Traffic-driven Epidemic Spreading in Finite-size Scale-Free Networks

TL;DR

This paper investigates how traffic flow conditions influence epidemic spreading in finite-size scale-free networks, revealing that epidemic thresholds depend on flow parameters and can be affected by congestion and delivery capabilities.

Contribution

It introduces a traffic-driven perspective to epidemic spreading, showing that flow conditions determine epidemic thresholds in scale-free networks, contrasting with traditional reaction-based models.

Findings

Epidemic threshold depends on traffic flow moments.

Bounded delivery causes congestion, slowing disease spread.

Threshold decreases with increasing flow.

Abstract

The study of complex networks sheds light on the relation between the structure and function of complex systems. One remarkable result is the absence of an epidemic threshold in infinite-size scale-free networks, which implies that any infection will perpetually propagate regardless of the spreading rate. The vast majority of current theoretical approaches assumes that infections are transmitted as a reaction process from nodes to all neighbors. Here we adopt a different perspective and show that the epidemic incidence is shaped by traffic flow conditions. Specifically, we consider the scenario in which epidemic pathways are defined and driven by flows. Through extensive numerical simulations and theoretical predictions, it is shown that the value of the epidemic threshold in scale-free networks depends directly on flow conditions, in particular on the first and second moments of the betweenness distribution given a routing protocol. We consider the scenarios in which the delivery capability of the nodes is bounded or unbounded. In both cases, the threshold values depend on the traffic and decrease as flow increases. Bounded delivery provokes the emergence of congestion, slowing down the spreading of the disease and setting a limit for the epidemic incidence. Our results provide a general conceptual framework to understand spreading processes on complex networks.