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Modeling the Within-Host Dynamics of SARS-CoV-2 Infection Based on Antiviral Treatment

Author

Listed:
  • Tianqi Song

    (School of Economics and Management, Shanghai Maritime University, Shanghai 201306, China)

  • Yishi Wang

    (Shanghai Institute of Aerospace System Engineering, Shanghai 201100, China)

  • Xi Gu

    (School of Economics and Management, Shanghai Maritime University, Shanghai 201306, China)

  • Sijia Qiao

    (Department of Finance, Shanghai National Accounting Institute, Shanghai 201702, China)

Abstract

The COVID-19 pandemic has highlighted the profound impact of the SARS-CoV-2 virus as a significant threat to human health. There is an urgent need to develop a comprehensive understanding of the current outbreak by studying the dynamics of the virus within the human body. In this research, we present a mathematical model that explores the progression of SARS-CoV-2 infection, taking into account both the innate and adaptive immune responses. We calculated the basic reproduction number and analyzed the stability of the equilibria. Additionally, we demonstrated the existence of a periodic solution through numerical simulations. By conducting a global sensitivity analysis, we determined the significance of the model parameters and investigated the influence of key parameters on viral load. The results emphasized the crucial roles of cytokines and antibodies in shaping the dynamics of SARS-CoV-2. Furthermore, we evaluated the effectiveness of antiviral treatment in controlling the dynamics of SARS-CoV-2 infection. Our findings revealed a direct relationship between the basic reproduction number and the impact of antiviral treatment. To evaluate the effect of antiviral treatment on viral load, we conducted numerical simulations.

Suggested Citation

  • Tianqi Song & Yishi Wang & Xi Gu & Sijia Qiao, 2023. "Modeling the Within-Host Dynamics of SARS-CoV-2 Infection Based on Antiviral Treatment," Mathematics, MDPI, vol. 11(16), pages 1-19, August.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:16:p:3485-:d:1215668
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    References listed on IDEAS

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    2. Farooq, Junaid & Bazaz, Mohammad Abid, 2020. "A novel adaptive deep learning model of Covid-19 with focus on mortality reduction strategies," Chaos, Solitons & Fractals, Elsevier, vol. 138(C).
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