Global stability and optimal control for a tuberculosis model with vaccination and treatment

Yali  Yang; Sanyi Tang; Xiaohong  Ren; Huiwen  Zhao; Chenping  Guo

doi:10.3934/dcdsb.2016.21.1009

Article Contents

2016, Volume 21, Issue 3: 1009-1022. Doi: 10.3934/dcdsb.2016.21.1009

This issue Previous Article Analysis of a free boundary problem for avascular tumor growth with a periodic supply of nutrients Next Article Errata: Evolutionary dynamics of a multi-trait semelparous model

Global stability and optimal control for a tuberculosis model with vaccination and treatment

1.
College of Mathematics and Information Science, Shaanxi Normal University, Xi'an 710062
2.
College of Mathematics and Information Science, Shaanxi Normal University, Xi'an, 710062
3.
Science College, Air Force Engineering University, Xi'an 710051

Received: January 31, 2015

Revised: June 30, 2015

Published: April 2016

Abstract Related Papers Cited by

Abstract

We formulate a mathematical model to explore the impact of vaccination and treatment on the transmission dynamics of tuberculosis (TB). We develop a technique to prove that the basic reproduction number is the threshold of global stability of the disease-free and endemic equilibria. We then incorporate a control term and evaluate the cost of control strategies, and then perform an optimal control analysis by Pontryagin's maximum principle. Our numerical simulations suggest that the maximum vaccination strategy should be enforced regardless of its efficacy.

Keywords:

Mathematics Subject Classification: Primary: 34D20, 34H05; Secondary: 49J15.

Citation:

Related Papers

Cited by

References

[1]	R. Baltussen, K. Floyd and C. Dye, Achieving the millennium development goals for health: Cost effectiveness analysis of strategies for tuberculosis control in developing countries, British Medical Journal, 331 (2005), 1364-1368.
[2]	M. Bannon and A. Finn, BCG and tuberculosis, Archives of Disease in Childhood, 80 (1999), 80-83.
[3]	C. Bhunu and W. Garira, Tuberculosis transmission model with chemoprophylaxis and treatment, Bulletin of Mathematical Biology, 70 (2008), 1163-1191.doi: 10.1007/s11538-008-9295-4.
[4]	S. M. Blower, A. R. McLean, T. C. Porco, P. M. Small, P. C. Hopewell, M. A. Sanchez and A. R. Moss, The intrinsic transmission dynamics of tuberculosis epidemic, Nature Medicine, 1 (1995), 815-821.doi: 10.1038/nm0895-815.
[5]	S. M. Blower, P. M. Small and P. C. Hopewell, Control stretagies for tuberculosis epidemic: New models for old problems, Science, 273 (1996), 497-500.
[6]	C. Castillo-Chavez and Z. Feng, To treat or not to treat: The case of tuberculosis, Journal of Mathemtical Biology, 35 (1997), 629-656.doi: 10.1007/s002850050069.
[7]	C. Castillo-Chavez and Z. Feng, Global stability of an age-structure model for TB and its applications to optimal vaccination strategies, Mathematical Biosciences, 151 (1998), 135-154.doi: 10.1016/S0025-5564(98)10016-0.
[8]	C. Castillo-Chavez and B. Song, Dynamical models of tuberculosis and their applications, Mathematical Biosciences and Engineering, 1 (2004), 361-404.doi: 10.3934/mbe.2004.1.361.
[9]	P. Clayden, S. Collins, C. Daniels, M. Frick, M. Harrington, T. Horn, R. Jefferys, K. Kaplan, E. Lessem, L. McKenna and T. Swan, 2014 Pipeline Report: HIV, Hepatitis C Virus (HCV) and Tuberculosis Drugs, Diagnostics, Vaccines, Preventive Technologies, Research Toward a Cure, and Immune-Based and Gene Therapies in Development, New York, 2014.
[10]	W. H. Fleming and R. W. Rishel, Deterministic and Stochastic Optimal Control, Springer Verlag, New York, 1975.
[11]	E. Jung, S. Lenhart and Z. Feng, Optimal control of treatments in a two-strain tuberculosis model, Discrete and Continous Dynamicals Systems Series B, 2 (2002), 473-482.doi: 10.3934/dcdsb.2002.2.473.
[12]	J. P. LaSalle, The Stability of Dynamical Systems, Regional Conference Series in Applied Mathematics, SIAM, Philadelphia, 1976.
[13]	K. X. Mao, C. X. Zhen, H. Y. Lu, Q. Dan, M. X. Ming, C. Y. Zhang, X. B. Hu and J. H. Dan, Protective effect of vaccination of Bacille Calmette-Gnerin on children, Chinese Journal Of Contemporary Pediatrics, 5 (2003), 325-326.
[14]	L. S. Pontryagin, V. G. Boltyanskii, R. V. Gamkrelidze and E. F. Mishchenko, The Mathematical Theory of Optimal Processes, Translated from the Russian by K. N. Trirogoff; edited by L. W. Neustadt Interscience Publishers John Wiley & Sons, Inc. New York-London 1962.
[15]	P. Rodriguesa, M. G. M. Gomes and C. Rebelo, Drug resistance in tuberculosis-a reinfection model, Theoretical Population Biology, 71 (2007), 196-212.doi: 10.1016/j.tpb.2006.10.004.
[16]	M. A. Sanchez and S. M. Blower, Uncertainty and sensitivity analysis of the basic reproductive rate: Tuberculosis as an example, American Journal of Epidemiology, 145 (1997), 1127-1137.doi: 10.1093/oxfordjournals.aje.a009076.
[17]	J. M. Tchuenche, S. A. Khamis, F. B. Agusto and S. C. Mpeshe, Optimal control and sensitivity analysis of an influenza model with treatment and vaccination, Acta Biotheoretica, 59 (2011), 1-28.doi: 10.1007/s10441-010-9095-8.
[18]	C. Ted and M. Megan, Modeling epidemics of multidrug-resisitant M.tuberculosis of heterogeneous fitness, Nature Medicine, 10 (2004), 1117-1121.
[19]	P. van den Driessche and J. Watmough, Reproduction numbers and sub-threshold endemic equilibria for compartmental models of disease transmission, Mathematical Biosciences, 180 (2002), 29-48.doi: 10.1016/S0025-5564(02)00108-6.
[20]	WHO, Global Tuberculosis Report 2014, World Health Organization Press, Switzerland, 2014.
[21]	Y. L. Yang, J. Q. Li, Z. E. Ma and Y. C. Zhou, Global stability of two models with incomplete treatment for tuberculosis, Chaos, Solitions and Fractals, 43 (2010), 79-85.doi: 10.1016/j.chaos.2010.09.002.