Epidemic variability in complex networks

TL;DR

This paper investigates how disease outbreaks vary over time in complex networks using simulations, revealing sensitivity to initial conditions, the role of network structure, and implications for containment strategies.

Contribution

It provides a detailed numerical analysis of epidemic variability on complex networks, highlighting the influence of network topology and path multiplicity on infection dynamics.

Findings

Large fluctuations in infection times for hubs limit early detection.

Infection time depends on node degree and distance to seed.

Longer paths reduce average infection time.

Abstract

We study numerically the variability of the outbreak of diseases on complex networks. We use a SI model to simulate the disease spreading at short times, in homogeneous and in scale-free networks. In both cases, we study the effect of initial conditions on the epidemic's dynamics and its variability. The results display a time regime during which the prevalence exhibits a large sensitivity to noise. We also investigate the dependence of the infection time on nodes' degree and distance to the seed. In particular, we show that the infection time of hubs have large fluctuations which limit their reliability as early-detection stations. Finally, we discuss the effect of the multiplicity of shortest paths between two nodes on the infection time. Furthermore, we demonstrate that the existence of even longer paths reduces the average infection time. These different results could be of use for the design of time-dependent containment strategies.