1/012021.

S. Athithan and M. Ghosh, “Mathematical modelling of tb with the effects of case detection and treatment,” International Journal of Dynamics and Control, vol. 1, pp. 223–230, 2013, doi: 10.1007/s40435-013-0020-2.

D. B. Kitaro, B. K. Bole, and K. P. Rao, “Modeling and bifurcation analysis of tuberculosis with multidrugresistant compartment incorporating chemoprophylaxis treatment,” Frontiers in Applied Mathematics and Statistics, vol. 9, p. 1264201, doi: 10.3389/fams.2023.1264201.

W. S. T. Strategy, “Implementing the who stop tb strategy,” URL: WHO/HTm/TB/2008.401.

F. A. Oguntolu, O. J. Peter, K. Oshinubi, T. A. Ayoola, A. O. Oladapo, and M. M. Ojo, “Analysis and dynamics of tuberculosis outbreak: A mathematical modelling approach,” Advances in Systems Science and Applications, vol. 4, pp. 144–161, 2022.

K. Oshinubi, O. J. Peter, E. Addai, E. Mwizerwa, O. Babasola, I. V. Nwabufo, I. Sane, U. M. Adam, A. Adeniji, and J. O. Agbaje, “Mathematical modelling of tuberculosis outbreak in an east African country incorporating vaccination and treatment,” Computation, vol. 11, no. 7, p. 143, 2023, doi: 10.3390/computation11070143.

I. Syahrini, Sriwahyuni, V. Halfiani, S. M. Yuni, T. Iskandar, Rasudin, and M. Ramli, “The epidemic of tuberculosis on vaccinated population,” in Journal of Physics: Conference Series, vol. 890, no. 1. IOP Publishing, 2017, p. 012017, doi: 10.1088/1742-6596/890/1/012017.

S. Egbetade, M. Ibrahim, and C. Ejieji, “On existence of a vaccination model of tuberculosis disease pandemic,” International Journal of Engineering and Science, vol. 2, no. 7, pp. 41–44, 2013.

K. Q. Fredlina, T. Oka, and I. Dwipayana, “Sir (susceptible, infectious, recovered) model for tuberculosis disease transmission, j,” Matematika, vol. 1, no. 1, pp. 52–58, 2012.

N. H. Shah and J. Gupta, “Mathematical modelling of pulmonary and extra-pulmonary tuberculosis,” Inter. J. Math. Trends Technol. 4 (9): 158, vol. 162, 2013.

B. K. Mishra and J. Srivastava, “Mathematical model on pulmonary and multidrug-resistant tuberculosis patients with vaccination,” Journal of the Egyptian Mathematical Society, vol. 22, no. 2, pp. 311–316, 2014, doi: 10.1016/j.joems.2013.07.006.

U. T. Mustapha, B. Idris, S. S. Musa, and A. Yusuf, “Mathematical modeling and analysis of mycobacterium tuberculosis transmission in humans with hospitalization and reinfection,” Office of Czestochowa University of Technology, vol. 21, no. 1, pp. 55–66, 2022.

S. Suddin, E. N. Bano, and M. H. Yanni, “Mathematical modelling of multidrug-resistant tuberculosis with vaccination,” MATEMATIKA: Malaysian Journal of Industrial and Applied Mathematics, pp. 109–120, 2021.

Y. Yu, Y. Shi, and W. Yao, “Dynamic model of tuberculosis considering multi-drug resistance and their applications,” Infectious disease modelling, vol. 3, pp. 362–372, 2018, doi: 10.1016/j.idm.2018.11.001.

O. Diekmann and J. A. P. Heesterbeek, Mathematical epidemiology of infectious diseases: model building, analysis and interpretation. John Wiley & Sons, 2000, vol. 5.

M. Ronoh, R. Jaroudi, P. Fotso, V. Kamdoum, N. Matendechere, J. Wairimu, R. Auma, and J. Lugoye, “A mathematical model of tuberculosis with drug resistance effects,” Applied Mathematics, vol. 7, no. 12, pp. 1303–1316, 2016, doi: 10.4236/am.2016.712115.




DOI: https://doi.org/10.37905/jjom.v6i1.22127



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