Mathematical epidemic model having two latent stages of exposed individuals

Authors

DOI:

https://doi.org/10.65112/tcmis.10045

Keywords:

Susceptible, basic reproduction number, infected, local stability, uniform permanence,

Abstract

In this paper, a five-dimensional SEIR system is investigated in which the exposed individuals are divided into two class of different stages. The first stage of exposed individuals proposed in the model represents individuals exhibiting early latent stage, however, second stage of infected individuals have long-term latent infection. In this study, after proposing the model, we analyse the basic reproduction number, identify the system's steady states, and investigate their local stability as well as global stability. These theoretical results are further verified by numerical simulations. Finally, we explore the biological significance of the findings, emphasizing their complex and potential implications.

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References

[1] W. O. Kermack and A. G. McKendrick, “A contribution to the mathematical theory of epidemics,” Proceedings of the Royal Society of London. Series A, vol. 115, no. 772, pp. 700–721, 1927. https://doi.org/10.1098/rspa.1927.0118. DOI: https://doi.org/10.1098/rspa.1927.0118

[2] W. O. Kermack and A. G. McKendrick, “Contributions to the mathematical theory of epidemics. II. The problem of endemicity,” Proceedings of the Royal Society of London. Series A, vol. 138, no. 834, pp. 55–83, 1932. https://doi.org/10.1098/rspa.1932.0171. DOI: https://doi.org/10.1098/rspa.1932.0171

[3] H. W. Hethcote, “The mathematics of infectious diseases,” SIAM Review, vol. 42, no. 4, pp. 599–653, 2000. https://doi.org/10.1137/S0036144500371907. DOI: https://doi.org/10.1137/S0036144500371907

[4] J. D. Murray, “Mathematical Biology II: Spatial Models and Biomedical Applications,” Springer, 2nd ed., vol. 18. New York: Springer, 2003. https://doi.org/10.1007/b98869. DOI: https://doi.org/10.1007/b98869

[5] H. W. Hethcote and D. W. Tudor, “Integral equation models for endemic infectious diseases,” Journal of Mathematical Biology, vol. 9, pp. 37–47, 1980. https://doi.org/10.1007/BF00276034. DOI: https://doi.org/10.1007/BF00276034

[6] World Health Organization, Global Tuberculosis Report 2024. Geneva, Switzerland: WHO Press, 2024.

[7] J. P. Aparicio, A. F. Capurro, and C. Castillo-Chavez, “Transmission and dynamics of tuberculosis on generalized households,” Journal of Theoretical Biology, vol. 206, pp. 327–341, 2000. https://doi.org/10.1006/jtbi.2000.2129. DOI: https://doi.org/10.1006/jtbi.2000.2129

[8] J. P. Aparicio, A. F. Capurro, and C. Castillo-Chavez, “Mathematical approaches for emerging and reemerging infectious diseases: Models, methods and theory,” Berlin, Germany: Springer-Verlag, 2001, pp. 351–360. https://doi.org/10.1007/978-1-4613-0065-6. DOI: https://doi.org/10.1007/978-1-4757-3667-0_20

[9] A. Paddar, A. E. Matouk, S. Qureshi, K. Dehingia and T. U. R. Shah, “Analyzing the impact of single feedback control strategy on the dynamics of fractional order tumor model,” Discover Applied Sciences, vol. 7, pp. 1301, 2025. https://doi.org/10.1007/s42452-025-07863-9. DOI: https://doi.org/10.1007/s42452-025-07863-9

[10] A. Naseem, K. Gdawiec, S. Qureshi, I. K. Argyros, M. A. U. Rehman, A. Soomro, E. Hinkal, K. Hosseini and A. Paddar, “A high-efficiency fourth-order iterative method for nonlinear equations: Convergence and computational gains,” Journal of Complexity, vol. 92, pp. 101994, 2026. https://doi.org/10.1016/j.jco.2025.101994. DOI: https://doi.org/10.1016/j.jco.2025.101994

[11] A. Paddar, S. Qureshi, A. E. Matouk, and K. Dehingia , “Dynamical analysis of a vector-borne disease model with control function strategies,” Discover Applied Sciences, vol. 7, pp. 1031, 2025. https://doi.org/10.1007/s42452-025-07644-4. DOI: https://doi.org/10.1007/s42452-025-07644-4

