Immunokinetic Model for COVID-19 Patients

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Abstract

In this paper, we develop a fractional-order differential model for the dynamics of immune responses to SARS-CoV-2 viral load in one host. In the model, a fractional-order derivative is incorporated to represent the effects of temporal long-run memory on immune cells and tissues for any age group of patients. The population of cytotoxic T-cells (CD8 + ), natural killer (NK) cells and infected viruses are unknown in this model. Some interesting sufficient conditions that ensure the asymptotic stability of the steady states are obtained.

This model indicates some complex phenomena in COVID-19 such as “immune exhaustion” and “Long COVID”. Sensitivity analysis is also investigated for model parameters to determine the parameters that are effective in determining of the long COVID duration, disease control and future treatment as well as vaccine design. The model is verified with clinical and experimental data of 5 patients with COVID-19.

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  1. SciScore for 10.1101/2022.01.13.476252: (What is this?)

    Please note, not all rigor criteria are appropriate for all manuscripts.

    Table 1: Rigor

    Ethicsnot detected.
    Sex as a biological variablenot detected.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.

    Table 2: Resources

    No key resources detected.


    Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).


    Results from LimitationRecognizer: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.

    Results from TrialIdentifier: No clinical trial numbers were referenced.


    Results from Barzooka: We did not find any issues relating to the usage of bar graphs.


    Results from JetFighter: We did not find any issues relating to colormaps.


    Results from rtransparent:
    • Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
    • Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
    • No protocol registration statement was detected.

    Results from scite Reference Check: We found no unreliable references.


    About SciScore

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