References
[1] WHO. (2019). Global Tuberculosis Control. http://www.who.int/tb/publications/global_report/2019/pdf/fullreport. pdf.
[2] CDC. (2011). The Difference Between Latent TB Infection and Active TB Disease.
[3] CDC. (2016). Transmission and Pathogenesis of Tuberculosis.
[4] Marino, S., Kirschner, D. E. (2004). The human immune response to the Mycobacterium tuberculosis in lung and lymph node. Journal of Theor Biol., vol. 227, pp. 463-486.
[5] Mondragon, E. I., Esteva, L. Galan, L. C. (2011). A Mathematical Model for Cellular Immunology of Tuberculosis, Mathematical Biosciences and Engineering, vol. 8, no. 4, pp. 973-986.
[6] Mondragon, E. I., Esteva, L., Burbano-Rosero, E. M. (2018). Mathematical model for growth of Mycobacterium tuberculosis in granuloma. Math Biosci Eng., vol. 15, no. 2, pp. 407-428.
[7] Shi, R., Li, Y., Tang, S. (2014). A mathematical model with optimal controls for cellular immunology of tuberculosis. Taiwanese Journal of Mathematics, vol. 18, no. 2, pp. 575-597.
[8] Magombedze, G., Garira, W., Mwenje, E. (2006). Modelling the human immune response mechanisms to Mycobacterium tuberculosis infection in lungs. Math Biosci Eng., vol. 3, pp. 661-682.
[9] Zhang, W., Frascoli, F., Heffernan, J. M. (2020). Analysis of solutions and disease progressions for a within-host tuberculosis model. Mathematics in Applied Sciences and Engineering, vol. 1, no. 1, pp. 39-49.
[10] Adi, Y. A., Thobirin, A. (2020). Backward bifurcation in a within-host tuberculosis model. Advances in Mathematics Scientific Journal, vol. 9, no. 9, pp. 7269-7282.
[11] Khalil, H. K. (2002). Nonlinear System. Prentice-Hall, New Jersey.
[12] 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, vol. 70, no. 5, pp. 1272-1296.