NDLI: Steady states and critical behavior of epidemic spreading on complex networks

Content Provider	IEEE Xplore Digital Library
Author	Shujuan Peng Yuanxiang Li Bojin Zheng
Copyright Year	2008
Description	Author affiliation: Dept. of Comput. Sch., Wuhan Univ., Wuhan (Shujuan Peng)
Abstract	In this paper, we derive the interaction Markov chains (IMC) mean-field equations for dynamics of the epidemic spreading susceptible-infected-recovery (SIR) model that takes place on top of complex homogeneous and heterogeneous networks. These equations are solved numerically by means of a stochastic approach. We use these equations to examine the threshold behaviour and dynamics of the model on small-world and scale-free networks. Theoretical and numerically analysis show that in homogeneous networks the model exhibits a critical threshold in the epidemic spreading rate below which it cannot diffuse in the system. In the case of SF networks, on the other hand, this threshold becomes vanishingly small in the limit of infinite system size. The results indicate the critical threshold value for SIR epidemic is greatly decided by the network topology and scale-free networks are prone to the spreading epidemics. We present analytical and Monte Carlo calculations for both small-world and scale-free networks and compare the results with those obtained by the numerical method, which shows stochastic numerical approach (SNA) can save memory and get the fast exploration. For small-world networks, we find different reconnection probability values p will affect the epidemic spreading speed but no influence for the final statement.
Starting Page	3481
Ending Page	3486
File Size	173103
Page Count	6
File Format	PDF
ISBN	9781424421138
DOI	10.1109/WCICA.2008.4593477
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-06-25
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Mathematical model Numerical models Equations Steady-state Stochastic processes Complex networks Analytical models Stochastic numerical approach Interaction Markov chains Epidemic spreading Threshold value Monte-Carlo simulation
Content Type	Text
Resource Type	Article

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