The large graph limit of a stochastic epidemic model on a dynamic multilayer network

TL;DR

This paper derives a system of ODEs describing the large-scale behavior of a stochastic SIR epidemic process on a dynamic multilayer network, accounting for edge activation/deactivation influenced by infection status.

Contribution

It extends existing models by incorporating dynamic multilayer networks and provides conditions linking the ODEs to pair approximation methods.

Findings

Derived a large graph limit theorem for epidemic dynamics.

Analyzed the impact of edge activation/deactivation on disease spread.

Illustrated results with a two-layer network example.

Abstract

We consider an SIR-type (Susceptible $\to$ Infected $\to$ Recovered) stochastic epidemic process with multiple modes of transmission on a contact network. The network is given by a random graph following a multilayer configuration model where edges in different layers correspond to potentially infectious contacts of different types. We assume that the graph structure evolves in response to the epidemic via activation or deactivation of edges. We derive a large graph limit theorem that gives a system of ordinary differential equations (ODEs) describing the evolution of quantities of interest, such as the proportions of infected and susceptible vertices, as the number of nodes tends to infinity. Analysis of the limiting system elucidates how the coupling of edge activation and deactivation to infection status affects disease dynamics, as illustrated by a two-layer network example with edge types corresponding to community and healthcare contacts. Our theorem extends some earlier results deriving the deterministic limit of stochastic SIR processes on static, single-layer configuration model graphs. We also describe precisely the conditions for equivalence between our limiting ODEs and the systems obtained via pair approximation, which are widely used in the epidemiological and ecological literature to approximate disease dynamics on networks. Potential applications include modeling Ebola dynamics in West Africa, which was the motivation for this study.