[1]ZHAO Jing,XIA Chengyi,SUN Shiwen,et al.A novel SIR model with infection delay and nonuniform transmission in complex networks[J].CAAI Transactions on Intelligent Systems,2013,8(2):128-134.[doi:10.3969/j.issn.1673-4785.201210027]
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A novel SIR model with infection delay and nonuniform transmission in complex networks

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 8 Number of periods: 2013 2 Page number: 128-134 Column: 学术论文—智能系统 Public date: 2013-04-25
Title:
A novel SIR model with infection delay and nonuniform transmission in complex networks
Author(s):
ZHAO Jing1; 2;  XIA Chengyi1; 2;  SUN Shiwen1; 2;  WANG Li1; 2
1. Tianjin Key Laboratory of Intelligence Computing and Novel Software Technology;
2. Key Laboratory of Computer Vision and Systems (Ministry of Education), Tianjin University of Technology, Tianjin 300384, China
Keywords:
infection delay;  nonuniform transmission;  critical threshold;  complex networks;  SIR model
CLC:
TP18；O231.5
DOI:
10.3969/j.issn.1673-4785.201210027
Abstract:
In order to analyze and understand the spreading behavior of infectious diseases, the authors propose to examine susceptible-infected-removed (SIR) model. The researchers simultaneously introduce into the epidemic model the two factors: influencing disease spreading behavior, and infection delay and nonuniform transmission, utilizing the SIR model. Based on the mean-field approximation and large-scale numerical simulations， the analytical results of critical thresholds of disease spreading were derived, along with the infection delay and the nonuniform transmission having a distinct impact on the critical threshold. The infection delay can greatly decrease the critical threshold and facilitate the spread of epidemics, while the nonuniform transmission can augment the critical threshold and hinder the epidemic spreading in complex networks. Current results are conducive to further understand the epidemic spreading inside the complex real systems, as well as to fully consider the roles of infection delay, transmission factors and topological structure of population in the spreading of diseases. The results also provide a number of theoretical evidence to design more effective epidemic prevention and containment measures.
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