Low-dimensional clustering detects incipient dominant influenza strain clusters

TL;DR

This paper presents a low-dimensional clustering method to detect emerging dominant influenza strains early, aiding vaccine development by identifying incipient clusters before they become dominant.

Contribution

The study introduces a novel clustering approach and a metric for early detection of dominant influenza strains using sequence data, demonstrated on historical H3N2 data.

Findings

Successfully identified an incipient H3N2 strain before it became dominant.

Detected the 2009 H1N1 strain early in the sequence data.

Method shows potential for improving influenza vaccine strain selection.

Abstract

Influenza has been circulating in the human population and has caused three pandemics in the last century (1918 H1N1, 1957 H2N2, 1968 H3N2). The 2009 A(H1N1) was classified by the World Health Organization (WHO) as the fourth pandemic. Influenza has a high evolution rate, which makes vaccine design challenging. We here consider an approach for early detection of new dominant strains. By clustering the 2009 A(H1N1) sequence data, we found two main clusters. We then define a metric to detect the emergence of dominant strains. We show on historical H3N2 data that this method is able to identify a cluster around an incipient dominant strain before it becomes dominant. For example, for H3N2 as of March 30, 2009, the method detects the cluster for the new A/British Columbia/RV1222/2009 strain. This strain detection tool would appear to be useful for annual influenza vaccine selection.