Evolving nature of human contact networks with its impact on epidemic processes

TL;DR

This paper analyzes the evolving nature of human contact networks, identifies key mechanisms of their dynamics, and introduces a temporal network model that accurately predicts epidemic spreading behaviors.

Contribution

It proposes the memory driven (MD) model capturing contact evolution mechanisms and demonstrates its effectiveness in replicating real-world epidemic spreading dynamics.

Findings

The MD model matches real-world SI spreading times.

Activity transitions promote epidemic spread.

Contact establishment suppresses prevalence.

Abstract

Human contact networks are constituted by a multitude of individuals and pairwise contacts among them. However, the dynamic nature, which generates the evolution of human contact networks, of contact patterns is not known yet. Here, we analyse three empirical datasets and identify two crucial mechanisms of the evolution of temporal human contact networks, i.e. the activity state transition laws for an individual to be socially active, and the contact establishment mechanism that active individuals adopt. We consider both of the two mechanisms to propose a temporal network model, named the memory driven (MD) model, of human contact networks. Then we study the susceptible-infected (SI) spreading processes on empirical human contact networks and four corresponding temporal network models, and compare the full prevalence time of SI processes with various infection rates on the networks. The full prevalence time of SI processes in the MD model is the same as that in real-world human contact networks. Moreover, we find that the individual activity state transition promotes the spreading process, while, the contact establishment of active individuals suppress the prevalence. Apart from this, we observe that even a small percentage of individuals to explore new social ties is able to induce an explosive spreading on networks. The proposed temporal network framework could help the further study of dynamic processes in temporal human contact networks, and offer new insights to predict and control the diffusion processes on networks.