A Probabilistic Modeling Analysis of the Longitudinal Immune Response to Infection and Vaccination Across Demographic Groups and Pulmonary Symptoms

Antibody and cytokine kinetics describe the dynamic response to immune events such as infection and vaccination. These dynamics are not fully understood, and mathematical characterization may help explain variability across demographic groups and pulmonary symptoms post-acute infection. We fit time-dependent probability models to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) data to obtain distributions of longitudinal antibody response and cytokine values. To assess differences between groups, an overlap metric is applied to the modeled response curves. Our antibody models suggest significant differences between male and female populations and demonstrate deficient antibody responses of less-healthy groups such as smokers. Our cytokine models suggest that those with pulmonary symptoms post-acute infection have elevated responses over time. Further, we find that the cytokine response increases and then decays more rapidly than the antibody response. These results are consistent with clinical observations.