Adaptive immunity, performed by T and B lymphocytes, seeks total virus elimination through specific recognition of viral antigens. It has been shown that innate or adaptive immune response regulation variations are associated with an excessive immune response, leading to tissue damage with an increased risk of complications and death. This article is a novel contribution focused on models that represent pathogenic interactions with humans. In our case, the objective was to build and analyze a mathematical model for SARS-CoV-2 infection in the human host, including elements of respiratory cell dynamics, viral particles, and immune-responding cells. The methodology developed considered modeling by means of ordinary differential equations, validation by comparing referenced studies, and sensitivity analysis with respect to the variables considered. Finally, a comparison of simulation models was performed, verifying that an increase in viral particles increases the response of some adaptive immune system cells in the human host.
Citation: Ledyz Cuesta-Herrera, Luis Pastenes, Fernando Córdova-Lepe, Ariel D. Arencibia. Mathematical modeling of the immune response mediated by human T-helper lymphocytes in viral diseases[J]. Mathematical Biosciences and Engineering, 2025, 22(11): 2807-2825. doi: 10.3934/mbe.2025103
Adaptive immunity, performed by T and B lymphocytes, seeks total virus elimination through specific recognition of viral antigens. It has been shown that innate or adaptive immune response regulation variations are associated with an excessive immune response, leading to tissue damage with an increased risk of complications and death. This article is a novel contribution focused on models that represent pathogenic interactions with humans. In our case, the objective was to build and analyze a mathematical model for SARS-CoV-2 infection in the human host, including elements of respiratory cell dynamics, viral particles, and immune-responding cells. The methodology developed considered modeling by means of ordinary differential equations, validation by comparing referenced studies, and sensitivity analysis with respect to the variables considered. Finally, a comparison of simulation models was performed, verifying that an increase in viral particles increases the response of some adaptive immune system cells in the human host.
| [1] |
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
|
| [2] |
R. Blanco-Rodríguez, X. Du, E. Hernández-Vargas, Computational simulations to dissect the cell immune response dynamics for severe and critical cases of SARS-CoV-2 infection, Comput. Methods Programs Biomed., 211 (2021), 106412. https://doi.org/10.1016/j.cmpb.2021.106412 doi: 10.1016/j.cmpb.2021.106412
|
| [3] |
S. Wang, Y. Pan, Q. Wang, H. Miao, A. N. Brown, L. Rong, Modeling the viral dynamics of SARS-CoV-2 infection, Math. Biosci., 328 (2020), 108438. https://doi.org/10.1016/j.mbs.2020.108438 doi: 10.1016/j.mbs.2020.108438
|
| [4] |
E. A. Hernandez-Vargas, J. X. Velasco-Hernandez, In-host mathematical modelling of COVID-19 in humans, Ann. Rev. Control, 50 (2020), 448–456. https://doi.org/10.1016/j.arcontrol.2020.09.006 doi: 10.1016/j.arcontrol.2020.09.006
|
| [5] |
