This study develops a nonlinear time-delay mathematical model to investigate the transmission dynamics of tuberculosis in Algeria over the period 1990–2024. The model explicitly incorporates a delay representing the incubation period between infection and the onset of infectiousness, which significantly affects system behavior by potentially inducing instability and oscillatory dynamics. In particular, increasing the delay can destabilize the endemic equilibrium and lead to periodic outbreaks through Hopf bifurcation. The basic reproduction number $ \mathcal{R}_0 $ is derived, and rigorous analysis establishes the local and global stability conditions for both disease-free and endemic equilibria. Sensitivity analysis identifies key epidemiological parameters influencing $ \mathcal{R}_0 $, providing insight into effective intervention strategies. From a public health perspective, the results highlight that reducing diagnostic delays and improving timely treatment can stabilize disease dynamics and prevent recurrent outbreaks. Numerical simulations, calibrated with Algerian tuberculosis data from 1990 to 2024, support the theoretical findings and demonstrate the critical role of delay effects in shaping tuberculosis transmission and control.
Citation: Nadjla Abidat, Akhil Kumar Srivastav, Nico Stollenwerk, Abderrazak NABTi, Maíra Aguiar. A nonlinear time-delay modeling case study of Tuberculosis transmission in Algeria: 1990–2024[J]. Mathematical Biosciences and Engineering, 2026, 23(6): 1622-1650. doi: 10.3934/mbe.2026059
This study develops a nonlinear time-delay mathematical model to investigate the transmission dynamics of tuberculosis in Algeria over the period 1990–2024. The model explicitly incorporates a delay representing the incubation period between infection and the onset of infectiousness, which significantly affects system behavior by potentially inducing instability and oscillatory dynamics. In particular, increasing the delay can destabilize the endemic equilibrium and lead to periodic outbreaks through Hopf bifurcation. The basic reproduction number $ \mathcal{R}_0 $ is derived, and rigorous analysis establishes the local and global stability conditions for both disease-free and endemic equilibria. Sensitivity analysis identifies key epidemiological parameters influencing $ \mathcal{R}_0 $, providing insight into effective intervention strategies. From a public health perspective, the results highlight that reducing diagnostic delays and improving timely treatment can stabilize disease dynamics and prevent recurrent outbreaks. Numerical simulations, calibrated with Algerian tuberculosis data from 1990 to 2024, support the theoretical findings and demonstrate the critical role of delay effects in shaping tuberculosis transmission and control.
| [1] | World Health Organization, Global Tuberculosis Report 2024, WHO Press, 2024. |
| [2] |
K. Lönnroth, M. Raviglione, M. Weil, P. Uplekar, M. R. Dye, K. G. Williams, et al., Tuberculosis control and elimination 2010–50: cure, care, and social development, Lancet, 375 (2010), 1814–1829. https://doi.org/10.1016/S0140-6736(10)60483-7 doi: 10.1016/S0140-6736(10)60483-7
|
| [3] | F. Brauer, C. Castillo-Chavez, Mathematical Models in Population Biology and Epidemiology, Springer, Berlin, 2012. https://doi.org/10.1007/978-1-4614-1686-9 |
| [4] |
A. Nabti, S. Djilali, S. Bentout, Stability and spatial profiles of a double age-dependent diffusive viral infection model with spatial heterogeneity, Z. Angew. Math. Phys., 76 (2025), 89. https://doi.org/10.1007/s00033-025-02466-1 doi: 10.1007/s00033-025-02466-1
|
| [5] |
H. Y. Lam, S. Jayasinghe, K. D. Ahuja, A. P. Hills, Agent-based modelling in active commuting research: A scoping review, Crit. Public Health, 35 (2025), 2483473. https://doi.org/10.1080/09581596.2025.2483473 doi: 10.1080/09581596.2025.2483473
|
| [6] |
A. Nabti, M. Kirane, Global dynamics of an age-structured tuberculosis model with a general nonlinear incidence rate, J. Biol. Syst., 33 (2025), 1117–1159. https://doi.org/10.1142/S0218339025500330 doi: 10.1142/S0218339025500330
|
| [7] |
R. Chinnathambi, F. A. Rihan, H. J. Alsakaji, A fractional-order model with time delay for tuberculosis with endogenous reactivation and exogenous reinfections, Math. Methods Appl. Sci., 44 (2021), 8011–8025. https://doi.org/10.1002/mma.5676 doi: 10.1002/mma.5676
|
| [8] |
S. Bhatter, A. K. Verma, R. K. Upadhyay, M. A. Khan, S. Kumar, P. Singh, Mathematical modeling of tuberculosis using Caputo fractional derivative: a comparative analysis with real data, Sci. Rep., 15 (2025), 12672. https://doi.org/10.1038/s41598-025-12672-3 doi: 10.1038/s41598-025-12672-3
|
| [9] |
A. Ghanmi, A. Nabti, Stability study for an age-structured epidemic model with latent phase, relapse and nonlinear infection rate, Mathematics, 13 (2025), 3994. https://doi.org/10.3390/math13243994 doi: 10.3390/math13243994
