Curvature-Weighted Contact Networks: Spectral Reduction and Global Stability in a Markovian SIR Model

TL;DR

This paper introduces a novel network-based SIR epidemic model that incorporates geometric curvature into contact networks, providing insights into how structural features influence epidemic thresholds and stability.

Contribution

It develops a curvature-weighted contact matrix framework that generalizes classical models by integrating geometric properties, and proves stability results based on spectral analysis.

Findings

Curvature acts as a geometric regularizer, reducing spectral radii.

The model establishes thresholds for disease extinction and persistence.

Geometric features influence long-term epidemic dynamics.

Abstract

We propose a new network-based SIR epidemic model in which transmission is modulated by a curvature-weighted contact matrix that encodes structural and geometric features of the underlying graph. The formulation encompasses both adjacency-driven and Markovian mixing, allowing heterogeneous interactions to be shaped by curvature-sensitive topological properties. We prove that the basic reproduction number satisfies \[ R_0=\frac{\beta}{\gamma}\lambda_{\max}(M), \] where $M$ is the curvature-weighted transmission operator. Using Perron--Frobenius theory together with linear and nonlinear Lyapunov functionals, we establish: (i) global asymptotic stability of the disease-free equilibrium when $R_0<1$, and (ii) existence and global asymptotic stability of a unique endemic equilibrium when $R_0>1$. Our results show that curvature acts as a geometric regularizer of connectivity, lowering spectral radii, raising effective epidemic thresholds, and organizing the long-term dynamics through monotone contraction toward the endemic state. This framework generalizes classical network epidemiology by integrating geometric information directly into transmission operators, providing a rigorous foundation for epidemic dynamics on structurally heterogeneous networks.