Optimal Control Strategy for Traffic Driven Epidemic Spreading Based on Community Structure

Fei Shao,Guo-Ping Jiang

doi:10.1155/2013/204093

Abstract

It is shown that community structure has a great impact on traffic transportation and epidemic spreading. The density of infected nodes and the epidemic threshold have been proven to have significant relationship with the node betweenness in traffic driven epidemic spreading method. In this paper, considering the impact of community structure on traffic driven epidemic spreading, an effective and novel strategy to control epidemic spreading in scale-free networks is proposed. Theoretical analysis shows that the new control strategy will obviously increase the ratio between the first and the second moments of the node betweenness distribution in scale-free networks. It is also found that the more accurate the community is identified, the stronger community structure the network has and the more efficient the control strategy is. Simulations on both computer-generated and real-world networks have confirmed the theoretical results.

Highlights

In the past few years, lots of epidemics among humans, animals, and plants caused an enormous amount of damage and loss
Since lots of real-world networks can be properly described as complex networks with nodes representing individuals and edges denoting the interactions among them, the disease outbreaks in biological systems can be viewed as the epidemic spreading on complex networks
Through theoretical predictions and extensive numerical simulations, it is shown that the traffic driven epidemic spreading depends directly on flow conditions, in particular on the node betweenness distribution. (Betweenness is a measure of the extent to which a node lies on the paths between others)

Summary

Introduction

In the past few years, lots of epidemics among humans, animals, and plants caused an enormous amount of damage and loss. With the booming development of complex networks theory, a number of models have been proposed to characterize the epidemic spreading [1,2,3,4,5,6,7,8,9,10,11]. In those most extensively studied models, they assume that the spreading is driven by reaction processes, which occurs from every infected node through all its neighbours at each time step. It has obvious effects on controlling the epidemic spreading in scale-free networks because it can enrich the ratio between the first and the second moments of the node betweenness distribution

Models

Analysis

Simulation Results and Discussions

Conclusion