计算机科学
马尔可夫链
扩散
传输(电信)
接触追踪
流行病模型
2019年冠状病毒病(COVID-19)
电信
机器学习
物理
疾病
病理
传染病(医学专业)
热力学
医学
人口
人口学
社会学
作者
Lifeng Shen,Jianbo Wang,Zhanwei Du,Xiao-Ke Xu
出处
期刊:Chinese Physics
[Acta Physica Sinica, Chinese Physical Society and Institute of Physics, Chinese Academy of Sciences]
日期:2023-01-01
卷期号:72 (6): 068701-068701
被引量:1
标识
DOI:10.7498/aps.72.20222206
摘要
Epidemic outbreaks in the real world are often accompanied by rapid information diffusion, which will change individual behavior patterns and affect the spread of epidemics. The community phenomenon in human society will also have an important influence on the spread of epidemics. The above factors to construct a new bilayer network are considered in this work. The activity-driven model is used to generate time-varying online information contact layer network and offline physical contact layer network. The information diffusion of individual online contact layer is used to affect the epidemic spreading dynamics of offline physical contact layer, and the individual mobility factor is used to control the community structure characteristics. In order to obtain the spreading dynamic equation of the whole network and analyze the spreading threshold of the network effectively, the microscopic Markov chain (MMC) approach is improved and extended to time-varying networks. Experimental verification of Monte Carlo simulations shows that the proposed method is highly accurate in predicting epidemic outbreak thresholds. The results show that individual mobility has no effect on the epidemic outbreak threshold, but it will affect the final number of infections in each community. The greater the individual contact capability of the online contact layer, the smaller the individual contact capability of the offline contact layer that can effectively suppress the epidemic spread. The above findings can present an important reference for effectively preventing and controlling the epidemic transmission in the real world.
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