Epidemic spreading on weighted adaptive networks

Yinzuo Zhou and Yingjie Xia

Physica A: Statistical Mechanics and its Applications, 2014, vol. 399, issue C, 16-23

Abstract: Considering the fact that the contact strengths among people are diverse both in the duration time and the distance, in this paper, we study the epidemic dynamics with susceptible–infective–susceptible (SIS) model on a weighted adaptive network to emphasize this contact feature. In this model, the weight of a link denotes the contact strength between two individuals connected by this link, and each susceptible individual may adaptively transfer the weight from a link to another. We find that this weight adaption process could significantly aggravate the prevalence of an epidemic. Moreover, we examine the effectiveness of the link-removal strategy with our model, and the results show that the weight adaption process may weaken the efficiency of the strategy. The theoretical analysis is supported by the simulation results.

Keywords: Weighted network; Adaptive; Epidemic spreading (search for similar items in EconPapers)
Date: 2014
References: View complete reference list from CitEc
Citations: View citations in EconPapers (7)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0378437113011606
Full text for ScienceDirect subscribers only. Journal offers the option of making the article available online on Science direct for a fee of $3,000

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:399:y:2014:i:c:p:16-23

DOI: 10.1016/j.physa.2013.12.036

Access Statistics for this article

Physica A: Statistical Mechanics and its Applications is currently edited by K. A. Dawson, J. O. Indekeu, H.E. Stanley and C. Tsallis

More articles in Physica A: Statistical Mechanics and its Applications from Elsevier
Bibliographic data for series maintained by Catherine Liu ().