Vaccine models predict rules for updating vaccines against evolving pathogens such as SARS-CoV-2 and influenza in the context of pre-existing immunity

Currently, vaccines for SARS-CoV-2 and influenza viruses are updated if the new vaccine induces higher antibody-titers to circulating variants than current vaccines. This approach does not account for complex dynamics of how prior immunity skews recall responses to the updated vaccine. We: (i) use c...

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Published inFrontiers in immunology Vol. 13; p. 985478
Main Authors Desikan, Rajat, Linderman, Susanne L., Davis, Carl, Zarnitsyna, Veronika I., Ahmed, Hasan, Antia, Rustom
Format Journal Article
LanguageEnglish
Published Switzerland Frontiers Media S.A 03.10.2022
Subjects
COVID-19
COVID-19 - prevention & control
COVID-19 Vaccines
Humans
Immunology
Influenza A Virus, H5N1 Subtype
Influenza Vaccines
Influenza, Human - prevention & control
modeling
omicron
SARS-CoV-2
Vaccine
variants
COVID-19
SARS-CoV-2
modeling
omicron
Vaccine
variants
simulations
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Summary:Currently, vaccines for SARS-CoV-2 and influenza viruses are updated if the new vaccine induces higher antibody-titers to circulating variants than current vaccines. This approach does not account for complex dynamics of how prior immunity skews recall responses to the updated vaccine. We: (i) use computational models to mechanistically dissect how prior immunity influences recall responses; (ii) explore how this affects the rules for evaluating and deploying updated vaccines; and (iii) apply this to SARS-CoV-2. Our analysis of existing data suggests that there is a strong benefit to updating the current SARS-CoV-2 vaccines to match the currently circulating variants. We propose a general two-dose strategy for determining if vaccines need updating as well as for vaccinating high-risk individuals. Finally, we directly validate our model by reanalysis of earlier human H5N1 influenza vaccine studies.
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Reviewed by: Yimin Huang, Biogen Idec, United States; Juan Manuel Carreño Qurioz, Icahn School of Medicine at Mount Sinai, United States
These authors have contributed equally to this work
This article was submitted to Vaccines and Molecular Therapeutics, a section of the journal Frontiers in Immunology
Edited by: Rong Hai, University of California, Riverside, United States
ISSN:1664-3224
1664-3224
DOI:10.3389/fimmu.2022.985478