Understanding antibody magnitude and durability following vaccination against SARS-CoV-2

Quiyana M Murphy; George K Lewis; Mohammad M Sajadi; Jonathan E Forde; Stanca M Ciupe

doi:10.1016/j.mbs.2024.109274

Understanding antibody magnitude and durability following vaccination against SARS-CoV-2

Math Biosci. 2024 Aug 30:376:109274. doi: 10.1016/j.mbs.2024.109274. Online ahead of print.

Authors

Quiyana M Murphy¹, George K Lewis², Mohammad M Sajadi², Jonathan E Forde³, Stanca M Ciupe⁴

Affiliations

¹ Department of Mathematics, Virginia Polytechnic Institute and State University, 225 Stanger Street, Blacksburg, 24060, VA, USA; Virginia Tech Center for the Mathematics of Biosystems, Virginia Tech, Blacksburg, VA, USA.
² Institute of Human Virology, University of Maryland School of Medicine, Baltimore, MD, USA.
³ Department of Mathematics and Computer Sciences, Hobart and William Smith Colleges, Geneva, NY, USA.
⁴ Department of Mathematics, Virginia Polytechnic Institute and State University, 225 Stanger Street, Blacksburg, 24060, VA, USA; Virginia Tech Center for the Mathematics of Biosystems, Virginia Tech, Blacksburg, VA, USA. Electronic address: stanca@vt.edu.

PMID: 39218212
DOI: 10.1016/j.mbs.2024.109274

Abstract

Vaccination against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) results in transient antibody response against the spike protein. The individual immune status at the time of vaccination influences the response. Using mathematical models of antibody decay, we determined the dynamics of serum immunoglobulin G (IgG) and serum immunoglobulin A (IgA) over time. Data fitting to longitudinal IgG and IgA titers was used to quantify differences in antibody magnitude and antibody duration among infection-naïve and infection-positive vaccinees. We found that prior infections result in more durable serum IgG and serum IgA responses, with prior symptomatic infections resulting in the most durable serum IgG response and prior asymptomatic infections resulting in the most durable serum IgA response. These findings can guide vaccine boosting schedules.

Keywords: Mathematical modeling; SARS-CoV-2 infection; SARS-CoV-2 vaccine; Serum IgA; Serum IgG.