tetano
Editor, Senior Moderator
Sci Adv
. 2023 Nov 3;9(44):eabp9185.
doi: 10.1126/sciadv.abp9185. Epub 2023 Nov 3. Phylogenetic identification of influenza virus candidates for seasonal vaccines
Maryam Hayati[SUP] 1 [/SUP], Benjamin Sobkowiak[SUP] 2 [/SUP], Jessica E Stockdale[SUP] 2 [/SUP], Caroline Colijn[SUP] 2 [/SUP]
Affiliations
The seasonal influenza (flu) vaccine is designed to protect against those influenza viruses predicted to circulate during the upcoming flu season, but identifying which viruses are likely to circulate is challenging. We use features from phylogenetic trees reconstructed from hemagglutinin (HA) and neuraminidase (NA) sequences, together with a support vector machine, to predict future circulation. We obtain accuracies of 0.75 to 0.89 (AUC 0.83 to 0.91) over 2016-2020. We explore ways to select potential candidates for a seasonal vaccine and find that the machine learning model has a moderate ability to select strains that are close to future populations. However, consensus sequences among the most recent 3 years also do well at this task. We identify similar candidate strains to those proposed by the World Health Organization, suggesting that this approach can help inform vaccine strain selection.
. 2023 Nov 3;9(44):eabp9185.
doi: 10.1126/sciadv.abp9185. Epub 2023 Nov 3. Phylogenetic identification of influenza virus candidates for seasonal vaccines
Maryam Hayati[SUP] 1 [/SUP], Benjamin Sobkowiak[SUP] 2 [/SUP], Jessica E Stockdale[SUP] 2 [/SUP], Caroline Colijn[SUP] 2 [/SUP]
Affiliations
- PMID: 37922357
- DOI: 10.1126/sciadv.abp9185
The seasonal influenza (flu) vaccine is designed to protect against those influenza viruses predicted to circulate during the upcoming flu season, but identifying which viruses are likely to circulate is challenging. We use features from phylogenetic trees reconstructed from hemagglutinin (HA) and neuraminidase (NA) sequences, together with a support vector machine, to predict future circulation. We obtain accuracies of 0.75 to 0.89 (AUC 0.83 to 0.91) over 2016-2020. We explore ways to select potential candidates for a seasonal vaccine and find that the machine learning model has a moderate ability to select strains that are close to future populations. However, consensus sequences among the most recent 3 years also do well at this task. We identify similar candidate strains to those proposed by the World Health Organization, suggesting that this approach can help inform vaccine strain selection.