Abstract

The lipid nanoparticle (LNP)-formulated mRNA vaccines BNT162b2 and mRNA-1273 are a widely adopted multi vaccination public health strategy to manage the COVID-19 pandemic. Clinical trial data has described the immunogenicity of the vaccine, albeit within a limited study time frame. Here, we use a within-host mathematical model for LNP-formulated mRNA vaccines, informed by available clinical trial data from 2020 to September 2021, to project a longer term understanding of immunity as a function of vaccine type, dosage amount, age, and sex. We estimate that two standard doses of either mRNA-1273 or BNT162b2, with dosage times separated by the company-mandated intervals, results in individuals losing more than 99% humoral immunity relative to peak immunity by 8 months following the second dose. We predict that within an 8 month period following dose two (corresponding to the original CDC time-frame for administration of a third dose), there exists a period of time longer than 1 month where an individual has lost more than 99% humoral immunity relative to peak immunity, regardless of which vaccine was administered. We further find that age has a strong influence in maintaining humoral immunity; by 8 months following dose two we predict that individuals aged 18–55 have a four-fold humoral advantage compared to aged 56–70 and 70+ individuals. We find that sex has little effect on the immune response and long-term IgG counts. Finally, we find that humoral immunity generated from two low doses of mRNA-1273 decays at a substantially slower rate relative to peak immunity gained compared to two standard doses of either mRNA-1273 or BNT162b2. Our predictions highlight the importance of the recommended third booster dose in order to maintain elevated levels of antibodies.

Details

Title
Long-term durability of immune responses to the BNT162b2 and mRNA-1273 vaccines based on dosage, age and sex
Author
Korosec, Chapin S. 1 ; Farhang-Sardroodi, Suzan 2 ; Dick, David W. 1 ; Gholami, Sameneh 1 ; Ghaemi, Mohammad Sajjad 3 ; Moyles, Iain R. 1 ; Craig, Morgan 4 ; Ooi, Hsu Kiang 3 ; Heffernan, Jane M. 1 

 York University, Modelling Infection and Immunity Lab, Mathematics and Statistics, Toronto, Canada (GRID:grid.21100.32) (ISNI:0000 0004 1936 9430); York University, Centre for Disease Modelling, Mathematics and Statistics, Toronto, Canada (GRID:grid.21100.32) (ISNI:0000 0004 1936 9430) 
 York University, Centre for Disease Modelling, Mathematics and Statistics, Toronto, Canada (GRID:grid.21100.32) (ISNI:0000 0004 1936 9430); University of Manitoba, Department of Mathematics, Winnipeg, Canada (GRID:grid.21613.37) (ISNI:0000 0004 1936 9609) 
 National Research Council Canada, Digital Technologies Research Centre, Toronto, Canada (GRID:grid.24433.32) (ISNI:0000 0004 0449 7958) 
 Université de Montréal & Sainte-Justine University Hospital Research Centre, Department of Mathematics and Statistics, Montréal, Canada (GRID:grid.14848.31) (ISNI:0000 0001 2292 3357) 
Publication year
2022
Publication date
2022
Publisher
Nature Publishing Group
e-ISSN
20452322
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
2748053441
Copyright
© The Author(s) 2022. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.