Genotype networks drive oscillating endemicity and epidemic trajectories in viral evolution

TL;DR

This paper introduces an eco-evolutionary model showing how the structure of viral genotype networks influences epidemic patterns and endemic stability, integrating genomic data for better prediction of viral evolution and outbreaks.

Contribution

It presents a novel framework linking genotype network topology to epidemic dynamics, enabling better understanding and prediction of viral evolution and outbreaks.

Findings

Network topology can induce endemic or epidemic states.

Genotype network structure predicts emergence times of viral haplotypes.

Model integrates real genomic data for epidemic forecasting.

Abstract

Rapidly evolving viruses use antigenic drift as a key mechanism to evade host immunity and persist in real populations. While traditional models of antigenic drift and epidemic spread rely on low-dimensional antigenic spaces, genomic surveillance data reveal that viral evolution produces complex antigenic genotype networks with hierarchical modular structures. In this study, we present an eco-evolutionary framework in which viral evolution and population immunity dynamics are shaped by the structure of antigenic genotype networks. Using synthetic networks, we demonstrate that network topology alone can drive transitions between stable endemic states and recurrent seasonal epidemics. Furthermore, our results show how the integration of the genotype network of the H3N2 influenza in our model allows for estimating the emergence times of various haplotypes resulting from its evolution. Our findings underscore the critical role of the topology of genotype networks in shaping epidemic behavior and, besides, provide a robust framework for integrating real-world genomic data into predictive epidemic models.