L. Cuesta-Herrera, L. Pastenes, F. Córdova-Lepe, A. D. Arencibia, H. A. Torres-Mantilla, Cell lysis analysis for respiratory viruses through simulation modeling, J. Phys. Conf. Ser., 2159 (2022), 012002. https://doi.org/10.1088/1742-6596/2159/1/012002 doi: 10.1088/1742-6596/2159/1/012002
|
| [6] |
Q. Sun, T. Miyoshi, S. Richard, Analysis of COVID-19 in Japan with extended SEIR model and ensemble Kalman filter, J. Comput. Appl. Math., 419 (2023), 114772. https://doi.org/10.1016/j.cam.2022.114772 doi: 10.1016/j.cam.2022.114772
|
| [7] |
E. Ngondiep, A robust numerical two-level second-order explicit approach to predicting the spread of Covid-2019 pandemic with undetected infectious cases, J. Comput. Appl. Math., 403 (2022), 113852. https://doi.org/10.1016/j.cam.2021.113852 doi: 10.1016/j.cam.2021.113852
|
| [8] |
S. Q. Du, W. Yuan, Mathematical modeling of interaction between innate and adaptive immune responses in COVID-19 and implications for viral pathogenesis, J. Med. Virol., 92 (2020), 1615–1628. https://doi.org/10.1002/jmv.25866 doi: 10.1002/jmv.25866
|
| [9] | F. C. Schuffeneger, M. Gajardo, M. Freundlich, Eje renina angiotensina, enzima convertidora de angiotensina 2 y Coronavirus, Rev. Chil. Pediatr., 91 (2020). http://dx.doi.org/10.32641/rchped.vi91i3.2548 |
| [10] | D. A. J. Tyrrell, S. H. Myint, Coronaviruses, University of Texas Medical Branch at Galveston, 1996. |
| [11] |
S. Wang, M. Hao, Z. Pan, J. Lei, X. Zou, Data-driven multi-scale mathematical modeling of SARS-CoV-2 infection reveals heterogeneity among COVID-19 patients, PLoS Comput. Biol., 17 (2021), e1009587. https://doi.org/10.1371/journal.pcbi.1009587 doi: 10.1371/journal.pcbi.1009587
|
| [12] | P. Vetter, C. S. Eberhardt, B. Meyer, P. A. M. Murillo, G. Torriani, F. Pigny, et al., Daily viral kinetics and innate and adaptive immune response assessment in COVID-19: A case series, mSphere, 5 (2020). https://doi.org/10.1128/mSphere.00827-20 |
| [13] |
A. Gonçalves, J. Bertrand, R. Ke, E. Comets, X. De Lamballerie, D. Malvy, et al., Timing of antiviral treatment initiation is critical to reduce SARS-CoV-2 viral load, CPT: Pharmacometrics Syst. Pharmacol., 9 (2020), 509–514. https://doi.org/10.1002/psp4.12543 doi: 10.1002/psp4.12543
|
| [14] |
A. Goyal, E. F. Cardozo-Ojeda, J. T. Schiffer, Potency and timing of antiviral therapy as determinants of duration of SARS-CoV-2 shedding and intensity of inflammatory response, Sci. Adv., 6 (2020), eabc7112. https://doi.org/10.1126/sciadv.abc7112 doi: 10.1126/sciadv.abc7112
|
| [15] |
L. Cuesta-Herrera, L. Pastenes, A. D. Arencibia, F. Córdova-Lepe, C. Montoya, Dynamics of activation and regulation of the immune response to attack by viral pathogens using mathematical modeling, Mathematics, 12 (2024), 2681. https://doi.org/10.3390/math12172681 doi: 10.3390/math12172681
|
| [16] |
K. A. Pawelek, D. Dor Jr, C. Salmeron, A. handel, within-host models of high and low pathogenic influenza virus infections: The role of macrophages, PLoS One, 11 (2016), e0150568. https://doi.org/10.1371/journal.pone.0150568 doi: 10.1371/journal.pone.0150568
|
| [17] |
O. O. Okundalaye, W. A. M. Othman, A. S. Oke, Toward an efficient approximate analytical solution for 4-compartment COVID-19 fractional mathematical model, J. Comput. Appl. Math., 416 (2022), 114506. https://doi.org/10.1016/j.cam.2021.113852 doi: 10.1016/j.cam.2021.113852
|
| [18] |
