Divergent COVID-19 Disease Trajectories Predicted by a DAMP-Centered Immune Network Model

Area of Science:

• Immunology
• Virology
• Mathematical Biology

Background:

• COVID-19 presents with diverse clinical outcomes, from mild illness to severe disease, persistent symptoms, or viral recrudescence.
• The wide spectrum of disease may stem from SARS-CoV-2's interference with the pathogen-associated molecular pattern (PAMP) response, particularly dampened type I interferon signaling.
• This immune dysregulation shifts the balance towards damage-associated molecular pattern (DAMP) signaling, influencing disease severity.

Purpose of the Study:

• To investigate the hypothesis that delayed PAMP signaling drives the diverse clinical manifestations of COVID-19.
• To develop a mathematical model that captures the immune response dynamics in COVID-19 patients.
• To identify distinct patient archetypes based on viral load and immune profiles.

Main Methods:

• Construction of a parsimonious mechanistic mathematical model of the immune response to SARS-CoV-2 infection.
• Calibration of the model using initial viral load and key immune parameters.
• Analysis of model outputs to identify distinct viral load and immune response trajectories.

Main Results:

• The model successfully generated four distinct "patient archetypes" representing different trajectories of viral load, immune response, and disease.
• These model-generated archetypes showed temporal dynamics consistent with clinical data from hospitalized COVID-19 patients.
• The model accurately accounted for the impact of corticosteroid therapy on disease progression.

Conclusions:

• The mathematical model provides a framework for understanding the immune mechanisms underlying COVID-19 disease spectrum.
• Vaccine-induced neutralizing antibodies and cellular memory are predicted to be protective against severe COVID-19.
• The generalizable modeling approach can be applied to analyze immune responses in other viral infections.