Quantifying antibody dynamics of severe and non-severe patients with COVID-19

Abstract

COVID-19 pandemic is a major public health threat with unanswered questions regarding the role of the immune system in the severity level of the disease. In this paper, based on antibody kinetic data of patients with different disease severity, topological data analysis highlights clear differences in the shape of antibody dynamics between three groups of patients, which were non-severe, severe, and one intermediate case of severity. Subsequently, different mathematical models were developed to quantify the dynamics between the different severity groups. The best model was the one with the lowest media value of Akaike Information Criterion for all groups of patients. Although it has been reported high IgG level in severe patients, our findings suggest that IgG antibodies in severe patients may be less effective than non-severe patients due to early B cell production and early activation of the seroconversion process from IgM to IgG antibody.

A Computational Framework for Exploring SARS-CoV-2 Pharmacodynamic Dose and Timing Regimes

“…Jenner et al explore patient-specific outcomes using within-host models to describe individual patients' predisposition to produce infected cell-derived interferon [15]. Ordonez-Jimenez et al describe patient-specific reactions as a function of initial infection severity [16]. Finally, it must be noted that pharmacodynamic models are subject to limitations imposed by modeling choices.…”