A statistical analysis of antigenic similarity among influenza A (H3N2) viruses

An accurate assessment of antigenic similarity between influenza viruses is important for vaccine strain recommendations and influenza surveillance. Due to the mechanisms that result in frequent changes in the antigenicities of strains, it is desirable to obtain an antigenic similarity measure that...

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Autor principal:	Emmanuel S. Adabor
Formato:	article
Lenguaje:	EN
Publicado:	Elsevier 2021
Materias:	Influenza virus Antigenic similarity Vaccine effectiveness Machine learning Statistical model Science (General) Q1-390 Social sciences (General) H1-99
Acceso en línea:	https://doaj.org/article/683e1065d8cf48e5bf48ed9b19cf9da8
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spelling	oai:doaj.org-article:683e1065d8cf48e5bf48ed9b19cf9da82021-12-02T05:02:57ZA statistical analysis of antigenic similarity among influenza A (H3N2) viruses2405-844010.1016/j.heliyon.2021.e08384https://doaj.org/article/683e1065d8cf48e5bf48ed9b19cf9da82021-11-01T00:00:00Zhttp://www.sciencedirect.com/science/article/pii/S2405844021024877https://doaj.org/toc/2405-8440An accurate assessment of antigenic similarity between influenza viruses is important for vaccine strain recommendations and influenza surveillance. Due to the mechanisms that result in frequent changes in the antigenicities of strains, it is desirable to obtain an antigenic similarity measure that accounts for specific changes in strains that are of epidemiological importance in influenza. Empirically grounded statistical models best achieve this. In this study, an interpretable machine-learning model was developed using distinguishing features of antigenic variants to analyze antigenic similarity. The features comprised of cluster information, amino acid sequences located in known antigenic and receptor-binding sites of influenza A (H3N2). In order to assess validity of parameters, accuracy and relevance of model to vaccine effectiveness, the model was applied to influenza A (H3N2) viruses due to their abundant genetic data and epidemiological relevance to influenza surveillance. An application of the model revealed that all model parameters were statistically significant to determining antigenic similarity between strains. Furthermore, upon evaluating the model for predicting antigenic similarity between strains, it achieved 95% area under Receiver Operating Characteristic curve (AUC), 94% accuracy, 76% precision, 97% specificity, 68% sensitivity and a diagnostic odds ratio (DOR) of 83.19. Above all, the model was found to be strongly related to influenza vaccine effectiveness to indicate the correlation between vaccine effectiveness and antigenic similarity between vaccine and circulating strains in an epidemic. The study predicts probabilities of antigenic similarity and estimates changes in strains that lead to antigenic variants. A successful application of the methods presented in this study would complement the global efforts in influenza surveillance.Emmanuel S. AdaborElsevierarticleInfluenza virusAntigenic similarityVaccine effectivenessMachine learningStatistical modelScience (General)Q1-390Social sciences (General)H1-99ENHeliyon, Vol 7, Iss 11, Pp e08384- (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Influenza virus Antigenic similarity Vaccine effectiveness Machine learning Statistical model Science (General) Q1-390 Social sciences (General) H1-99
spellingShingle	Influenza virus Antigenic similarity Vaccine effectiveness Machine learning Statistical model Science (General) Q1-390 Social sciences (General) H1-99 Emmanuel S. Adabor A statistical analysis of antigenic similarity among influenza A (H3N2) viruses
description	An accurate assessment of antigenic similarity between influenza viruses is important for vaccine strain recommendations and influenza surveillance. Due to the mechanisms that result in frequent changes in the antigenicities of strains, it is desirable to obtain an antigenic similarity measure that accounts for specific changes in strains that are of epidemiological importance in influenza. Empirically grounded statistical models best achieve this. In this study, an interpretable machine-learning model was developed using distinguishing features of antigenic variants to analyze antigenic similarity. The features comprised of cluster information, amino acid sequences located in known antigenic and receptor-binding sites of influenza A (H3N2). In order to assess validity of parameters, accuracy and relevance of model to vaccine effectiveness, the model was applied to influenza A (H3N2) viruses due to their abundant genetic data and epidemiological relevance to influenza surveillance. An application of the model revealed that all model parameters were statistically significant to determining antigenic similarity between strains. Furthermore, upon evaluating the model for predicting antigenic similarity between strains, it achieved 95% area under Receiver Operating Characteristic curve (AUC), 94% accuracy, 76% precision, 97% specificity, 68% sensitivity and a diagnostic odds ratio (DOR) of 83.19. Above all, the model was found to be strongly related to influenza vaccine effectiveness to indicate the correlation between vaccine effectiveness and antigenic similarity between vaccine and circulating strains in an epidemic. The study predicts probabilities of antigenic similarity and estimates changes in strains that lead to antigenic variants. A successful application of the methods presented in this study would complement the global efforts in influenza surveillance.
format	article
author	Emmanuel S. Adabor
author_facet	Emmanuel S. Adabor
author_sort	Emmanuel S. Adabor
title	A statistical analysis of antigenic similarity among influenza A (H3N2) viruses
title_short	A statistical analysis of antigenic similarity among influenza A (H3N2) viruses
title_full	A statistical analysis of antigenic similarity among influenza A (H3N2) viruses
title_fullStr	A statistical analysis of antigenic similarity among influenza A (H3N2) viruses
title_full_unstemmed	A statistical analysis of antigenic similarity among influenza A (H3N2) viruses
title_sort	statistical analysis of antigenic similarity among influenza a (h3n2) viruses
publisher	Elsevier
publishDate	2021
url	https://doaj.org/article/683e1065d8cf48e5bf48ed9b19cf9da8
work_keys_str_mv	AT emmanuelsadabor astatisticalanalysisofantigenicsimilarityamonginfluenzaah3n2viruses AT emmanuelsadabor statisticalanalysisofantigenicsimilarityamonginfluenzaah3n2viruses
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A statistical analysis of antigenic similarity among influenza A (H3N2) viruses

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