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Mathematical modeling and analysis of mycobacterium tuberculosis transmission in humans with hospitalization and reinfection

Treść / Zawartość
Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Tuberculosis (TB), a serious public health infection that mainly affects the lungs, is caused by bacteria (Mycobacterium tuberculosis, TB). This research is designed and analyzed using a compartmental modelling approach to study the transmission dynamics of TB with different stages of infection. Qualitative analysis of the proposed model reveals that the model exhibits two equilibrium points: the disease-free equilibrium point (DFE) and the endemic equilibrium (EE). The basic reproduction number (R0 ) is determined using the next generation matrix technique, and stability analysis is carried out to show whether the disease can persist or die out in population. Further analysis of the model shows that the EE is globally asymptotically stable (GAS) when R0 > 1. With the aid of the forward sensitivity index method, we determine the most sensitive parameters of the model to control the spread of TB infection effectively. Our analysis shows that treatment (medication) and campaign awareness coupled with other key control measures, could help maintain the spread of MTB infection in human geographical boundaries.
Rocznik
Strony
55--66
Opis fizyczny
Bibliogr. 21 poz., rys., tab.
Twórcy
  • Department of Mathematics, Federal University Dutse, 7156 Jigawa, Nigeria
autor
  • Department of Mathematics, Federal University Dutse, 7156 Jigawa, Nigeria
  • Department of Applied Mathematics, Hong Kong Polytechnic University, Hong Kong, China
  • Department of Mathematics, Kano University of Science and Technology, Wudil, Nigeria
  • Department of Mathematics, Federal University Dutse, 7156 Jigawa, Nigeria
  • Department of Computer Engineering, Biruni University, 34010 Istanbul, Turkey
Bibliografia
  • [1] Hassan, A.S., Garba, S.M., Gumel, A.B., & Lubuma, J.S. (2014). Dynamics of Mycobacterium and bovine tuberculosis in a human-buffalo population. Computational and Mathematical Methods in Medicine, 2014, article ID 912306.
  • [2] World Health Organization (WHO). Tuberculosis, Key Facts. 2021. (https://www.who.int/news-room/fact-sheets/detail/tuberculosis). Access 6 December 2021.
  • [3] AlMatar, M., AlMandeal, H., Var, I., Kayar, B., & Köksal, F. (2017). New drugs for the treatment of Mycobacterium tuberculosis infection. Biomedicine & Pharmacotherapy, 91, 546-558.
  • [4] Aparicio, J.P., Castillo-Chavez, C. (2009). Mathematical modelling of turberculosis epidemics. Math. Bios. and Eng., 6, 209-237.
  • [5] Ozcaglar, C., Shabbeer, A., Vandenberg, S.L., Yener, B., & Bennett, K.P. (2012). Epidemiological models of Mycobacterium tuberculosis complex infections. Mathematical Biosciences, 236(2), 77-96.
  • [6] Wahid, B.K.A., & Bisso, S. (2016). Mathematical analysis and simulation of an age-structured model of two-patch for tuberculosis (TB). Applied Mathematics, 7(15), 1882.
  • [7] Bowong, S., & Kurths, J. (2012). Modeling and analysis of the transmission dynamics of tuberculosis without and with seasonality. Nonlinear Dynamics, 67(3), 2027-2051.
  • [8] Cardona, P.J., Català, M., & Prats, C. (2020). Origin of tuberculosis in the Paleolithic predicts unprecedented population growth and female resistance. Scientific Reports, 10(1), 42.
  • [9] Huo, H.F., & Zou, M.X. (2016). Modelling effects of treatment at home on tuberculosis transmission dynamics. Applied Mathematical Modelling, 40(21-22), 9474-9484.
  • [10] Sadat, R., Agarwal, P., Saleh, R. et al. (2021). Lie symmetry analysis and invariant solutions of 3D Euler equations for axisymmetric, incompressible, and inviscid flow in the cylindrical coordinates. Advanced in Difference Equations, 486(2021). DOI: 10.1186/s13662-021-03637-w.
  • [11] Hethcote, H.W. (2000). The mathematics of infectious diseases. SIAM Review, 42(4), 599-653.
  • [12] Van den Driessche, P., & Watmough, J. (2002). Reproduction numbers and sub-threshold endemic equilibria for compartmental models of disease transmission. Mathematical Biosciences, 180(1-2), 29-48.
  • [13] Anderson, R.M. (1982). Transmission Dynamics and Control of Infectious Disease Agents. In Population Biology of Infectious Diseases (pp. 149-176). Berlin, Heidelberg: Springer.
  • [14] Anderson, R.M., & May, R.M. (1992). Infectious Diseases of Humans: Dynamics and Control. Oxford University Press.
  • [15] Hussaini, N., Lubuma, J.M., Barley, K., & Gumel, A.B. (2016). Mathematical analysis of a model for AVL-HIV co-endemicity. Mathematical Biosciences, 271, 80-95.
  • [16] La Salle, J.P. (1976). The Stability of Dynamical Systems. Society for Industrial and Applied Mathematics.
  • [17] Mustapha, U.T., Qureshi, S., Yusuf, A., & Hincal, E. (2020). Fractional modeling for the spread of Hookworm infection under Caputo operator. Chaos, Solitons & Fractals, 137, 109878.
  • [18] Usaini, S., Mustapha, U.T., & Sabiu, S.M. (2018). Modelling scholastic underachievement as a contagious disease. Mathematical Methods in the Applied Sciences, 41(18), 8603-8612.
  • [19] Wu, J., Dhingra, R., Gambhir, M., & Remais, J.V. (2013). Sensitivity analysis of infectious disease models: methods, advances and their application. Journal of The Royal Society Interface, 10(86), 20121018.
  • [20] Wen-Xiu Ma, Mohamed R. Ali, & Sadat, R. (2020). Analytical solutions for nonlinear dispersive physical model. Complexity, 2020, Article ID 3714832, 8 pages, DOI: 10.1155/2020/3714832.
  • [21] Cohen, T., Colijn, C., Finklea, B., & Murray, M. (2007). Exogenous re-infection and the dynamics of tuberculosis epidemics: local effects in a network model of transmission. Journal of the Royal Society Interface, 4(14), 523-531.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-e8fd5eb3-30c5-47e9-bc0f-91a51f60313d
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