In silico prediction of influenza vaccine effectiveness by sequence analysis. Issue 7 (12th February 2021)

• Record Type: Journal Article
• Authors: Cao, Lirong; Lou, Jingzhi; Zhao, Shi; Chan, Renee W.Y.; Chan, Martin; Wu, William K.K.; Chong, Marc Ka Chun; Zee, Benny Chung-Ying; Yeoh, Eng Kiong; Wong, Samuel Yeung-Shan; Chan, Paul K.S.; Wang, Maggie Haitian
• Is Part Of: Vaccine. Volume 39:Issue 7(2021)
• Page Start: 1030
• Page End: 1034
• Publication Date: 2021-02-12
• Subjects: Influenza -- Vaccine effectiveness -- Real-time estimation; Vaccines -- Periodicals; 615.372
• DOI: 10.1016/j.vaccine.2021.01.006
• Languages: English
• ISSNs: 0264-410X

Abstract

Abstract: The effectiveness of seasonal influenza vaccines varies with the matching of vaccine strains to circulating strains. Based on the genetic distance of hemagglutinin and neuraminidase gene of the influenza viruses to vaccine strains, we statistically quantified the relationship between the genetic mismatch and vaccine effectiveness (VE) for influenza A/H1N1pdm09, A/H3N2 and B. We also proposed a systematic approach to integrate multiple genes and influenza types for overall VE estimation. Evident linear relationships were identified and validated in independent data. The modelling framework may enable in silico prediction for VE on a real-time basis and inform the influenza vaccine selection strategy.