Clarifying dominant factors determining the immune heterogeneity from non-survivors to survivors is crucial for developing therapeutics and vaccines against COVID-19. The main difficulty is quantitatively analysing the multi-level clinical data, including viral dynamics, immune response and tissue damages. Here, we adopt a top-down modelling approach to quantify key functional aspects and their dynamical interplay in the battle between the virus and the immune system, yielding an accurate description of real-time clinical data involving hundreds of patients for the first time. The quantification of antiviral responses gives that, compared to antibodies, T cells play a more dominant role in virus clearance, especially for mild patients (96.5%). Moreover, the anti-inflammatory responses, namely the cytokine inhibition and tissue repair rates, also positively correlate with T cell number and are significantly suppressed in non-survivors. Simulations show that the lack of T cells can lead to more significant inflammation, proposing an explanation for the monotonic increase of COVID-19 mortality with age and higher mortality for males. We propose that T cells play a crucial role in the immunity against COVID-19, which provides a new direction–improvement of T cell number for advancing current prevention and treatment.
【저자키워드】 COVID-19, T cell, mathematical model, Immunology and inflammation, 【초록키워드】 Treatment, antibodies, Inflammation, Vaccine, immune response, Mortality, Immunity, cytokine, immune system, virus, heterogeneity, immune, Simulation, Patient, viral dynamics, age, quantification, tissue repair, antiviral response, COVID-19 mortality, Non-survivors, Factor, tissue, Clinical data, virus clearance, survivor, anti-inflammatory responses, dominant, approach, males, non-survivor, lack, significantly, functional, provide, suppressed, Clarifying, mild patient, yielding, 【제목키워드】 Immunity, Antiviral, Anti-inflammation, death, Critical,