The influence of a transport process on the epidemic threshold

TL;DR

This paper investigates how different transport processes influence epidemic thresholds in multiplex networks, revealing that transport generally lowers thresholds but non-local dynamics can raise them, with validation on real transport data.

Contribution

It introduces a multiplex network model with dynamic transport layers and analyzes how various transport mechanisms affect epidemic thresholds, including realistic human mobility patterns.

Findings

Transport lowers epidemic thresholds due to additional connections.

Non-local transport dynamics can increase the epidemic threshold.

Model validation on Munich U-Bahn network confirms theoretical predictions.

Abstract

By generating transient encounters between individuals beyond their immediate social environment, transport can have a profound impact on the spreading of an epidemic. In this work, we consider epidemic dynamics in the presence of the transport process that gives rise to a multiplex network model. In addition to a static layer, the (multiplex) epidemic network consists of a second dynamic layer in which any two individuals are connected for the time they occupy the same site during a random walk they perform on a separate transport network. We develop a mean-field description of the stochastic network model and study the influence the transport process has on the epidemic threshold. We show that any transport process generally lowers the epidemic threshold because of the additional connections it generates. In contrast, considering also random walks of fractional order that in some sense are a more realistic model of human mobility, we find that these non-local transport dynamics raise the epidemic threshold in comparison to a classical local random walk. We also test our model on a realistic transport network (the Munich U-Bahn network), and carefully compare mean-field solutions with stochastic trajectories in a range of scenarios.