Modeling SARS-CoV-2 Infection Dynamics: Insights into Viral Clearance and Immune Synergy.

Abstract

Understanding the mechanisms of interaction between SARS-CoV-2 infection and the immune system is crucial for developing effective treatment strategies against COVID-19. In this paper, a mathematical model is formulated to investigate the interactions among SARS-CoV-2 infection, cellular immunity, and humoral immunity. Clinical data from eight asymptomatic or mild COVID-19 patients in Munich are used to fit the model, and the dynamics of natural killer (NK) cells, cytotoxic T lymphocytes (CTLs), B cells, and antibodies are further explored using the average of the best-fitting parameter values. Subsequently, the impact of NK cells, CTLs, B cells, and antibodies on SARS-CoV-2 infection is numerically investigated. The results indicate that (i) the synergy of NK cells, CTLs, and antibodies leads to a rapid decrease in the viral load during SARS-CoV-2 infection; (ii) antibodies play a crucial role compared to other immune mechanisms, and enhancing B cell stimulation may be more effective in clearing the virus from the lungs; (iii) in terms of cytotoxic effects, CTLs are stronger and more sustained than NK cells. Furthermore, the existence and local stability of the model's equilibria are fully classified, and complex dynamics of the model are further investigated using bifurcation theory, revealing multistability phenomena, including multiple attractors and periodic solutions. These findings suggest potential uncertainty and diversity in SARS-CoV-2 infection outcomes.