Abstract
Vaccination against seasonal influenza viruses is the most effective way to prevent infection. A key factor in the effectiveness of the seasonal influenza vaccine is its immunological compatibility with the circulating viruses during the season. The high evolutionary rate, antigenic shift and antigenic drift of influenza viruses, represents the main obstacle for correct prediction of the vaccine effectiveness for an upcoming flu season. Conventional structural and phylogenetic approaches for assessment of vaccine effectiveness have had a limited success in prediction of vaccine efficacy in the past. Recently, a novel bioinformatics approach for assessment of effectiveness of seasonal influenza vaccine was proposed. Here, this approach was used for prediction of the vaccine effectiveness for the influenza season 2017/18 in US.
Highlights
Influenza vaccine effectiveness (VE) can vary from year to year, and among different age and risk groups
Datasets We analyzed the hemagglutinin HA1 region of 251 human H3N2 viruses collected in Australia from July to September 2017 and 113 human H3N2 viruses collected in the US in the same period that were stored in publicly open database GISAID
We showed that informational complementarity represented with F(0.299) between circulating influenza viruses and the vaccine virus correlate with VE of seasonal flu vaccine[3]
Summary
Influenza vaccine effectiveness (VE) can vary from year to year, and among different age and risk groups. Evolution of influenza viruses between the time of vaccine selection and the beginning of the flu season (week 40 for the Northern Hemisphere) can seriously hamper VE. For this reason, in most years, the flu vaccine is 50% to 70% effective Each flu season researchers try to determine the VE adequately, but study results vary due to differences in study design, outcome(s) measured, population studied and the season in which the flu vaccine was studied. These differences make it difficult to compare the results of the several studies. The VE in the US for the period 2004–2015 determined by this Network is available
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