Modeling COVID-19 vaccine booster-elicited antibody response and impact of infection history-

Abstract

Takara Nishiyama, Yuichiro Miyamatsu, Hyeongki Park, Naotoshi Nakamura, ... Yoji Nagasaki
Abstract
The 3-dose COVID-19 vaccine (booster vaccination) has been offered worldwide. As booster vaccinations continue, it is important to understand the antibody dynamics elicited by booster vaccination in order to evaluate and develop vaccination needs and strategies. Here, we investigated longitudinal data by monitoring IgG antibodies against the receptor binding domain (RBD) in health care workers. We extended our previously developed mathematical model to booster vaccines and successfully fitted antibody titers over time in the absence and presence of past SARS-CoV-2 infection. Quantitative analysis using our mathematical model indicated that anti-RBD IgG titers increase to a comparable extent after booster vaccination, regardless of the presence or absence of infection, but infection history extends the duration of antibody response by 1.28 times. Such a mathematical modeling approach can be used to inform future vaccination strategies on the basis of an individual's immune history. Our simple quantitative approach can be extended to any kind of vaccination and therefore can form a basis for policy decisions regarding the distribution of booster vaccines to strengthen immunity in future pandemics.
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9 pages, 861 KiB
Open AccessArticle