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Prediction of influenza vaccine effectiveness for the influenza season 2017/18 in the US

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 antige...

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Detalles Bibliográficos
Autores principales: Paessler, Slobodan, Veljkovic, Veljko
Formato: Online Artículo Texto
Lenguaje:English
Publicado: F1000 Research Limited 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5795273/
https://www.ncbi.nlm.nih.gov/pubmed/29535862
http://dx.doi.org/10.12688/f1000research.13198.1
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author Paessler, Slobodan
Veljkovic, Veljko
author_facet Paessler, Slobodan
Veljkovic, Veljko
author_sort Paessler, Slobodan
collection PubMed
description 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.
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spelling pubmed-57952732018-03-12 Prediction of influenza vaccine effectiveness for the influenza season 2017/18 in the US Paessler, Slobodan Veljkovic, Veljko F1000Res Research Note 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. F1000 Research Limited 2017-11-29 /pmc/articles/PMC5795273/ /pubmed/29535862 http://dx.doi.org/10.12688/f1000research.13198.1 Text en Copyright: © 2017 Paessler S and Veljkovic V http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution Licence, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Note
Paessler, Slobodan
Veljkovic, Veljko
Prediction of influenza vaccine effectiveness for the influenza season 2017/18 in the US
title Prediction of influenza vaccine effectiveness for the influenza season 2017/18 in the US
title_full Prediction of influenza vaccine effectiveness for the influenza season 2017/18 in the US
title_fullStr Prediction of influenza vaccine effectiveness for the influenza season 2017/18 in the US
title_full_unstemmed Prediction of influenza vaccine effectiveness for the influenza season 2017/18 in the US
title_short Prediction of influenza vaccine effectiveness for the influenza season 2017/18 in the US
title_sort prediction of influenza vaccine effectiveness for the influenza season 2017/18 in the us
topic Research Note
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5795273/
https://www.ncbi.nlm.nih.gov/pubmed/29535862
http://dx.doi.org/10.12688/f1000research.13198.1
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