Multi-strain SIS dynamics with coinfection under host population structure

TL;DR

This paper develops an analytical framework for understanding multi-strain coinfection dynamics in structured host populations, linking strain interactions, host traits, and population structure to predict coexistence and persistence.

Contribution

It introduces a general SIS model for multiple strains in structured hosts, deriving a global replicator equation that predicts strain coexistence based on invasion fitness.

Findings

Explicit coexistence conditions derived from invasion fitness.

Model links host population structure with strain interaction dynamics.

Framework applicable to various host-pathogen systems.

Abstract

Coinfection phenomena are common in nature, yet there is a lack of analytical approaches for coinfection systems with a high number of circulating and interacting strains. In this paper, we investigated a coinfection SIS framework applied to N strains, co-circulating in a structured host population. Adopting a general formulation for fixed host classes, defined by arbitrary epidemiological traits such as class-specific transmission rates, susceptibilities, clearance rates, etc., our model can be easily applied in different frameworks: for example, when different host species share the same pathogen, in classes of vaccinated or non-vaccinated hosts, or even in classes of hosts defined by the number of contacts. Using the strain similarity assumption, we identify the fast and slow variables of the epidemiological dynamics on the host population, linking neutral and non-neutral strain dynamics, and deriving a global replicator equation. This global replicator equation allows to explicitly predict coexistence dynamics from mutual invasibility coefficients among strains. The derived global pairwise invasion fitness matrix contains explicit traces of the underlying host population structure, and of its entanglement with the strain interaction and trait landscape. Our work thus enables a more comprehensive study and efficient simulation of multi-strain dynamics in endemic ecosystems, paving the way to deeper understanding of global persistence and selection forces, jointly shaped by pathogen and host diversity.