A Modular Mathematical Model of the Immune Response for Investigating the Pathogenesis of Infectious Diseases

Area of Science:

• Immunology
• Mathematical Biology
• Computational Science

Background:

• The COVID-19 pandemic underscored the need for mathematical modeling in understanding viral dynamics and immunology.
• International collaboration provided extensive experimental data crucial for developing and validating computational models.

Purpose of the Study:

• To create a modular mathematical model of the immune response, integrating innate and adaptive immunity, specifically for SARS-CoV-2 infection.
• To validate the model using clinical data from moderate COVID-19 cases and assess its predictive capabilities for various disease severities.

Main Methods:

• Developed a modular mathematical model of the immune response to SARS-CoV-2.
• Validated the model with experimental data including viral load, antibody levels, T cell counts, and IL-6.
• Performed parameter optimization, sensitivity, and identifiability analyses for model refinement.

Main Results:

• The model accurately simulates moderate, severe, and critical COVID-19 progression based on key indicators like viral load and IL-6.
• Validation scenarios confirmed the model's ability to reproduce biologically relevant immune behaviors under conditions like hyperactivation and co-infection.
• The model serves as a general immune module within the Digital Twin project.

Conclusions:

• The validated mathematical model provides a robust tool for understanding SARS-CoV-2 infection dynamics.
• This integrated immune module can advance COVID-19 research and serve as a foundation for modeling other infectious diseases.