On analysis of development of an infectious disease in the development of an organism

Authors

  • Evgeny Leonidovich Pankratov * Nizhny Novgorod State Agrotechnical University, 97 Gagarin avenue, Nizhny Novgorod, 603950, Russia. https://orcid.org/0000-0001-6174-2612
  • L. E. Platonova Nizhniy Novgorod State Pedagogical University named Kozma Minin, 1 Ul'yanov street, Nizhny Novgorod, 603950, Russia.

https://doi.org/10.22105/thi.v2i3.37

Abstract

In this paper, we investigate the development of an infectious disease in a population of organisms using a mathematical model described by a system of Ordinary Differential Equations (ODEs) with time-varying parameters. The incorporation of time-dependent parameters enables the model to capture variations in epidemic conditions and disease transmission during different stages of an outbreak, providing a more realistic representation of epidemic dynamics than models with constant coefficients. An analytical solution of the proposed model is obtained using the Method of Functional Corrections (MFC), which provides an efficient framework for analyzing nonlinear epidemic systems without relying on numerical approximations. The analytical expressions derived from the proposed approach provide insight into the influence of model parameters on disease progression and facilitate the qualitative assessment of epidemic behavior under varying conditions. The obtained results demonstrate that the proposed analytical framework can effectively describe the temporal evolution of infectious diseases while preserving the mathematical structure of the original model. Owing to its flexibility and analytical nature, the proposed approach can serve as a useful tool for investigating infectious disease dynamics and may provide theoretical support for future studies in epidemiological modeling and health informatics.

Keywords:

Development of an infectious disease, Model of process, Analytical approach for analysis

References

  1. [1] World Health Organization. (2024). World health statistics 2024: Monitoring health for the Sustainable Development Goals (SDGs). https://www.who.int/publications/i/item/9789240094703/?utm_source=chatgpt.com

  2. [2] Liu, J., Bellows, B., Hu, X. J., Wu, J., Zhou, Z., Soteros, C., & Wang, L. (2023). A new time-varying coefficient regression approach for analyzing infectious disease data. Scientific reports, 13(1), 14687. https://doi.org/10.1038/s41598-023-41551-1

  3. [3] Buch, D. A., Johndrow, J. E., & Dunson, D. B. (2023). Explaining transmission rate variations and forecasting epidemic spread in multiple regions with a semiparametric mixed effects SIR model. Biometrics, 79(4), 2987–2997. https://doi.org/10.1111/biom.13901

  4. [4] Kermack, W. O., & McKendrick, A. G. (1927). A contribution to the mathematical theory of epidemics. Proceedings of the royal society of london, (115A), 700–721. https://doi.org/10.1098/rspa.1927.0118

  5. [5] Brauer, F., Castillo-Chavez, C., & Feng, Z. (2019). Mathematical models in epidemiology. Springer. https://doi.org/10.1007/978-1-4939-9828-9

  6. [6] Hethcote, H. W. (2000). The mathematics of infectious diseases. SIAM review, 42(4), 599–653. https://doi.org/10.1137/S0036144500371907

  7. [7] Yotongyos, K., & Sriyab, S. (2023). Modeling the spread of COVID-19 using nonautonomous dynamical system with simplex algorithm-based optimization for time-varying parameters. Journal of mathematics, 2023(1), 6156749. https://doi.org/10.1155/2023/6156749

  8. [8] Zelenkov, Y., & Reshettsov, I. (2023). Analysis of the COVID-19 pandemic using a compartmental model with time-varying parameters fitted by a genetic algorithm. Expert systems with applications, 224, 120034. https://doi.org/10.1016/j.eswa.2023.120034

  9. [9] Pakkanen, M. S., Miscouridou, X., Penn, M. J., Whittaker, C., Berah, T., Mishra, S., Mellan, T. A., & Bhatt, S. (2023). Unifying incidence and prevalence under a time-varying general branching process. Journal of mathematical biology, 87(2), 35. https://doi.org/10.1007/s00285-023-01958-w

