Modeling viral dynamics in SARS-CoV-2 infection based on differential equations and numerical analysis.

Area of Science:

• Virology
• Computational Biology
• Epidemiology

Background:

• Computational modeling is crucial for understanding viral pathogenesis, with prior success in modeling HIV-1 and Hepatitis B virus.
• The specific dynamics of SARS-CoV-2 pathogenesis remain inadequately explored through computational approaches.

Purpose of the Study:

• To propose and analyze differential equation-based models for SARS-CoV-2 dynamics.
• To elucidate the mechanisms underlying SARS-CoV-2 pathogenesis through computational simulation.

Main Methods:

• Development of four dynamical models representing host cells and SARS-CoV-2 viral particles.
• Numerical analysis including sensitivity and stability analysis of the models.
• Model simplification, Jacobian matrix eigenvalue calculation for stability, and calibration against mild and severe COVID-19 case data.

Main Results:

• Four initial models were simplified to two stable models based on sensitivity analysis.
• Calibration experiments demonstrated the models' ability to fit observed SARS-CoV-2 viral load data.
• Comparison of parameters between mild and severe cases indicated a significant correlation between cell-to-cell transmission and COVID-19 severity.

Conclusions:

• The study provides plausible computational models for SARS-CoV-2 dynamics.
• Cell-to-cell transmission is identified as a critical factor potentially driving COVID-19 severity.
• Further research is warranted to identify essential factors contributing to severe cases of COVID-19.