| Citation: |
Meng Wang, Yafei Zhao, Chen Zhang, Jie Lou. THE WITHIN-HOST VIRAL KINETICS OF SARS-COV-2[J]. Journal of Applied Analysis & Computation, 2023, 13(4): 2121-2152. doi: 10.11948/20220389
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THE WITHIN-HOST VIRAL KINETICS OF SARS-COV-2
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Abstract
Understanding the dynamics of SARS-COV-2 infection in vivo is crucial for exploring more effective treatments. This paper presents a series of dynamic models of viral infection in host. We use affine invariant set Monte Carlo algorithm to achieve parameter fitting and model selection, and study the structural identifiability of these models to determine if the clinical data could specify the model parameters. Then we analyze the actual identifiability and numerical simulation of the selected optimal model. Research shows that all models are structurally identifiable, and data noise has little effect on the actual identifiability of key parameters. Through numerical simulation we found the key factors that may cause cytokine storms. In addition, we also obtain some qualitative conclusions of the model, including the infection threshold, the stability of the equilibrium state and the periodic solution. Studies have found that viral load may exhibit complex periodic motions in some cases, which may provide new evidence to handle repeated reactivations among new corona virus infections.
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Keywords:
- Dynamics model /
- SARS-COV-2 /
- identifiability analysis /
- model selection /
- periodic solution
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References
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Meng Wang, Yafei Zhao, Chen Zhang, Jie Lou. THE WITHIN-HOST VIRAL KINETICS OF SARS-COV-2[J]. Journal of Applied Analysis & Computation, 2023, 13(4): 2121-2152. doi: 10.11948/20220389
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Figure 1.
Diagram of the dynamics of SARS-COV-2 infection. Healthy type Ⅱ alveolar cells are produced at a constant rate of
$ \alpha $ $ \omega $ $ \theta $ $ q $ $ \omega $ $ \mu $ $ \gamma $ $ m $ $ p $ -
Figure 2.
Fitting results. (a) Fitting curve of the T lymphocyte and its 95% confidence interval. (b) Fitting curve of the COVID-19 virus load and its 95% confidence interval.
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Figure 3.
Scatterplots showing parameter estimates for 100 simulated data sets use Affine Invariant Ensemble Markov chain Monte Carlo algorithm for Poisson noise. Beat-fit parameters (indicated by red stars) are as given in Table 5. Note that the parameters
$ \theta $ $ q $ $ n $ $ \delta, \beta_{v}, \alpha $ -
Figure 4.
The curve of viral load under different combinations of parameters. The black dots correspond to the peak of curves.
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Figure 5.
The figure on the left shows the number of cells infected by the virus under different combinations of parameters. The figure on the right shows the number of T lymphocytes under different Hill coefficients.
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Figure 6.
When
$ \delta=850<\delta_{H} $