  10. [10] Storvik, G., Diz-Lois Palomares, A., Engebretsen, S., Rø, G. Ø. I., Engø-Monsen, K., Kristoffersen, A. B., … & Frigessi, A. (2023). A sequential Monte Carlo approach to estimate a time-varying reproduction number in infectious disease models: The Covid-19 case. Journal of the royal statistical society series a: statistics in society, 186(4), 616–632. https://doi.org/10.1093/jrsssa/qnad043

  11. [11] Lambert, J. D. (1991). Numerical methods for ordinary differential systems (Vol. 146). Wiley New York. https://openlibrary.org/books/OL1869799M/Numerical_methods_for_ordinary_differential_systems?utm_source=chatgpt.com

  12. [12] Hairer, E., Nørsett, S. P., & Wanner, G. (1993). Solving ordinary differential equations I: Nonstiff problems. Springer Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-78862-1

  13. [13] Iserles, A. (2009). A first course in the numerical analysis of differential equations. Cambridge University Press. https://doi.org/10.1017/CBO9780511995569

  14. [14] He, J. H. (1999). Homotopy perturbation technique. Computer methods in applied mechanics and engineering, 178(3–4), 257–262. https://doi.org/10.1016/S0045-7825(99)00018-3

  15. [15] Adomian, G. (1994). Solving frontier problems of physics: The decomposition method. Springer Dordrecht. https://doi.org/10.1007/978-94-015-8289-6

  16. [16] He, J. H. (1999). Variational iteration method--a kind of non-linear analytical technique: Some examples. International journal of non-linear mechanics, 34(4), 699–708. https://doi.org/10.1016/S0020-7462(98)00048-1

  17. [17] Zhou, J. K. (1986). Differential transformation and its applications for electrical circuits. Huazhong University Press. https://www.sciepub.com/reference/12558?utm_source=chatgpt.com

  18. [18] Marinca, V., & Herisanu, N. (2015). Optimal homotopy asymptotic method. In The optimal homotopy asymptotic method: engineering applications (pp. 9–22). Springer. https://doi.org/10.1007/978-3-319-15374-2_2

  19. [19] Xia, Y., Chen, F., Chen, A., & Cao, J. (2004). Existence and global attractivity of an almost periodic ecological model. Applied mathematics and computation, 157(2), 449–475. https://doi.org/10.1016/j.amc.2003.08.045

  20. [20] Khan, Y., & Wu, Q. (2011). Homotopy perturbation transform method for nonlinear equations using He’s polynomials. Computers & mathematics with applications, 61(8), 1963–1967. https://doi.org/10.1016/j.camwa.2010.08.022

  21. [21] Wazwaz, A. M. (2009). Partial differential equations and solitary waves theory. Nonlinear physical science. Springer Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-00251-9

  22. [22] Sokolov, Y. D. (1955). About the definition of dynamic forces in the mine lifting. Applied mechanics, 1(1), 23–35.

  23. [23] Pankratov, E. L., & Bulaeva, E. A. (2015). On optimization of regimes of epitaxy from gas phase. some analytical approaches to model physical processes in reactors for epitaxy from gas phase during growth films. Reviews in theoretical science, 3(4), 365–398. https://elibrary.ru/item.asp?id=30749178

  24. [24] Goloverova, Y., Marin, G., Golubcova, A., Shabalina, S., & Romanova, K. (2020). The relevance of the risk of prevalence of infections associated with the provision of medical care among medical professionals at the present stage. Infectious diseases, 18(1), 60–66.

Published

2025-09-15

How to Cite

Pankratov, E. L. ., & Platonova , L. E. (2025). On analysis of development of an infectious disease in the development of an organism. Trends in Health Informatics, 2(3), 150-157. https://doi.org/10.22105/thi.v2i3.37