ABSTRACT
ObjectiveSerum antibody levels have been linked to immune protection in SARS-CoV-2 infection. After exposure to the virus and/or vaccination there is an increase in serum antibody titers followed by progressive non-linear waning of antibody levels. Our aim was to find out if this waning of antibody titers would adjust to a mathematical model. MethodsWe studied serum anti-RBD (receptor binding domain) IgG antibody titers over a ten-month period in a cohort of 54 health-care workers who were either never infected with SARS-CoV-2 (naive, nHCWs group, n = 27) or previously infected (experienced, eHCWs group, n = 27) with the virus after the second dose of the BNT162b2 mRNA vaccine. We have selected a risk threshold of 1000 UA/ml anti-RBD Ab titer for symptomatic infection based on the upper titer threshold for those volunteers who suffered infection prior to the omicron outbreak. Two mathematical models, exponential and potential, were used to quantify antibody waning kinetics and the relative quality of the goodness of fit to the data between both models was compared using the Akaike Information Criterion. ResultsWe found that the waning of anti-RBD IgG antibody levels adjusted significantly to both exponential and potential models in all participants from both the naive and experienced groups. Moreover, the waning slopes were significantly more pronounced for the naive when compared to the experienced health-care workers. In the nHCWs group, titers would descend below this 1000-units threshold at a median of 210.6 days (IQ range: 74,2). However, for the eHCWs group, the risk threshold would be reached at 440.0 days (IQ range: 135,2) post-vaccination. ConclusionsThe goodness of the fit of the anti-RBD IgG antibody waning would allow us to predict when the antibody titers would fall below an established threshold in both naive and previously infected subjects.
