Research article

Delayed immune responses and heterogeneous exposure shape within-host viral dynamics

  • Published: 22 May 2026
  • In this paper, we present a delayed within-host viral dynamics model that integrates nonlinear immune responses and heterogeneous exposure. The model combines logistic viral replication, delayed immune-mediated clearance, and time-dependent external forcing. We introduce a $ 4 \times 4 $ factorial framework that systematically couples four canonical immune response architectures (linear, Michaelis–Menten, Hill-type, and switch-like) with four representative exposure profiles (constant, periodic, event-driven, and adaptive), yielding sixteen distinct dynamical scenarios. A rigorous theoretical analysis established positivity, boundedness, equilibrium structure, and delay-induced Hopf bifurcations. Extensive numerical simulations quantified transient amplification, timing, clearance efficiency, oscillatory behavior, and long-term persistence using a comprehensive set of nine quantitative metrics. The results reveal robust dynamical hierarchies governed by immune sensitivity and exposure intermittence rather than maximal clearance capacity. In particular, ultrasensitive immune responses amplified early overshoots and cumulative burden, whereas impulsive or adaptive exposure substantially reduced persistence and accelerated post-peak decay. These findings revealed fundamental trade-offs between early transient control and long-term viral persistence in delayed immune feedback systems.

    Citation: Abdelkarim Lamghari, Aissam Jebrane. Delayed immune responses and heterogeneous exposure shape within-host viral dynamics[J]. Mathematical Biosciences and Engineering, 2026, 23(6): 1799-1843. doi: 10.3934/mbe.2026066

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  • In this paper, we present a delayed within-host viral dynamics model that integrates nonlinear immune responses and heterogeneous exposure. The model combines logistic viral replication, delayed immune-mediated clearance, and time-dependent external forcing. We introduce a $ 4 \times 4 $ factorial framework that systematically couples four canonical immune response architectures (linear, Michaelis–Menten, Hill-type, and switch-like) with four representative exposure profiles (constant, periodic, event-driven, and adaptive), yielding sixteen distinct dynamical scenarios. A rigorous theoretical analysis established positivity, boundedness, equilibrium structure, and delay-induced Hopf bifurcations. Extensive numerical simulations quantified transient amplification, timing, clearance efficiency, oscillatory behavior, and long-term persistence using a comprehensive set of nine quantitative metrics. The results reveal robust dynamical hierarchies governed by immune sensitivity and exposure intermittence rather than maximal clearance capacity. In particular, ultrasensitive immune responses amplified early overshoots and cumulative burden, whereas impulsive or adaptive exposure substantially reduced persistence and accelerated post-peak decay. These findings revealed fundamental trade-offs between early transient control and long-term viral persistence in delayed immune feedback systems.



