Nonlinear Mathematical Modeling of Infectious Disease Progression in Biological Systems

Authors

  • Evgeny L. 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, Ul'yanov Street, Nizhny Novgorod, 603950, Russia.

https://doi.org/10.48313/bic.vi.60

Abstract

The dynamics of infectious disease development within a population of interacting organisms are investigated using a mathematical modeling framework. The epidemic process is described by a system of nonlinear ordinary differential equations that account for susceptible, asymptomatically infected, symptomatic, and unreported infected individuals. The model incorporates time-dependent parameters and delay effects to capture realistic features of disease transmission and progression. An analytical approach based on the method of averaging of functional corrections is proposed and applied to obtain approximate solutions of the governing system. First- and second-order approximations are derived, enabling a detailed analysis of the temporal evolution of the epidemic process. The accuracy of the analytical results is confirmed through comparison with numerical simulations, showing strong agreement between the two approaches. The proposed model allows for the investigation of how variations in epidemiological parameters influence the speed and pattern of disease spread. In particular, transmission, recovery, and progression rates significantly affect the amplitude and duration of the epidemic outbreak. The results also highlight the important role of asymptomatic and unreported infections in sustaining disease transmission within the population.

Keywords:

Infectious dynamics, Epidemiological model, Nonlinear ODEs, Analytical approximation, Averaging method, Epidemic modeling, Delay systems

References

  1. [1] Liu, Z., Magal, P., Seydi, O., & Webb, G. (2020). A COVID-19 epidemic model with latency period. Infectious disease modelling, 5, 323–337. https://doi.org/10.1016/j.idm.2020.03.003

  2. [2] Shilova, M. A., & Chistenko, G. N. (2017). Fundamentals of immunoprophylaxis of infectious diseases. Belarusian State Medical University (BGMU). (In Russian). https://dokumen.pub/qdownload/9789852106634.html

  3. [3] 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. https://doi.org/10.20953/1729-9225-2020-1-60-66

  4. [4] Petrov, A. A., Lebedev, V. N., Sizikova, T. E., Plekhanov, T. M., & Borisevich, S. V. (2019). Analysis of using of antisense oligonucleotides for the prevention and treatment of dangerous and especially dangerous viral infectious diseases. Antibiotics and chemotherapy, 64(7–8), 56-62. (In Russian). https://cyberleninka.ru/article/n/analiz-primeneniya-antismyslovyh-oligonukleotidov-dlya-profilaktiki-i-lecheniya-opasnyh-i-osobo-opasnyh-virusnyh-infektsionnyh

  5. [5] Miragliotta, G., & Miragliotta, L. (2014). Vitamin D and Infectious diseases. Endocrine, metabolic & immune disorders drug targets, 14(4), 267-271. https://doi.org/10.2174/1871530314666141027102627

  6. [6] Hong, S., & Park, K. (2025). Multi-physiology modeling of the immune system in the era of precision immunotherapy. Frontiers in immunology, 16, 1548768. https://doi.org/10.3389/fimmu.2025.1548768

  7. [7] Keizer, V. I. P., Grosse-Holz, S., Woringer, M., Zambon, L., Aizel, K., Bongaerts, M., … ., Coulon A. (2022). Live-cell micromanipulation of a genomic locus reveals interphase chromatin mechanics. Science, 377(6605), 489–495. https://doi.org/10.1126/science.abi9810

  8. [8] Kouvatsos, D. D., Mageed, I. A., Anisimov, V., & Limnios, N. (2021). Non-extensive maximum entropy formalisms and inductive inferences of stable m/g/1 queue with heavy tails. In Advanced trends in queueing theory (pp. 171–200). Wiley-ISTE. https://doi.org/10.1002/9781119755234.ch5

  9. [9] Kouvatsos, D. D., & Mageed, I. A. (2021). Formalismes de maximum d’entropie non extensive et inférence inductive d’une file d’attente M/G/1 stable à queues lourdes. In Théorie des files d’attente 2 théorie et pratique (pp. 183). ISTE Group. https://books.google.com/books?id=3mNDEQAAQBAJ

  10. [10] Kwon, K. K., Lee, J., Kim, H., Lee, D. H., & Lee, S. G. (2024). Advancing high-throughput screening systems for synthetic biology and biofoundry. Current opinion in systems biology, 37, 100487. https://doi.org/10.1016/j.coisb.2023.100487

  11. [11] Kouvatsos, D. D., & Mageed, I. A. (2021). Formalismes de maximum d’entropie non extensive et inférence inductive d’une file d’attente M/G/1 stable à queues lourdes. In Théorie des files d’attente 2 (pp. 183–213). ISTE/ Wiley. https://doi.org/10.51926/iste.9004.ch5

  12. [12] Mohamed, I. A. M., & Kouvatsos, D. D. (2011). Extended properties of the class of rényi generalized entropies in the discrete time domain. International conference on computer networks and information technology (pp. 1–7). IEEE. https://doi.org/10.1109/ICCNIT.2011.6020894

  13. [13] Tapeh, S. M. T., Baei, M. S., & Keshel, S. H. (2021). Synthesis of thermogel modified with biomaterials as carrier for hUSSCs differentiation into cardiac cells: Physicomechanical and biological assessment. Materials science and engineering: c, 119, 111517. https://doi.org/10.1016/j.msec.2020.111517

  14. [14] Baniasad, A., Sharifzadeh Baei, M., & Motallebi Tala-Tapeh, S. (2026). Chitosan-PEGylated niosomes and liposomes as biomacromolecule carriers for Alzheimer’s disease treatment: galantamine drug delivery carrier. Materials chemistry and physics, 352, 132003. https://doi.org/10.1016/j.matchemphys.2025.132003

  15. [15] Motallebi, S. (2025). Advances in mesoporous silica nanoparticles for targeted and controlled drug delivery. Biocompounds, 2(4), 212–225. https://doi.org/10.48313/bic.vi.58

Published

2026-03-08

How to Cite

Pankratov, E. L., & Platonova, L. . (2026). Nonlinear Mathematical Modeling of Infectious Disease Progression in Biological Systems. Biocompounds, 3(1), 21-30. https://doi.org/10.48313/bic.vi.60

Similar Articles

1-10 of 20

You may also start an advanced similarity search for this article.