L. Fan, Z. Qiu, Q. Deng, T. Guo, L. Rong, Modeling SARS-CoV-2 infection dynamics: Insights into viral clearance and immune synergy, Bull. Math. Biol., 87 (2025), 67. https://doi.org/10.1007/s11538-025-01442-0 doi: 10.1007/s11538-025-01442-0
|
| [19] | S. M. Ciupe, J. M. Heffernan, In-host modeling, Infect. Dis. Modell., 2 (2017), 188–202. https://doi.org/10.1016/j.idm.2017.04.002 |
| [20] |
H. McCallum, N. Barlow, J. Hone, How should pathogen transmission be modelled?, Trends Ecol. Evol., 16 (2001), 295–300. https://doi.org/10.1016/S0169-5347(01)02144-9 doi: 10.1016/S0169-5347(01)02144-9
|
| [21] |
K. S. Kim, K. Ejima, Y. Ito, S. Iwanami, H. Ohashi, Y. Koizumi, et al., Modelling SARS-CoV-2 dynamics: Implications for therapy, PLoS Biol., 19 (2021), e3001128. https://doi.org/10.1371/journal.pbio.3001128 doi: 10.1371/journal.pbio.3001128
|
| [22] |
M. Menale, R. Travaglini, A nonconservative kinetic model under the action of an external force field for modeling the medical treatment of autoimmune response, Commun. Nonlinear Sci. Numer. Simul., 137 (2024), 108126. https://doi.org/10.1016/j.cnsns.2024.108126 doi: 10.1016/j.cnsns.2024.108126
|
| [23] |
L. Cuesta-Herrera, L. Pastenes, F. Cordova-Lepe, A. D. Arencibia, H. Torres-Mantilla, J. P. Gutierrez-Jara, Analysis of SEIR-type models used at the beginning of COVID-19 pandemic reported in high-impact journals, Medwave, 22 (2022), 2552. https://doi.org/10.5867/medwave.2022.08.2552 doi: 10.5867/medwave.2022.08.2552
|
| [24] |
J. E. Andrades-Grassi, L. Cuesta-Herrera, G. Bianchi-Pérez, H. C. Grassi, J. Y. López-Hernández, H. Torres-Mantilla, Spatial analysis of risk of morbidity and mortality by COVID-19 in Europe and the Mediterranean in the year 2020, Cuadernos Geográficos, 60 (2021), 279–294. https://doi.org/10.30827/cuadgeo.v60i1.15492 doi: 10.30827/cuadgeo.v60i1.15492
|
| [25] |
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
|
| [26] |
P. Abuin, A. Anderson, A. Ferramosca, E. A. Hernandez-Vargas, A. H. Gonzalez, Characterization of SARS-CoV-2 dynamics in the host, Ann. Rev. Control, 50 (2020), 457–468. https://doi.org/10.1016/j.arcontrol.2020.09.008 doi: 10.1016/j.arcontrol.2020.09.008
|
| [27] | E. A. Hernandez-Vargas, Modeling and Control of Infectious Diseases in the Host: With MATLAB and R, Academic Press, 2019. |
| [28] |
M. A. Nowak, C. R. M. Bangham, Population dynamics of immune responses to persistent viruses, Science, 272 (1996), 74–79. https://doi.org/10.1126/science.272.5258.74 doi: 10.1126/science.272.5258.74
|
| [29] |
T. W. Alleman, J. Vergeynst, L. De Visscher, M. Rollier, E. Torfs, I. Nopens, et al., Assessing the effects of non-pharmaceutical interventions on SARS-CoV-2 transmission in Belgium by means of an extended SEIQRD model and public mobility data, Epidemics, 37 (2021), 100505. https://doi.org/10.1016/j.epidem.2021.100505 doi: 10.1016/j.epidem.2021.100505
|
| [30] |
B. C. K. Choi, A. W. P. Pak, A simple approximate mathematical model to predict the number of severe acute respiratory syndrome cases and deaths, J. Epidemiol. Commun. Health, 57 (2003), 831–835. https://doi.org/10.1136/jech.57.10.831 doi: 10.1136/jech.57.10.831
|
| [31] |
M. Lipsitch, T. Cohen, B. Cooper, J. M. Robins, S. Ma, L. James, et al., Transmission Dynamics and Control of Severe Acute Respiratory Syndrome, Science, 300 (2003), 1966–1970. https://doi.org/10.1126/science.1086616 doi: 10.1126/science.1086616