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    [1] M. A. Nowak, R. M. May, Virus Dynamics: Mathematical Principles of Immunology and Virology, Oxford University Press, Oxford, 2000. https://doi.org/10.1093/oso/9780198504184.001.0001
    [2] A. S. Perelson, Modelling viral and immune system dynamics, Nat. Rev. Immunol., 2 (2002), 28–36. https://doi.org/10.1038/nri700 doi: 10.1038/nri700
    [3] A. S. Perelson, A. U. Neumann, M. Markowitz, J. M. Leonard, D. D. Ho, HIV-1 dynamics in vivo: Virion clearance rate, infected cell life-span and viral generation time, Science, 271 (1996), 1582–1586. https://doi.org/10.1126/science.271.5255.1582 doi: 10.1126/science.271.5255.1582
    [4] A. S. Perelson, R. M. Ribeiro, Modeling the within-host dynamics of HIV infection, BMC Biol., 11 (2013), 96. https://doi.org/10.1186/1741-7007-11-96 doi: 10.1186/1741-7007-11-96
    [5] K. A. Pawelek, G. T. Huynh, M. Quinlivan, A. Cullinane, L. Rong, A. S. Perelson, Modeling within-host dynamics of influenza virus infection including immune responses, PLoS Comput. Biol., 8 (2012), e1002588. https://doi.org/10.1371/journal.pcbi.1002588 doi: 10.1371/journal.pcbi.1002588
    [6] H. Song, W. Jiang, S. Liu, Virus dynamics model with intracellular delays and immune response, Math. Biosci. Eng., 12(1) (2015), 185–208. https://doi.org/10.3934/mbe.2015.12.185 doi: 10.3934/mbe.2015.12.185
    [7] G. Dagasso, J. Urban, M. Kwiatkowska, Incorporating time delays in the mathematical modelling of the human immune response in viral infections, Procedia Comput. Sci., 185 (2021), 144–151. https://doi.org/10.1016/j.procs.2021.05.016 doi: 10.1016/j.procs.2021.05.016
    [8] B. Wacker, Revisiting the classical target cell limited dynamical within-host HIV model: Basic mathematical properties and stability analysis, Math. Biosci. Eng., 21 (2024), 7805–7829. https://doi.org/10.3934/mbe.2024343 doi: 10.3934/mbe.2024343
    [9] I. Ghosh, Within host dynamics of SARS-CoV-2 in humans: Modeling immune responses and antiviral treatments, SN Comput. Sci., 2 (2021), 482. https://doi.org/10.1007/s42979-021-00919-8 doi: 10.1007/s42979-021-00919-8
    [10] Z. Zhou, D. Li, Z. Zhao, S. Shi, J. Wu, J. Li, et al., Dynamical modelling of viral infection and cooperative immune protection in COVID-19 patients, PLoS Comput. Biol., 19 (2023), e1011383. https://doi.org/10.1371/journal.pcbi.1011383 doi: 10.1371/journal.pcbi.1011383
    [11] N. K. Vaidya, A. Bloomquist, A. S. Perelson, Modeling within-host dynamics of SARS-CoV-2 infection: A case study in ferrets, Viruses, 13 (2021), 1635. https://doi.org/10.3390/v13081635 doi: 10.3390/v13081635
    [12] J. E. Forde, Delay Differential Equation models in Mathematical Biology, Ph.D. thesis, University of Michigan, 2005.
    [13] L. Rong, A. S. Perelson, Modeling latently infected cell activation: Viral and latent reservoir persistence, and viral blips in HIV-infected patients on potent therapy, PLoS Comput. Biol., 5 (2009), e1000533. https://doi.org/10.1371/journal.pcbi.1000533 doi: 10.1371/journal.pcbi.1000533
    [14] P. W. Nelson, A. S. Perelson, Mathematical analysis of delay differential equation models of HIV-1 infection, Math. Biosci., 179 (2002), 73–94. https://doi.org/10.1016/S0025-5564(02)00099-8 doi: 10.1016/S0025-5564(02)00099-8
    [15] Z. Hu, J. Yang, Q. Li, S. Liang, D. Fan, Mathematical analysis of stability and Hopf bifurcation in a delayed HIV infection model with saturated immune response, Math. Methods Appl. Sci., 47 (2024), 9834–9857. https://doi.org/10.1002/mma.10097 doi: 10.1002/mma.10097
