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- W4320164441 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." @default.
- W4320164441 created "2023-02-13" @default.
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- W4320164441 date "2023-01-01" @default.
- W4320164441 modified "2023-09-29" @default.
- W4320164441 title "THE WITHIN-HOST VIRAL KINETICS OF SARS-COV-2" @default.
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- W4320164441 doi "https://doi.org/10.11948/20220389" @default.
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