|
| [32] |
J. O. Lloyd-Smith, A. P. Galvani, W. M. Getz, Curtailing transmission of severe acute respiratory syndrome within a community and its hospital, Proc. R. Soc. London Ser. B Biol. Sci., 270 (2003), 1979–1989. https://doi.org/10.1098/rspb.2003.2481 doi: 10.1098/rspb.2003.2481
|
| [33] |
S. Riley, C. Fraser, C. A. Donnelly, A. C. Ghani, L. J. Abu-Raddad, A. J. Hedley, et al., Transmission dynamics of the etiological agent of sars in hong kong: Impact of public health interventions, Science, 300 (2003), 1961–1966. https://doi.org/10.1126/science.1086478 doi: 10.1126/science.1086478
|
| [34] |
R. Della Marca, N. Loy, A. Tosin, An SIR model with viral load-dependent transmission, J. Math.Biol., 86 (2023), 61. https://doi.org/10.1007/s00285-023-01901-z doi: 10.1007/s00285-023-01901-z
|
| [35] |
C. E. Mills, J. M. Robins, M. Lipsitch, Transmissibility of 1918 pandemic influenza, Nature, 432 (2004), 904–906. https://doi.org/10.1038/nature03063 doi: 10.1038/nature03063
|
| [36] |
A. Stegeman, A. Bouma, A. R. W. Elbers, M. C. M. de Jong, G. Nodelijk, F. de Klerk, et al., Avian influenza A virus (H7N7) epidemic in The Netherlands in 2003: Course of the epidemic and effectiveness of control measures, J. Infect. Dis., 190 (2004), 2088–2095. https://doi.org/10.1086/425583 doi: 10.1086/425583
|
| [37] |
K. Y. Ng, M. M. Gui, COVID-19: Development of a robust mathematical model and simulation package with consideration for ageing population and time delay for control action and resusceptibility, Phys. D Nonlinear Phenom., 411 (2020), 132599. https://doi.org/10.1016/j.physd.2020.132599 doi: 10.1016/j.physd.2020.132599
|
| [38] |
M. J. Wonham, T. de-Camino-Beck, M. A. Lewis, An epidemiological model for West Nile virus: Invasion analysis and control applications, Proc. Roy. Soc. London Ser. B Biol. Sci., 271 (2004), 501–507. https://doi.org/10.1098/rspb.2003.2608 doi: 10.1098/rspb.2003.2608
|
| [39] |
T. J. Sego, J. O. Aponte-Serrano, J. Ferrari Gianlupi, S. R. Heaps, K. Breithaupt, L. Brusch, et al., A modular framework for multiscale, multicellular, spatiotemporal modeling of acute primary viral infection and immune response in epithelial tissues and its application to drug therapy timing and effectiveness, PLoS Comput. Biol., 16 (2020), e1008451. https://doi.org/10.1371/journal.pcbi.1008451 doi: 10.1371/journal.pcbi.1008451
|
| [40] | J. A. Owen, J. Punt, S. A. Stranford, Kuby Immunol, WH Freeman New York, NY, USA, 2013. |
| [41] | A. K. Abbas, A. H. Lichtman, S. Pillai, Cellular and Molecular Immunology, 10th Edition, Elsevier Health Sciences, 2014. |
| [42] | L. Perko, Differential Equations and Dynamical Systems, Springer, New York, 2013. https://doi.org/10.1007/978-1-4613-0003-8 |
| [43] |
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
|
| [44] |
K. A. Walsh, K. Jordan, B. Clyne, D. Rohde, L. Drummond, P. Byrne, et al., SARS-CoV-2 detection, viral load and infectivity over the course of an infection, J. Infect., 81 (2020), 357–371. https://doi.org/10.1016/j.jinf.2020.06.067 doi: 10.1016/j.jinf.2020.06.067
|
| [45] |
S. Lee, T. Kim, E. Lee, C. Lee, H. Kim, H. Rhee, et al., Clinical course and molecular viral shedding among asymptomatic and symptomatic patients with SARS-CoV-2 infection in a community treatment center in the Republic of Korea, JAMA Int. Med., 180 (2020), 1447–1452. https://doi.org/10.1001/jamainternmed.2020.3862 doi: 10.1001/jamainternmed.2020.3862
|
| [46] |
Y. Liu, L. Yan, L. Wan, T. Xiang, A. Le, J. Liu, et al., Viral dynamics in mild and severe cases of COVID-19, Lancet Infect. Dis., 20 (2020), 656–657. https://doi.org/10.1016/S1473-3099(20)30232-2 doi: 10.1016/S1473-3099(20)30232-2
|
| [47] |
C. Zhou, T. Zhang, H. Ren, S. Sun, X. Yu, J. Sheng, et al., Impact of age on duration of viral RNA shedding in patients with COVID-19, Aging, 12 (2020), 22399. https://doi.org/10.18632/aging.104114 doi: 10.18632/aging.104114
|
| [48] |
A. Amoddeo, A mathematical model and numerical simulation for SARS-CoV-2 dynamics, Sci. Rep., 13 (2023), 4575. https://doi.org/10.1038/s41598-023-31733-2 doi: 10.1038/s41598-023-31733-2
|
| [49] |
H. Laferl, H. Kelani, T. Seitz, B. Holzer, I. Zimpernik, A. Steinrigl, et al., An approach to lifting self-isolation for health care workers with prolonged shedding of SARS-CoV-2 RNA, Infection, 49 (2021), 95–101. https://doi.org/10.1007/s15010-020-01530-4 doi: 10.1007/s15010-020-01530-4
|
| [50] |
A. Singanayagam, M. Patel, A. Charlett, J. L. Bernal, V. Saliba, J. Ellis, et al., Duration of infectiousness and correlation with RT-PCR cycle threshold values in cases of COVID-19, England, January to May 2020, Eurosurveillance, 25 (2020), 2001483. https://doi.org/10.2807/1560-7917.ES.2020.25.32.2001483 doi: 10.2807/1560-7917.ES.2020.25.32.2001483
|
| [51] |
Y. Sohn, S. J. Jeong, W. S. Chung, J. H. Hyun, Y. J. Baek, Y. Cho, et al., Assessing viral shedding and infectivity of asymptomatic or mildly symptomatic patients with COVID-19 in a later phase, J. Clin. Med., 9 (2020), 2924. https://doi.org/10.3390/jcm9092924 doi: 10.3390/jcm9092924
|
| [52] |
K. Ejima, K. S. Kim, C. Ludema, A. I. Bento, S. Iwanami, Y. Fujita, et al., Estimation of the incubation period of COVID-19 using viral load data, Epidemics, 35 (2021), 100454. https://doi.org/10.1016/j.epidem.2021.100454 doi: 10.1016/j.epidem.2021.100454
|
| [53] | J. D. Challenger, C. Y. Foo, Y. Wu, M. M. Marjaneh, F. Liew, R. S. Thwaites, et al., Modelling upper respiratory viral load dynamics of SARS-CoV-2, BMC Med., 20 (2022). https://doi.org/10.1186/s12916-021-02220-0 |
| [54] |
S. Iwami, K. Sato, R. J. De Boer, K. Aihara, T. Miura, Y. Koyanagi, Identifying viral parameters from in vitro cell cultures, Front. Microbiol., 3 (2012), 319. https://doi.org/10.3389/fmicb.2012.00319 doi: 10.3389/fmicb.2012.00319
|
| [55] |
A. Bondesan, A. Piralla, E. Ballante, A. M. G. Pitrolo, S. Figini, F. Baldanti, et al., Predictability of viral load dynamics in the early phases of SARS-CoV-2 through a model-based approach, Math. Biosci. Eng., 22 (2025), 725–743. https://doi.org/10.3934/mbe.2025027 doi: 10.3934/mbe.2025027
|
| [56] |
R. Wölfel, V. M. Corman, W. Guggemos, M. Seilmaier, S. Zange, M. A. Müller, et al., Virological assessment of hospitalized patients with COVID-2019, Nature, 581 (2020), 465–469. https://doi.org/10.1038/s41586-020-2196-x doi: 10.1038/s41586-020-2196-x
|
| [57] |
M. Sadria, A. T. Layton, Use of angiotensin-converting enzyme inhibitors and angiotensin ii receptor blockers during the Covid-19 pandemic: A modeling analysis, PLoS Comput. Biol., 16 (2020), e1008235. https://doi.org/10.1371/journal.pcbi.1008235 doi: 10.1371/journal.pcbi.1008235
|