Evolution of 2014/15 H3N2 Influenza Viruses Circulating in US: Consequences for Vaccine Effectiveness and Possible New Pandemic

Overview

Journal Front Microbiol

Specialty Microbiology

Date 2016 Jan 7

PMID 26733989

Citations 10

Authors

Veljko Veljkovic

Slobodan Paessler

Sanja Glisic

Jelena Prljic

Vladimir R Perovic

Nevena Veljkovic

Matthew Scotch

Affiliations

Soon will be listed here.

Abstract

A key factor in the effectiveness of the seasonal influenza vaccine is its immunological compatibility with the circulating viruses during the season. Here we propose a new bioinformatics approach for analysis of influenza viruses which could be used as an efficient tool for selection of vaccine viruses, assessment of the effectiveness of seasonal influenza vaccines, and prediction of the epidemic/pandemic potential of novel influenza viruses.

Citing Articles

Prediction of Antigenic Distance in Influenza A Using Attribute Network Embedding.

Peng F, Xia Y, Li W Viruses. 2023; 15(7).

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Predicting Antigenic Distance from Genetic Data for PRRSV-Type 1: Applications of Machine Learning.

Makau D, Prieto C, Martinez-Lobo F, Paploski I, VanderWaal K Microbiol Spectr. 2022; 11(1):e0408522.

PMID: 36511691 PMC: 9927307. DOI: 10.1128/spectrum.04085-22.

Antigenicity prediction and vaccine recommendation of human influenza virus A (H3N2) using convolutional neural networks.

Lee E, Tian H, Nakaya H Hum Vaccin Immunother. 2020; 16(11):2690-2708.

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Evolution of H5-Type Avian Influenza A Virus Towards Mammalian Tropism in Egypt, 2014 to 2015.

Mahmoud S, Mostafa A, El-Shesheny R, Seddik M, Khalafalla G, Shehata M Pathogens. 2019; 8(4).

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Optimizing the impact of low-efficacy influenza vaccines.

Sah P, Medlock J, Fitzpatrick M, Singer B, Galvani A Proc Natl Acad Sci U S A. 2018; 115(20):5151-5156.

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