Seasonal influenza vaccine strain selection by quantifying viral fitness
- 1State Key Laboratory of Common Mechanism Research for Major Diseases, Suzhou Institute of Systems Medicine, Chinese Academy of Medical Sciences & Peking Union M
Abstract
The long-standing challenge of seasonal influenza vaccines providing poor protection is attributed to the rapid and continuous evolution of the virus. In theory, the recommendation of vaccine strain is to predict the dominant strain with best fitness in the upcoming season. Here, we develop FutureFlu, a biologically-grounded framework that quantifies the fitness score of influenza variants in upcoming seasons, by integrating metrics on three levels: molecular genetic divergence, individual immune escape, and population-scale transmission dynamics. The fitness scores of influenza variants present strong positive correlation with their actual observed frequencies in the next seasons. Validation across 24 seasons of three influenza subtypes shows FutureFlu recommends antigenically matched vaccine strains more frequently than annual recommendations, particularly for challenging subtypes: 83.3% versus 45.8% seasons for A/H3N2, and 75.0% versus 33.3% seasons for B/Victoria. Furthermore, FutureFlu significantly outperforms the currently used methods in vaccine strain selection whether or not there is available antigenic data from hemagglutination inhibition (HI) assay, which provides a valuable supplement for WHO vaccine recommendation. To support global public health implementation, an open online platform (futureflu.com.cn) has been established to offer real-time viral fitness predictions and vaccine recommendations.
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BibTeX
@misc{qin2026seasonal,
title = {{Seasonal influenza vaccine strain selection by quantifying viral fitness}},
author = {L. Qin and M. Zhang and X. Liu and X. Ding and Q. Li and L. Yang and Y. Qi and J. Liu and H. Zhou and Z. Li and W. Xie and Z. Li and Y. Ma and J. Yang and H. Wang and J. Wang and T. Jiang and D. Wang and Y. Wang and A. Wu},
howpublished = {bioRxiv},
year = {2026},
month = sep,
doi = {10.64898/2026.09.15.751745},
url = {https://www.biorxiv.org/content/10.64898/2026.09.15.751745v1},
note = {CC BY-NC-ND},
}
RIS
TY - UNPB
TI - Seasonal influenza vaccine strain selection by quantifying viral fitness
AU - L. Qin
AU - M. Zhang
AU - X. Liu
AU - X. Ding
AU - Q. Li
AU - L. Yang
AU - Y. Qi
AU - J. Liu
AU - H. Zhou
AU - Z. Li
AU - W. Xie
AU - Z. Li
AU - Y. Ma
AU - J. Yang
AU - H. Wang
AU - J. Wang
AU - T. Jiang
AU - D. Wang
AU - Y. Wang
AU - A. Wu
PY - 2026
DA - 2026/09/15
PB - bioRxiv
DO - 10.64898/2026.09.15.751745
UR - https://www.biorxiv.org/content/10.64898/2026.09.15.751745v1
AB - The long-standing challenge of seasonal influenza vaccines providing poor protection is attributed to the rapid and continuous evolution of the virus. In theory, the recommendation of vaccine strain is to predict the dominant strain with best fitness in the upcoming season. Here, we develop FutureFlu, a biologically-grounded framework that quantifies the fitness score of influenza variants in upcoming seasons, by integrating metrics on three levels: molecular genetic divergence, individual immune escape, and population-scale transmission dynamics. The fitness scores of influenza variants present strong positive correlation with their actual observed frequencies in the next seasons. Validation across 24 seasons of three influenza subtypes shows FutureFlu recommends antigenically matched vaccine strains more frequently than annual recommendations, particularly for challenging subtypes: 83.3% versus 45.8% seasons for A/H3N2, and 75.0% versus 33.3% seasons for B/Victoria. Furthermore, FutureFlu significantly outperforms the currently used methods in vaccine strain selection whether or not there is available antigenic data from hemagglutination inhibition (HI) assay, which provides a valuable supplement for WHO vaccine recommendation. To support global public health implementation, an open online platform (futureflu.com.cn) has been established to offer real-time viral fitness predictions and vaccine recommendations.
ER -