- 必威体育下载

姓名
邮箱
手机号码
标题
留言内容
验证码

Abstract

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.

Keywords:

Authors and contacts

Corresponding author:Wang Jian-Bo,phyjbw@gmail.com; Xu Xiao-Ke,xuxiaoke@foxmail.com

Funds:Project supported by the National Natural Science Foundation of China (Grant No. 62173065).

FullText HTML: translate this paragraph

References

[1]
[2]
[3]
[4]
[5]
[6]
[7]
[8]
[9]
[10]
[11]
[12]
[13]
[14]
[15]
[16]
[17]
[18]
[19]
[20]
[21]
[22]
[23]
[24]
[25]
[26]
[27]
[28]
[29]
[30]

Cited By

DownLoad: Full-Size Img PowerPoint

符号	描述
$N$	网络中的个体数
$t$	时间步
${k_{\text{v}}}$	线上信息接触层中的活跃个体在单位时间步长中产生的连边数
${k_{\text{p}}}$	线下物理接触层中的活跃个体在单位时间步长中产生的连边数
$a_{l, i}^{\text{V}}$	线上信息接触层中个体i在$l$社团的活跃性类别
$a_{l, i}^{\text{P}}$	线下物理接触层中个体i在$l$社团的活跃性类别
$ \left\langle {{k_{\text{v}}}} \right\rangle $	线上信息接触层的平均度
$\left\langle {{k_{\text{p}}}} \right\rangle $	线下物理接触层的平均度
$\lambda $	信息扩散率
$\delta $	信息遗忘率
$\beta $	流行病传染率
$\mu $	流行病恢复率
${\beta ^{\text{U}}}$	状态为U的个体的流行病传染率
${\beta ^{\text{A}}}$	状态为A的个体的流行病传染率
$\sigma $	捕捉${\beta ^{\text{U}}}$和${\beta ^{\text{A}}}$之间差异的参数, 又称为自我保护率: ${\beta ^{\text{A}}} = \sigma {\beta ^{\text{U}}}$
$h$	个体流动率
$P_{l, i}^X$	个体i在$l$社团处于状态X的概率
${r_{l, i}}\left( t \right)$	处于$l$社团的个体i在时间$t$内没有被任何邻居告知的概率
$q_{l, i}^{\text{A}}\left( t \right)$	处于$l$社团且状态为A的个体i在时间$t$内没有被任何邻居感染的概率
$q_{l, i}^{\text{U}}\left( t \right)$	处于$l$社团且状态为U的个体i在时间$t$内没有被任何邻居感染的概率

DownLoad: CSV

[1]
[2]
[3]
[4]
[5]
[6]
[7]
[8]
[9]
[10]
[11]
[12]
[13]
[14]
[15]
[16]
[17]
[18]
[19]
[20]
[21]
[22]
[23]
[24]
[25]
[26]
[27]
[28]
[29]
[30]

