Propagation Dynamics of a Periodic Epidemic Model on Weighted Interconnected Networks

被引:10
作者
Xu, Zhongpu [1 ]
Wang, Yu [1 ]
Wu, Naiqi [2 ]
Fu, Xinchu [1 ]
机构
[1] Shanghai Univ, Dept Math, Shanghai 200444, Peoples R China
[2] Macau Univ Sci & Technol, Inst Syst Engn, Macau 999078, Peoples R China
来源
IEEE TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERING | 2020年 / 7卷 / 03期
关键词
Diseases; Mathematical model; Numerical models; Sociology; Statistics; Stability analysis; Complex networks; Weighted network; interconnected network; periodic incidence rate; basic reproduction number; stability; OUTBREAKS; THRESHOLD; PATTERNS; DISEASES;
D O I
10.1109/TNSE.2019.2939074
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
Many real-world networks are comprised of several networks interconnected with each other and they may have different topologies and epidemic dynamics. The dynamical behaviors of various epidemic models on coupled networks have attracted great attentions. For instance, many people adhere to the statutory working and rest days, which results in a periodic fluctuation of disease transmission in the population, and the strength of connections between two individuals cannot be ignored either. In this paper, we explore the disease transmission on weighted interconnected networks by establishing a model that includes contact strengths and periodic incidence rates. The weights on the links indicate the familiarity or intimacy of the interactive individuals. Here, we analyze the stability of the disease-free periodic solution and the unique positive periodic solution (i.e., disease-free equilibrium and endemic equilibrium) of the model, and an explicit expression for the basic reproduction number in some special cases of the periodic model is derived. We also perform numerical simulations to validate and supplement the theoretical results. It is found that the weight exponent promotes the epidemic transmission by enlarging the basic reproduction number, and the influence of internal infectious rate on epidemic prevalence is larger than that of cross infectious rate for different network structures. It is expected that this work can deepen the understanding of transmission dynamics on weighted interconnected networks.
引用
收藏
页码:1545 / 1556
页数:12
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