Abstract
The COVID-19 pandemic is a significant public health threat with unanswered questions regarding the immune system’s role in the disease’s severity level. Here, based on antibody kinetic data of severe and non-severe COVID-19 patients, topological data analysis (TDA) highlights that severity is not binary. However, there are differences in the shape of antibody responses that further classify COVID-19 patients into non-severe, severe, and intermediate cases of severity. Based on the results of TDA, different mathematical models were developed to represent the dynamics between the different severity groups. The best model was the one with the lowest average value of the Akaike Information Criterion for all groups of patients. Our results suggest that different immune mechanisms drive differences between the severity groups. Further inclusion of different components of the immune system will be central for a holistic way of tackling COVID-19.
•Antibody dynamics data of severe and non-severe COVID-19 patients•TDA uncovers differences between responders and non-responders.•ODE models were used to quantify the dynamics between patient groups.•IgG antibodies in severe patients may be less effective than in non-severe patients.