[1]	Gao Yan-Li, Xu Wei-Nan, Zhou Jie, Chen Shi-Ming.Analysis of seepage behaviour in binary two-layer coupled networks. Acta Physica Sinica, 2024, 73(16): 168901.doi:10.7498/aps.73.20240454
[2]	Li Ying-Ke, Zhao Shi, Lou Yi-Jun, Gao Dao-Zhou, Yang Lin, He Dai-Hai.Epidemiological parameters and models of coronavirus disease 2019. Acta Physica Sinica, 2020, 69(9): 090202.doi:10.7498/aps.69.20200389
[3]	Liang Xiao, Qian Zhi-Hong, Tian Hong-Liang, Wang Xue.Markov decision model based handoff selection algorithm for heterogeneous wireless networks. Acta Physica Sinica, 2016, 65(23): 236402.doi:10.7498/aps.65.236402
[4]	Su Xiao-Ping, Song Yu-Rong.Leveraging neighborhood “structural holes” to identifying key spreaders in social networks. Acta Physica Sinica, 2015, 64(2): 020101.doi:10.7498/aps.64.020101
[5]	Yin Wen-Ye, He Wei-Ji, Gu Guo-Hua, Chen Qian.A new full waveform analysis approach using simulated tempering Markov chain Monte Carlo method. Acta Physica Sinica, 2014, 63(16): 164205.doi:10.7498/aps.63.164205
[6]	Wang Xing-Yuan, Zhao Zhong-Xiang.Partitioning community structure in complex networks based on node dependent degree. Acta Physica Sinica, 2014, 63(17): 178901.doi:10.7498/aps.63.178901
[7]	Ding Yi-Min, Ding Zhuo, Yang Chang-Ping.The network model of urban subway networks with community structure. Acta Physica Sinica, 2013, 62(9): 098901.doi:10.7498/aps.62.098901
[8]	Li Rui-Qi, Tang Ming, Hui Pak-Ming.Epidemic spreading on multi-relational networks. Acta Physica Sinica, 2013, 62(16): 168903.doi:10.7498/aps.62.168903
[9]	Gao Zhong-Ke, Jin Ning-De, Yang Dan, Zhai Lu-Sheng, Du Meng.Complex networks from multivariate time series for characterizing nonlinear dynamics of two-phase flow patterns. Acta Physica Sinica, 2012, 61(12): 120510.doi:10.7498/aps.61.120510
[10]	Yuan Chao, Chai Yi.Group similarity based algorithm for network community structure detection. Acta Physica Sinica, 2012, 61(21): 218901.doi:10.7498/aps.61.218901
[11]	Zhang Cong, Shen Hui-Zhang, Li Feng, Yang He-Qun.Multi-resolution density modularity for finding community structure in complex networks. Acta Physica Sinica, 2012, 61(14): 148902.doi:10.7498/aps.61.148902
[12]	Di Gen-Hu, Xu Yong, Xu Wei, Gu Ren-Cai.Chaos for a class of complex epidemiological models. Acta Physica Sinica, 2011, 60(2): 020504.doi:10.7498/aps.60.020504
[13]	Shao Fei, Jiang Guo-Ping.Optimal traffic routing strategy based on community structure. Acta Physica Sinica, 2011, 60(7): 078902.doi:10.7498/aps.60.078902
[14]	Lü Ling, Zou Jia-Rui, Yang Ming, Meng Le, Guo Li, Chai Yuan.Synchronization of spatiotemporal chaos in large scale rich-club network. Acta Physica Sinica, 2010, 59(10): 6864-6870.doi:10.7498/aps.59.6864
[15]	Shen Yi, Xu Huan-Liang.The evaluation function of weight similarity and its application in community detection in weighted networks. Acta Physica Sinica, 2010, 59(9): 6022-6028.doi:10.7498/aps.59.6022
[16]	Wang Gao-Xia, Shen Yi.Modularity matrix of networks and the measure of community structure. Acta Physica Sinica, 2010, 59(2): 842-850.doi:10.7498/aps.59.842
[17]	Zheng Li-Ming, Liu Song-Hao, Wang Fa-Qiang.Geometric phase evolution of atom under non-Markovian environment. Acta Physica Sinica, 2009, 58(4): 2430-2434.doi:10.7498/aps.58.2430
[18]	Gao Zhong-Ke, Jin Ning-De.Complex network community structure of two-phase flow pattern and its statistical characteristics. Acta Physica Sinica, 2008, 57(11): 6909-6920.doi:10.7498/aps.57.6909
[19]	Chen Bo, Xia Qing-Zhong, Lebedev V. T..Experimental study of fullerene-PVP polymers by small-angle neutron scattering. Acta Physica Sinica, 2005, 54(6): 2821-2825.doi:10.7498/aps.54.2821
[20]	Zhong Ling, Weng Jia-Qiang.. Acta Physica Sinica, 2000, 49(4): 626-630.doi:10.7498/aps.49.626

Catalog

Vol. 72, Issue 6 - 2023-03-20

Metrics

Abstract views:4934
PDF Downloads:143
Cited By:0

Search

Article