Utilizing virus genomic surveillance to predict vaccine effectiveness
Jiye Kwon, Ke Li, Joshua L. Warren, Sameer Pandya, Anne M. Hahn, Yale SARS-CoV-2 Genomic Surveillance Initiative, Virginia E. Pitzer, Daniel M. Weinberger, Nathan D. Grubaugh, Joel Wertheim, Joel Wertheim, Joel Wertheim, Joel Wertheim · 2026
Read Utilizing virus genomic surveillance to predict vaccine effectiveness by Jiye Kwon, Ke Li, Joshua L. Warren, Sameer Pandya, Anne M. Hahn, Yale SARS-CoV-2 Genomic Surveillance Initiative, Virginia E. Pitzer, Daniel M. Weinberger, Nathan D. Grubaugh, Joel Wertheim, Joel Wertheim, Joel Wertheim, Joel Wertheim (2026) in the Tower of Babel, a free online library of public-domain works preserved in clean, searchable text. No sign-up. The full text is served as a plain-text file at a predictable URL, where text is present: https://antarctic-labs.com/tower-of-babel/pmc-13258143.txt.
Description
Jiye Kwon and co-authors test whether virus genome watching can forecast COVID vaccine protection in a 2026 study. More than ten thousand viral genomes from Connecticut cases between 2021 and 2024 were compared with each vaccine formula's spike sequence. A Bayesian weekly model joined those distances with over a million test results.
Greater amino acid distance in the spike gene tracked lower vaccine effectiveness over time. Sharp drops matched new variant arrivals, while slow drift within variants eroded protection gradually. Each block of added spike changes corresponded to a measurable protection loss in the fitted model. Public health teams gain a forecasting frame: sequence surveillance could warn of waning protection before outcome studies catch up. Updating vaccine formulas might then start earlier, guided by genomes already being collected for other reasons.
Catalog record
| CREATOR | Jiye Kwon, Ke Li, Joshua L. Warren, Sameer Pandya, Anne M. Hahn, Yale SARS-CoV-2 Genomic Surveillance Initiative, Virginia E. Pitzer, Daniel M. Weinberger, Nathan D. Grubaugh, Joel Wertheim, Joel Wertheim, Joel Wertheim, Joel Wertheim |
|---|---|
| YEAR | 2026 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 70 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |