Modelling variability of the immunity build-up and waning following RNA-based vaccination
Juan Magalang, Tyll Krueger, Joerg Galle

TL;DR
This paper presents a mathematical model of RNA-based COVID-19 vaccination that explains the variability in immunity duration and suggests optimal timing for booster doses based on immune response dynamics.
Contribution
The study introduces a detailed immune response model for RNA vaccines, linking immune kinetics to protection duration and vaccination scheduling.
Findings
Protection duration peaks around 100 days for most individuals.
Protection times decrease with virus variants with mutated antibody sites.
Optimal second dose timing is about 5 weeks after the first.
Abstract
RNA-based vaccination has been broadly applied in the COVID pandemic. A characteristic of the immunization was fast waning immunity. However, the time scale of this process varied considerable for virus subtypes and among individuals. Understanding the origin of this variability is crucial in order to improve future vaccination strategies. Here, we introduce a mathematical model of RNA-based vaccination and the kinetics of the induced immune response. In the model, antigens produced following vaccination rise an immune response leading to germinal center reactions and accordingly B-cell differentiation into memory B-cells and plasma cells. In a negative feedback loop, the antibodies synthesized by newly specified plasma cells shut down the germinal center reaction as well as antigen-induced differentiation of memory B-cell into plasma cells. This limits the build-up of long-lasting…
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Taxonomy
TopicsSARS-CoV-2 and COVID-19 Research · vaccines and immunoinformatics approaches · Immunotherapy and Immune Responses