|
| [10] | K. L. Cooke, P. van den Driessche, On zeroes of some transcendental equations, Funkc. Ekvacioj, 29 (1986), 77–90. |
| [11] |
A. K. Srivastav, M. Ghosh, Modeling the transmission dynamics of malaria with saturated treatment: A case study of India, J. Appl. Math. Comput., 67 (2021), 519–540. https://doi.org/10.1007/s12190-020-01416-9 doi: 10.1007/s12190-020-01416-9
|
| [12] | Y. Kuang, Delay Differential Equations with Applications in Population Dynamics, Academic Press, New York, 1993. https://doi.org/10.1016/C2009-0-22254-6 |
| [13] | N. Abidat, A. Nabti, K. Bouaziz, Hopf bifurcation analysis of a TB infection model with two discrete time delays, Palest. J. Math., 14 (2025). https://pjm.ppu.edu/vol/2220 |
| [14] |
W. M. Liu, H. W. Hethcote, S. A. Levin, Dynamical behavior of epidemiological models with nonlinear incidence rates, J. Math. Biol., 25 (1987), 359–380. https://doi.org/10.1007/BF00277162 doi: 10.1007/BF00277162
|
| [15] |
A. Nabti, Dynamical analysis of an age-structured SEIR model with relapse, Z. Angew. Math. Phys., 75 (2024), 84. https://doi.org/10.1007/s00033-024-02227-6 doi: 10.1007/s00033-024-02227-6
|
| [16] |
E. Beretta, Y. Kuang, Geometric stability switch criteria in delay differential systems with delay-dependent parameters, SIAM J. Math. Anal., 33 (2002), 1144–1165. https://doi.org/10.1137/S0036141000376086 doi: 10.1137/S0036141000376086
|
| [17] | Algerian Ministry of Health, National Report on Tuberculosis Control in Algeria., Ministry of Health Publications, 2023. |
| [18] | Ministry of Health Algeria, National Tuberculosis Report 2023, Ministère de la Santé, (2023). |
| [19] | World Health Organization, Global Tuberculosis Report 2024, WHO Press, 2024. |
| [20] | World Health Organization, Treatment of Tuberculosis: Guidelines for National Programmes., WHO Press, 1997. |
| [21] |
S. Saha, P. Dutta, G. Samanta, Dynamical behavior of SIRS model incorporating government action and public response in presence of deterministic and fluctuating environments, Chaos Solitons Fractals, 164 (2022), 112643. https://doi.org/10.1016/j.chaos.2022.112643 doi: 10.1016/j.chaos.2022.112643
|
| [22] |
P. Dutta, G. Samanta, J. J. Nieto, Periodic transmission and vaccination effects in epidemic dynamics: a study using the SIVIS model, Nonlinear Dyn., 112 (2024), 2381–2409. https://doi.org/10.1007/s11071-023-08967-5 doi: 10.1007/s11071-023-08967-5
|
| [23] |
S. Dutta, P. Dutta, P. Akhtar, G. Samanta, Bifurcation analysis and chaotic dynamics in an SIR model with nonlinear incidence and constrained healthcare capacity, Chaos Solitons Fractals, 201 (2025), 117329. https://doi.org/10.1016/j.chaos.2025.117329 doi: 10.1016/j.chaos.2025.117329
|
| [24] | World Health Organization, Global Tuberculosis Report 2023, WHO Press, Geneva, 2023. |
| [25] |
Z. Feng, C. Castillo-Chavez, A. F. Capurro, A model for tuberculosis with exogenous reinfection, Theor. Popul. Biol., 57 (2000), 235–247. https://doi.org/10.1006/tpbi.2000.1451 doi: 10.1006/tpbi.2000.1451
|
| [26] |
C. Castillo-Chavez, Z. Feng, To treat or not to treat: the case of tuberculosis, J. Math. Biol., 35 (1997), 629–656. https://doi.org/10.1007/s002850050069 doi: 10.1007/s002850050069
|
| [27] |
P. van den Driessche, J. Watmough, Reproduction numbers and sub-threshold endemic equilibria for compartmental models of disease transmission, Math. Biosci., 180 (2002), 29–48. https://doi.org/10.1016/S0025-5564(02)00108-6 doi: 10.1016/S0025-5564(02)00108-6
|
| [28] |
N. Chitnis, J. Hyman, J. M. Cushing, Determining important parameters in the spread of malaria through the sensitivity analysis of a mathematical model, Bull. Math. Biol., 70 (2008), 1272–1296. https://doi.org/10.1007/s11538-008-9299-0 doi: 10.1007/s11538-008-9299-0
|
| [29] |
E. O. Alzahrani, W. Ahmad, M. A. Khan, S. J. Malebary, Optimal control strategies of Zika virus model with mutant, Commun. Nonlinear Sci. Numer. Simul., 93 (2021), 105532. https://doi.org/10.1016/j.cnsns.2020.105532 doi: 10.1016/j.cnsns.2020.105532
|
| [30] | W. Rudin, Principles of Mathematical Analysis, McGraw-Hill, New York, 1976. |
| [31] | T. Tao, Analysis Ⅱ, Springer, 2016. https://doi.org/10.1007/978-981-10-1804-6 |
| [32] | WHO. Global Tuberculosis Report; World Health Organization: Geneva, Switzerland, 2023. Available from: https://extranet.who.int/tme/generateCSV.asp?ds = notifications |
| [33] | World Bank, Population, total – Algeria, Available from: https://data.worldbank.org/indicator/SP.POP.TOTL?locations = DZ |
| [34] |
M. Aguiar, N. Stollenwerk, Condition-specific mortality risk can explain differences in COVID-19 case fatality ratios around the globe, Public Health, 188 (2020), 18–20. https://doi.org/10.1016/j.puhe.2020.08.004 doi: 10.1016/j.puhe.2020.08.004
|