1. Vaccine models predict rules for updating vaccines against evolving pathogens such as SARS-CoV-2 and influenza in the context of pre-existing immunity.
- Author
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Desikan R, Linderman SL, Davis C, Zarnitsyna VI, Ahmed H, and Antia R
- Subjects
- Humans, SARS-CoV-2, COVID-19 Vaccines, Influenza Vaccines, Influenza, Human prevention & control, Influenza A Virus, H5N1 Subtype, COVID-19 prevention & control
- Abstract
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., Competing Interests: Author RD is employed by GSK. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Desikan, Linderman, Davis, Zarnitsyna, Ahmed and Antia.)
- Published
- 2022
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