Immunity-induced criticality of the genotype network of influenza A (H3N2) hemagglutinin

TL;DR

This paper investigates the genotype network structure of influenza A (H3N2) hemagglutinin, revealing critical-like behavior driven by mutation and immunity, which influences epidemic dynamics and pathogen evolution modeling.

Contribution

It introduces the concept of criticality in influenza genotype networks and links network structure to epidemic dynamics, advancing understanding of pathogen evolution.

Findings

Genotype networks exhibit heavy-tailed distributions of module sizes and connectivity.

Network structure influences the dynamics of multistrain epidemic models.

Influenza's epidemic behavior is linked to self-organized criticality in its genotype network.

Abstract

Seasonal influenza kills hundreds of thousands every year, with multiple constantly-changing strains in circulation at any given time. A high mutation rate enables the influenza virus to evade recognition by the human immune system, including immunity acquired through past infection and vaccination. Here, we capture the genetic similarity of influenza strains and their evolutionary dynamics with genotype networks. We show that the genotype networks of influenza A (H3N2) hemagglutinin are characterized by heavy-tailed distributions of module sizes and connectivity, suggesting critical-like behavior. We argue that: (i) genotype networks are driven by mutation and host immunity to explore a subspace of networks predictable in structure, and (ii) genotype networks provide an underlying structure necessary to capture the rich dynamics of multistrain epidemic models. In particular, inclusion of strain-transcending immunity in epidemic models is dependent upon the structure of an underlying genotype network. This interplay suggests a self-organized criticality where the epidemic dynamics of influenza locates critical-like regions of its genotype network. We conclude that this interplay between disease dynamics and network structure might be key for future network analysis of pathogen evolution and realistic multistrain epidemic models.