    [16] A. Goyal, D. B. Reeves, E. F. Cardozo-Ojeda, J. T. Schiffer, B. T. Mayer, Viral load and contact heterogeneity predict SARS-CoV-2 transmission and super-spreading events, eLife, 10 (2021), e63537. https://doi.org/10.7554/eLife.63537 doi: 10.7554/eLife.63537
    [17] N. G. Davies, P. Klepac, Y. Liu, K. Prem, M. Jit, R. M. Eggo, Age-dependent effects in the transmission and control of COVID-19 epidemics, Nat. Med., 26 (2020), 1205–1211. https://doi.org/10.1038/s41591-020-0962-9 doi: 10.1038/s41591-020-0962-9
    [18] O. Puhach, K. Adea, N. Hulo, P. Sattonnet, C. Genecand, A. Iten, et al., Infectious viral load in unvaccinated and vaccinated individuals infected with ancestral, Delta or Omicron SARS-CoV-2, Nat. Med., 28 (2022), 1491–1500. https://doi.org/10.1038/s41591-022-01816-0 doi: 10.1038/s41591-022-01816-0
    [19] A. Bouchnita, A. Jebrane, A hybrid multi-scale model of COVID-19 transmission dynamics to assess the potential of non-pharmaceutical interventions, Chaos Solitons Fractals, 138 (2020), 109941. https://doi.org/10.1016/j.chaos.2020.109941 doi: 10.1016/j.chaos.2020.109941
    [20] A. Lamghari, D. S. I. Kanté, A. Jebrane, A. Hakim, Modeling the impact of distancing measures on infectious disease spread: A case study of COVID-19 in the Moroccan population, Math. Biosci. Eng., 21 (2024), 4370–4396. https://doi.org/10.3934/mbe.2024193 doi: 10.3934/mbe.2024193
    [21] D. S. I. Kanté, A. Jebrane, A. Bouchnita, A. Hakim, Estimating the risk of contracting COVID-19 in different settings using a multiscale transmission dynamics model, Mathematics, 11 (2023), 254. https://doi.org/10.3390/math11010254 doi: 10.3390/math11010254
    [22] G. McCarthy, H. M. Dobrovolny, Determining the best mathematical model for implementation of non-pharmaceutical interventions, Math. Biosci. Eng., 22 (2025), 700–724. https://doi.org/10.3934/mbe.2025026 doi: 10.3934/mbe.2025026
    [23] K. Ngoun, N. Alvarez, A. Awad, H. Ryu, Immune dysregulation in COVID-19: Mathematical modeling of the within-host dynamics, Math. Biosci. Eng., 23 (2026), 987–1049. https://doi.org/10.3934/mbe.2026038 doi: 10.3934/mbe.2026038
    [24] P. Baccam, C. Beauchemin, C. A. Macken, F. G. Hayden, A. S. Perelson, Kinetics of influenza a virus infection in humans, J. Virol., 80 (2006), 7590–7599. https://doi.org/10.1128/JVI.01623-05 doi: 10.1128/JVI.01623-05
    [25] R. Ke, C. Zitzmann, D. D. Ho, R. M. Ribeiro, A. S. Perelson, In vivo kinetics of SARS-CoV-2 infection and its relationship with a person's infectiousness, Proc. Natl. Acad. Sci. U.S.A., 118 (2021), e2111477118. https://doi.org/10.1073/pnas.2111477118 doi: 10.1073/pnas.2111477118
    [26] K. S. Kim, K. Ejima, S. Iwanami, Y. Fujita, H. Ohashi, Y. Koizumi, et al., A quantitative model used to compare within-host SARS-CoV-2, MERS-CoV and SARS-CoV dynamics provides insights into the pathogenesis and treatment of SARS-CoV-2, PLoS Biol., 19 (2021), e3001128. https://doi.org/10.1371/journal.pbio.3001128 doi: 10.1371/journal.pbio.3001128
    [27] G. A. Bocharov, A. A. Romanyukha, Mathematical model of antiviral immune response. Ⅲ. Influenza A virus infection, J. Theor. Biol., 167(4) (1994), 323–360. https://doi.org/10.1006/jtbi.1994.1074 doi: 10.1006/jtbi.1994.1074
