Mathematical Modeling of Immune Response to Protein Subunit COVID-19 Vaccines
This research presents a mathematical model to investigate the long-term immunity conferred by two doses of a spike glycoprotein-clamp vaccine with a 45μg MF59 adjuvant against COVID-19. Through the application of non-linear mixed modeling techniques, we capture the dynamic interactions within the immune system and provide insightful predictions regarding vaccine outcome. Our findings highlight the importance of achieving a balanced immune response between the TH1 and TH2 arms. By conducting a sensitivity analysis, we identify the specific model parameters that have the greatest impact on the peak magnitude of the immune response, thus influencing the strength of the acquired immunity. Notably, we observe increased sensitivity to parameters associated with the TH1 component of the immune response. This study, which combines mathematical modeling and empirical data, offers a quantitative framework for understanding the intricate dynamics of the immune response to protein subunit COVID-19 vaccines. The insights gained from this research contribute to the optimization of vaccination strategies and aid in the development of more effective approaches to combat COVID-19.