Vaccination accelerates the race of influenza virus against collective immunity
- 1Sechenov Institute of Evolutionary Physiology and Biochemistry RAS, St Petersburg, Russia.
Abstract
Respiratory viruses persist in a population, because their rapid evolution allows them to escape recognition by antibodies in previously-infected individuals. To optimize parameters of an effective vaccine, an epidemiological model of a population infected with an evolving virus is coupled to a model of multi-locus evolution. Model parameters are informed from influenza virus data. The cases of overlapping and non-overlapping effect of consecutive vaccinations are considered. The results predict that vaccination will accelerate viral evolution and may even increase infection incidence, unless one of two scenarios takes place: either antibodies elicited by vaccine are more broadly neutralizing than antibodies to natural infection, or the vaccine vector lies slightly ahead of the dominant virus variant in the evolutionary trajectory. These results suggest that the probabilistic prognosis of virus evolution represents a promising strategy of vaccine improvement complementing the search for broadly-neutralizing vaccines. Author summary: Some viruses persist in a population, because their rapid evolution allows them to escape recognition by antibodies in previously-infected individuals. Developing effective vaccines against these viruses poses a challenge. To predict parameters of an effective vaccine, a mathematical model of population infected with various strains of evolving virus and keeping immune memory cells left by past viral strains is developed. The model is based on matching the standard epidemiological approach to multi-site evolution models and is informed from available data on influenza virus. Results demonstrate that vaccination will accelerate viral evolution and may even increase infection incidence, unless one of two following conditions takes place. Either the immune response to vaccine is more broadly neutralizing than that the immune response to infection, or the vaccine vector is slightly ahead of the virus on evolutionary trajectory. Given the difficulties with developing broadly-neutralizing vaccines, the “forward-neutralizing” vaccines offer a promising alternative. The approach relies on the accurate probabilistic prognosis of virus evolution from computational models and genomic data.
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BibTeX
@article{rouzine2026vaccination,
title = {{Vaccination accelerates the race of influenza virus against collective immunity}},
author = {Igor M Rouzine},
journal = {PLOS Pathogens},
year = {2026},
month = oct,
doi = {10.1371/journal.ppat.1014311},
url = {https://journals.plos.org/plospathogens/article?id=10.1371/journal.ppat.1014311},
note = {CC BY},
}
RIS
TY - JOUR
TI - Vaccination accelerates the race of influenza virus against collective immunity
AU - Igor M Rouzine
PY - 2026
DA - 2026/10/12
JO - PLOS Pathogens
DO - 10.1371/journal.ppat.1014311
UR - https://journals.plos.org/plospathogens/article?id=10.1371/journal.ppat.1014311
AB - Respiratory viruses persist in a population, because their rapid evolution allows them to escape recognition by antibodies in previously-infected individuals. To optimize parameters of an effective vaccine, an epidemiological model of a population infected with an evolving virus is coupled to a model of multi-locus evolution. Model parameters are informed from influenza virus data. The cases of overlapping and non-overlapping effect of consecutive vaccinations are considered. The results predict that vaccination will accelerate viral evolution and may even increase infection incidence, unless one of two scenarios takes place: either antibodies elicited by vaccine are more broadly neutralizing than antibodies to natural infection, or the vaccine vector lies slightly ahead of the dominant virus variant in the evolutionary trajectory. These results suggest that the probabilistic prognosis of virus evolution represents a promising strategy of vaccine improvement complementing the search for broadly-neutralizing vaccines. Author summary: Some viruses persist in a population, because their rapid evolution allows them to escape recognition by antibodies in previously-infected individuals. Developing effective vaccines against these viruses poses a challenge. To predict parameters of an effective vaccine, a mathematical model of population infected with various strains of evolving virus and keeping immune memory cells left by past viral strains is developed. The model is based on matching the standard epidemiological approach to multi-site evolution models and is informed from available data on influenza virus. Results demonstrate that vaccination will accelerate viral evolution and may even increase infection incidence, unless one of two following conditions takes place. Either the immune response to vaccine is more broadly neutralizing than that the immune response to infection, or the vaccine vector is slightly ahead of the virus on evolutionary trajectory. Given the difficulties with developing broadly-neutralizing vaccines, the “forward-neutralizing” vaccines offer a promising alternative. The approach relies on the accurate probabilistic prognosis of virus evolution from computational models and genomic data.
SN - 1553-7374
SN - 1553-7366
ER -