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Predicting the Kinetic Coordination of Immune Response Dynamics in SARS-CoV-2 Infection: Implications for Disease Pathogenesis

Author

Listed:
  • Dmitry Grebennikov

    (Marchuk Institute of Numerical Mathematics, Russian Academy of Sciences, 119333 Moscow, Russia
    Moscow Center for Fundamental and Applied Mathematics at INM RAS, 119333 Moscow, Russia
    Sechenov First Moscow State Medical University, Ministry of Healthcare of the Russian Federation, 119991 Moscow, Russia)

  • Antonina Karsonova

    (Sechenov First Moscow State Medical University, Ministry of Healthcare of the Russian Federation, 119991 Moscow, Russia)

  • Marina Loguinova

    (The National Medical Research Centre for Endocrinology, Ministry of Healthcare of the Russian Federation, 117292 Moscow, Russia)

  • Valentina Casella

    (Infection Biology Laboratory, Department of Medicine and Life Sciences, Universitat Pompeu Fabra, 08003 Barcelona, Spain)

  • Andreas Meyerhans

    (Infection Biology Laboratory, Department of Medicine and Life Sciences, Universitat Pompeu Fabra, 08003 Barcelona, Spain
    Institució Catalana de Recerca i Estudis Avançats (ICREA), 08010 Barcelona, Spain)

  • Gennady Bocharov

    (Marchuk Institute of Numerical Mathematics, Russian Academy of Sciences, 119333 Moscow, Russia
    Moscow Center for Fundamental and Applied Mathematics at INM RAS, 119333 Moscow, Russia
    Sechenov First Moscow State Medical University, Ministry of Healthcare of the Russian Federation, 119991 Moscow, Russia)

Abstract

A calibrated mathematical model of antiviral immune response to SARS-CoV-2 infection is developed. The model considers the innate and antigen-specific responses to SARS-CoV-2 infection. Recently published data sets from human challenge studies with SARS-CoV-2 were used for parameter evaluation. The calibration of the mathematical model of SARS-CoV-2 infection is based on combining the parameter guesses from our earlier study of influenza A virus infection, some recent quantitative models of SARS-CoV-2 infection and clinical data-based parameter estimation of a subset of the model parameters. Hence, the calibrated mathematical model represents a theoretical exploration type of study, i.e., ‘in silico patient’ with mild-to-moderate severity phenotype, rather than a completely validated quantitative model of COVID-19 with respect to all its state-space variables. Understanding the regulation of multiple intertwined reaction components of the immune system is necessary for linking the kinetics of immune responses with the clinical phenotypes of COVID-19. Consideration of multiple immune reaction components in a single calibrated mathematical model allowed us to address some fundamental issues related to the pathogenesis of COVID-19, i.e., the sensitivity of the peak viral load to the parameters characterizing the antiviral specific response components, the kinetic coordination of the individual innate and adaptive immune responses, and the factors favoring a prolonged viral persistence. The model provides a tool for predicting the infectivity of patients, i.e., the amount of virus which is transmitted via droplets from the person infected with SARS-CoV-2, depending on the time of infection. The thresholds for variations of the innate and adaptive response parameters which lead to a prolonged persistence of SARS-CoV-2 due to the loss of a kinetic response synchrony/coordination between them were identified.

Suggested Citation

  • Dmitry Grebennikov & Antonina Karsonova & Marina Loguinova & Valentina Casella & Andreas Meyerhans & Gennady Bocharov, 2022. "Predicting the Kinetic Coordination of Immune Response Dynamics in SARS-CoV-2 Infection: Implications for Disease Pathogenesis," Mathematics, MDPI, vol. 10(17), pages 1-27, September.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:17:p:3154-:d:904890
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    References listed on IDEAS

    as
    1. Vasiliy N. Afonyushkin & Ilya R. Akberdin & Yulia N. Kozlova & Ivan A. Schukin & Tatyana E. Mironova & Anna S. Bobikova & Viktoriya S. Cherepushkina & Nikolaj A. Donchenko & Yulia E. Poletaeva & Fedor, 2022. "Multicompartmental Mathematical Model of SARS-CoV-2 Distribution in Human Organs and Their Treatment," Mathematics, MDPI, vol. 10(11), pages 1-21, June.
    2. Juan Carlos Chimal-Eguia, 2021. "Mathematical Model of Antiviral Immune Response against the COVID-19 Virus," Mathematics, MDPI, vol. 9(12), pages 1-19, June.
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    Cited by:

    1. Ziqi Liu & Ziqiao Yin & Zhilong Mi & Binghui Guo, 2023. "Long-COVID Inducement Mechanism Based on the Path Module Correlation Coefficient," Mathematics, MDPI, vol. 11(6), pages 1-14, March.
    2. Macauley Locke & Dmitry Grebennikov & Igor Sazonov & Martín López-García & Marina Loguinova & Andreas Meyerhans & Gennady Bocharov & Carmen Molina-París, 2024. "Exploring the Therapeutic Potential of Defective Interfering Particles in Reducing the Replication of SARS-CoV-2," Mathematics, MDPI, vol. 12(12), pages 1-28, June.
    3. Anastasia Mozokhina & Latifa Ait Mahiout & Vitaly Volpert, 2023. "Modeling of Viral Infection with Inflammation," Mathematics, MDPI, vol. 11(19), pages 1-15, September.

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