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

Author:

Williams Blake J M1,Ogbunugafor C Brandon1234ORCID,Althouse Benjamin M567ORCID,Hébert-Dufresne Laurent18ORCID

Affiliation:

1. Vermont Complex Systems Center, University of Vermont , Burlington, VT 05405, USA

2. Department of Ecology and Evolutionary Biology, Yale University , New Haven, CT 06511, USA

3. Santa Fe Institute , Santa Fe, NM 87501, USA

4. Public Health Modeling Unit, Yale School of Public Health , New Haven, CT 06510, USA

5. Institute for Disease Modeling, Global Health, Bill & Melinda Gates Foundation , Seattle, WA 98109, USA

6. Information School, University of Washington , Seattle, WA 98195, USA

7. Department of Biology, New Mexico State University , Las Cruces, NM 88003, USA

8. Department of Computer Science, University of Vermont , Burlington VT 05405, USA

Abstract

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 indicative of critical 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 is consistent with self-organized criticality where the epidemic dynamics of influenza locates critical 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.

Funder

National Institutes of Health

Publisher

Oxford University Press (OUP)

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