    [28] D. Wodarz, Killer Cell Dynamics: Mathematical and Computational Approaches to Immunology, Springer-Verlag, New York, 2007. https://doi.org/10.1007/978-0-387-68733-9
    [29] J. E. Ferrell, S. H. Ha, Ultrasensitivity part Ⅰ: Michaelian responses and zero-order ultrasensitivity, Trends Biochem. Sci., 39 (2014), 496–503. https://doi.org/10.1016/j.tibs.2014.08.003 doi: 10.1016/j.tibs.2014.08.003
    [30] K. Guram, S. S. Kim, V. Wu, P. D. Sanders, S. Patel, S. P. Schoenberger, et al., A threshold model for T-cell activation in the era of checkpoint blockade immunotherapy, Front. Immunol., 10 (2019), 491. https://doi.org/10.3389/fimmu.2019.00491 doi: 10.3389/fimmu.2019.00491
    [31] D. J. D. Earn, P. Rohani, B. M. Bolker, B. T. Grenfell, A simple model for complex dynamical transitions in epidemics, Science, 287 (2000), 667–670. https://doi.org/10.1126/science.287.5453.667 doi: 10.1126/science.287.5453.667
    [32] S. Altizer, A. Dobson, P. Hosseini, P. Hudson, M. Pascual, P. Rohani, Seasonality and the dynamics of infectious diseases, Ecol. Lett., 9 (2006), 467–484. https://doi.org/10.1111/j.1461-0248.2005.00879.x doi: 10.1111/j.1461-0248.2005.00879.x
    [33] S. Setianto, D. Hidayat, Modeling the time-dependent transmission rate using Gaussian pulses for analyzing the COVID-19 outbreaks in the world, Sci. Rep., 13 (2023), 4466. https://doi.org/10.1038/s41598-023-31714-5 doi: 10.1038/s41598-023-31714-5
    [34] S. Funk, E. Gilad, C. Watkins, V. A. A. Jansen, The spread of awareness and its impact on epidemic outbreaks, Proc. Natl. Acad. Sci. U.S.A., 106 (2009), 6872–6877. https://doi.org/10.1073/pnas.0810762106 doi: 10.1073/pnas.0810762106
    [35] Y. Kuang, Delay Differential Equations with Applications in Population Dynamics, Academic Press, Boston, MA, 1993.
    [36] J. K. Hale, S. M. Verduyn Lunel, Introduction to Functional Differential Equations, Springer-Verlag, New York, 1993. https://doi.org/10.1007/978-1-4612-4342-7
    [37] C. L. Dym, Principles of Mathematical Modeling, 2nd edition, Elsevier Academic Press, Amsterdam, 2004.
    [38] T. Erneux, Applied Delay Differential Equations, Springer, New York, 2009. https://doi.org/10.1007/978-0-387-74372-1
    [39] E. Zeidler, Nonlinear Functional Analysis and its Applications I: Fixed-Point Theorems, Springer, New York, 1986.
    [40] A. Handel, I. M. Longini, R. Antia, Towards a quantitative understanding of the within-host dynamics of influenza A infections, J. R. Soc. Interface, 7 (2010), 35–47. https://doi.org/10.1098/rsif.2009.0067 doi: 10.1098/rsif.2009.0067
    [41] A. Iggidr, J. Mbang, G. Sallet, Stability analysis of within-host parasite models with delays, Math. Biosci., 209 (2007), 51–75. https://doi.org/10.1016/j.mbs.2007.01.008 doi: 10.1016/j.mbs.2007.01.008
    [42] C. Li, J. Xu, J. Liu, Y. Zhou, The within-host viral kinetics of SARS-CoV-2, Math. Biosci. Eng., 17 (2020), 2853–2861. https://doi.org/10.3934/mbe.2020159 doi: 10.3934/mbe.2020159
    [43] F. X. Lescure, L. Bouadma, D. Nguyen, M. Parisey, P. H. Wicky, S. Behillil, et al., Clinical and virological data of the first cases of COVID-19 in Europe: A case series, Lancet Infect. Dis., 20 (2020), 697–706. https://doi.org/10.1016/S1473-3099(20)30200-0 doi: 10.1016/S1473-3099(20)30200-0
