Prediction of influenza vaccine effectiveness for the influenza season 2017/18 in the US [version 1; referees: 2 approved]
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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doaj-a46fae25d3424c6c83ae92f9dfde8a282020-11-25T04:04:04ZengF1000 Research LtdF1000Research2046-14022017-11-01610.12688/f1000research.13198.114319Prediction of influenza vaccine effectiveness for the influenza season 2017/18 in the US [version 1; referees: 2 approved]Slobodan Paessler0Veljko Veljkovic1Department of Pathology, Galveston National Laboratory, University of Texas Medical Branch, Galveston, TX, 77555, USABiomed Protection, Galveston, TX, 77550, USAVaccination 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.https://f1000research.com/articles/6-2067/v1Immunity to InfectionsPreventive MedicineStatistical Methodologies & Health Informatics |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Slobodan Paessler Veljko Veljkovic |
spellingShingle |
Slobodan Paessler Veljko Veljkovic Prediction of influenza vaccine effectiveness for the influenza season 2017/18 in the US [version 1; referees: 2 approved] F1000Research Immunity to Infections Preventive Medicine Statistical Methodologies & Health Informatics |
author_facet |
Slobodan Paessler Veljko Veljkovic |
author_sort |
Slobodan Paessler |
title |
Prediction of influenza vaccine effectiveness for the influenza season 2017/18 in the US [version 1; referees: 2 approved] |
title_short |
Prediction of influenza vaccine effectiveness for the influenza season 2017/18 in the US [version 1; referees: 2 approved] |
title_full |
Prediction of influenza vaccine effectiveness for the influenza season 2017/18 in the US [version 1; referees: 2 approved] |
title_fullStr |
Prediction of influenza vaccine effectiveness for the influenza season 2017/18 in the US [version 1; referees: 2 approved] |
title_full_unstemmed |
Prediction of influenza vaccine effectiveness for the influenza season 2017/18 in the US [version 1; referees: 2 approved] |
title_sort |
prediction of influenza vaccine effectiveness for the influenza season 2017/18 in the us [version 1; referees: 2 approved] |
publisher |
F1000 Research Ltd |
series |
F1000Research |
issn |
2046-1402 |
publishDate |
2017-11-01 |
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. |
topic |
Immunity to Infections Preventive Medicine Statistical Methodologies & Health Informatics |
url |
https://f1000research.com/articles/6-2067/v1 |
work_keys_str_mv |
AT slobodanpaessler predictionofinfluenzavaccineeffectivenessfortheinfluenzaseason201718intheusversion1referees2approved AT veljkoveljkovic predictionofinfluenzavaccineeffectivenessfortheinfluenzaseason201718intheusversion1referees2approved |
_version_ |
1724437920951042048 |