[12] J. Andrawus, J. Y. Musa, S. Babuba, A. Yusuf, S. Qureshi, U. T. Mustapha, A. Oghenefejiro and K. Dehingia , “Dynamical analysis of a vector-borne disease model with control function strategies,” Journal of the Nigerian Society of Physical Sciences, vol. 7, pp. 2732, 2025. https://doi.org/10.46481/jnsps.2025.2732. DOI: https://doi.org/10.46481/jnsps.2025.2732

[13] Z. A. Qureshi, S. Qureshi, A. A. Shaikh and M. Y. Shahani, “Optimizing tuberculosis dynamics through a comparative evaluation of mathematical models,” Communications in Mathematical Biology and Neuroscience, vol. 63, pp. 1-18, 2025. https://doi.org/10.289 19/cmbn/9248.

[14] K. Das, S. A. Murthy, and M. H. A. Biswas, “Mathematical transmission analysis of SEIR tuberculosis disease model,” Sensors International, vol. 2, Art. no. 100120, 2021. https://doi.org/10.1016/j.sintl.2021.100120. DOI: https://doi.org/10.1016/j.sintl.2021.100120

[15] Z. Ma and J. Li, “Dynamical Modelling and Analysis of Epidemics,” Singapore: World Scientific Publishing, 2009. https://doi.org/10.1142/9789812797506. DOI: https://doi.org/10.1142/6799

[16] K. B. Blyuss and Y. N. Kyrychko, “Effects of latency and age structure on the dynamics and containment of COVID-19,” Journal of Theoretical Biology, 2021. https://doi.org/10.1016/j.jtbi.2021.110587. DOI: https://doi.org/10.1101/2020.04.25.20079848

[17] M. Nagumo, “ ¨Uber die Lage der Integralkurven gew¨ohnlicher Differentialgleichungen,” Pro- ceedings of the Physico-Mathematical Society of Japan, vol. 24, 1942. https://doi.org/10.11429/ppmsj1919.24.0_551.

[18] P. van den Driessche and J. Watmough, “Reproduction numbers and sub-threshold endemic equilibria for compartmental models of disease transmission,” Mathematical Biosciences, vol. 180, pp. 29–48, 2002. https://doi.org/10.1016/S0025-5564(02)00108-6. DOI: https://doi.org/10.1016/S0025-5564(02)00108-6

[19] A. B. Gumel, “Causes of backward bifurcations in some epidemiological models,” Journal of Mathematical Analysis and Applications, vol. 395, pp. 355–365, 2012. https://doi.org/10.1016/j.jmaa.2012.04.077. DOI: https://doi.org/10.1016/j.jmaa.2012.04.077

[20] J. Dushoff, W. Huang, and C. Castillo-Chavez, “Backwards bifurcations and catastrophe in simple models of fatal diseases,” Journal of Mathematical Biology, vol. 36, pp. 227–248, 1998. https://doi.org/10.1007/s002850050099. DOI: https://doi.org/10.1007/s002850050099

[21] E. H. Elbasha and A. B. Gumel, “Theoretical assessment of public health impact of imperfect prophylactic HIV-1 vaccines with therapeutic benefits,” Bulletin of Mathematical Biology, vol. 68, pp. 577–614, 2006. https://doi.org/10.1007/s11538-005-9057-5. DOI: https://doi.org/10.1007/s11538-005-9057-5

[22] D. R. Merkin, “Introduction to the Theory of Stability,” Texts in Applied Mathematics, 1997. https://doi.org/10.1007/978-1-4612-4046-4. DOI: https://doi.org/10.1007/978-1-4612-4046-4_1

[23] J. La Salle and S. Lefschetz,, “Stability by Liapunov’s Direct Method with Applications,” Mathematics in Science and Engineering, 1961. https://doi.org/10.1016/S0076-5392(09)60355-6 DOI: https://doi.org/10.1016/S0076-5392(09)60355-6

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Published

2026-04-07

How to Cite

Kaushik, R. (2026). Mathematical epidemic model having two latent stages of exposed individuals. Transactions on Computational Modeling and Intelligent Systems, 3, 10045. https://doi.org/10.65112/tcmis.10045

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