    [44] A. K. McElroy, R. S. Akondy, D. R. McIlwain, H. Chen, Z. Bjornson-Hooper, N. Mukherjee, et al., Immunologic timeline of Ebola virus disease and recovery in humans, JCI Insight, 5 (2020), e137260. https://doi.org/10.1172/jci.insight.137260 doi: 10.1172/jci.insight.137260
    [45] J. D. Challenger, C. Y. Foo, Y. Wu, A. W. C. Yan, M. Moradi Marjaneh, F. Liew, et al., Modelling upper respiratory viral load dynamics of SARS-CoV-2, BMC Med., 20(1) (2022), 25. https://doi.org/10.1186/s12916-021-02220-0 doi: 10.1186/s12916-021-02220-0
    [46] H. C. Stankiewicz Karita, T. Q. Dong, C. Johnston, K. M. Neuzil, M. K. Paasche-Orlow, P. J. Kissinger, et al., Trajectory of viral RNA load among persons with incident SARS-CoV-2 G614 infection (Wuhan strain) in association with COVID-19 symptom onset and severity, JAMA Netw. Open, 5 (2022), e2142796. https://doi.org/10.1001/jamanetworkopen.2021.42796 doi: 10.1001/jamanetworkopen.2021.42796
    [47] S. A. Iyaniwura, R. M. Ribeiro, C. Zitzmann, T. Phan, R. Ke, A. S. Perelson, The kinetics of SARS-CoV-2 infection based on a human challenge study, Proc. Natl. Acad. Sci., 121 (2024), e2406303121. https://doi.org/10.1073/pnas.2406303121 doi: 10.1073/pnas.2406303121
    [48] C. Hadjichrysanthou, S. Cauchemez, M. Baguelin, Within-host dynamics of influenza A virus, J. R. Soc. Interface, 13 (2016), 20160289. https://doi.org/10.1098/rsif.2016.0289 doi: 10.1098/rsif.2016.0289
    [49] K. Owens, S. Esmaeili, J. T. Schiffer, Heterogeneous SARS-CoV-2 kinetics due to variable timing and intensity of immune responses, JCI Insight, 9 (2024), e176286. https://doi.org/10.1172/jci.insight.176286 doi: 10.1172/jci.insight.176286
    [50] J. A. Hay, S. M. Kissler, J. R. Fauver, C. Mack, C. G. Tai, R. M. Samant, et al., Quantifying the impact of immune history and variant on SARS-CoV-2 viral kinetics and infection rebound: A retrospective cohort study, eLife, 11 (2022), e81849. https://doi.org/10.7554/eLife.81849 doi: 10.7554/eLife.81849
    [51] M. Koutsakos, W. S. Lee, A. Reynaldi, H.-X. Tan, G. Gare, P. Kinsella, et al., The magnitude and timing of recalled immunity after breakthrough infection is shaped by SARS-CoV-2 variants, Immunity, 55 (2022), 1316–1326.e4. https://doi.org/10.1016/j.immuni.2022.05.018 doi: 10.1016/j.immuni.2022.05.018
    [52] P. Cao, A. W. C. Yan, J. M. Heffernan, S. Petrie, R. G. Moss, L. A. Carolan, et al., Innate immunity and the inter-exposure interval determine the dynamics of secondary influenza virus infection and explain observed viral hierarchies, PLoS Comput. Biol., 11 (2015), e1004334. https://doi.org/10.1371/journal.pcbi.1004334 doi: 10.1371/journal.pcbi.1004334
    [53] B. Chatterjee, H. S. Sandhu, N. M. Dixit, Modeling recapitulates the heterogeneous outcomes of SARS-CoV-2 infection and quantifies the differences in the innate immune and CD8 T-cell responses between patients experiencing mild and severe symptoms, PLoS Pathog., 18 (2022), e1010630. https://doi.org/10.1371/journal.ppat.1010630 doi: 10.1371/journal.ppat.1010630
    [54] R. Ke, P. P. Martinez, R. L. Smith, L. L. Gibson, A. Mirza, M. Conte, et al., Daily longitudinal sampling of SARS-CoV-2 infection reveals substantial heterogeneity in infectiousness, Nat. Microbiol., 7 (2022), 640–652. https://doi.org/10.1038/s41564-022-01105-z doi: 10.1038/s41564-022-01105-z
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