Testing for genetic mutation of seasonal influenza virus

Vera Liu and Stephen Walker

Journal of Applied Statistics, 2023, vol. 50, issue 1, 1-18

Abstract: Influenza virus strains undergo genetic mutations every year and these changes in genetic makeup pose difficulties for effective vaccine selection. To better understand the problem it is important to statistically quantify the amount of genetic change between circulating strains from different years. In this paper, we propose the nonparametric crossmatch test applied to phylogenetic trees to assess the level of discrepancy between circulating flu virus strains between two years; the viruses being represented by a phylogenetic tree. The crossmatch test has advantages compared to parametric tests in that it preserves more information in the data. The outcome of the test would indicate whether the circulating influenza virus has mutated sufficiently in the past year to be considered as a new population of virus, suggesting the need to consider a new vaccine. We validate the test on simulated phylogenetic tree samples with varying branch lengths, as well as with publicly available virus sequence data from the ‘Global Initiative on Sharing All Influenza Data’ (GISAID: https://www.gisaid.org/)

Date: 2023
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DOI: 10.1080/02664763.2021.1978955

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