Research on how the difference of personal propagation ability influences the epidemic spreading in activity-driven network

Han, Di; Yan, Shuting; Han, Seung-Beom; Qian, Lingfei; Chris, Ampimah Benjamin

doi:10.1016/j.physa.2018.09.077

Cited by 7 publications

(2 citation statements)

References 27 publications

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“…The currently propagation theory of complex network mainly focuses on the global level. Most scholars have studied the problem of propagation in the actual scenario [1][2][3][4][5][6][7][8][9][10][11][12][13]. To sum up, the current literature research mainly focuses on global level propagation, which can be classified as global propagation model, because of taking into account global, statistical and structural factors of complex network.…”

Section: Literature Overviewmentioning

confidence: 99%

Non-linear Point to Point Propagation Model Based on Complex Networks

Zhao¹,

Wang²,

Wang³

et al. 2019

dtcse

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Infectious disease model is used to describe the global level of a complex network propagate, but in the real world, people pay more attention to the point to point level of a complex network propagate, especially for the quantification of point to point propagation. In order to solve this problem, we proposed a non-linear point to point propagation model of complex network based on edge weight calculation. Characteristics of edge propagation weight between nodes is described and quantified from point to point perspective. At the same time, nodes have a damping effect on propagation. The propagation damping calculation is incorporated into characteristics of edge propagation weight and node which is functional. Referring to some ideas of the neuron model, series theory and information transmission theory, and the model is verified in a large-scale financial risk propagation experiment.

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Section: Literature Overviewmentioning

confidence: 99%

Non-linear Point to Point Propagation Model Based on Complex Networks

Zhao¹,

Wang²,

Wang³

et al. 2019

dtcse

View full text Add to dashboard Cite

show abstract

“…Many scholars have proposed improvements and optimizations to the time-varying network model, to make it more in line with the real network and dynamic characteristics, and mainly applied it to the epidemic transmission and the information transmission in social networks [21]. Some scholars set properties for nodes, such as attractiveness and willingness [22][23][24][25][26][27][28][29] or introduced external factor to the process of propagation [30][31][32].…”

Section: Introductionmentioning

confidence: 99%

Time‐Varying Spatial Memory Model and Its Impact on Virus Spreading

Han

Zhao

et al. 2021

Complexity

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The models of time-varying network have a profound impact on the study of virus spreading on the networks. On the basis of an activity-driven memory evolution model, a time-varying spatial memory model (TSM) is proposed. In the TSM model, the cumulative number of connections between nodes is recorded, and the spatiality of nodes is considered at the same time. Therefore, the active nodes tend to connect the nodes with high intimacy and close proximity. Then, the TSM model is applied to epidemic spreading, and the epidemic spreading on different models is compared. To verify the universality of the TSM model, this model is also applied to rumor spreading, and it is proved that it can also play a good inhibiting effect. We find that, in the TSM network, the introduction of spatiality and memory can slow down the propagation speed and narrow the propagation scope of disease or rumor, and memory is more important. We then explore the impact of different prevention and control methods on pandemic spreading to provide reference for COVID-19 management control and find when the activity of node is restricted, the spreading will be controlled. As floating population has been acknowledged as a key parameter that affects the situation of COVID-19 after work resumption, the factor of population mobility is introduced to calculate the interregional population interaction rate, and the time-varying interregional epidemic model is established. Finally, our results of infectious disease parameters based on daily cases are in good agreement with the real data, and the effectiveness of different control measures is evaluated.

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How the individuals’ risk aversion affect the epidemic spreading

Han

Shao

et al. 2020

Applied Mathematics and Computation

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Research on how the difference of personal propagation ability influences the epidemic spreading in activity-driven network

Cited by 7 publications

References 27 publications

Non-linear Point to Point Propagation Model Based on Complex Networks

Non-linear Point to Point Propagation Model Based on Complex Networks

Time‐Varying Spatial Memory Model and Its Impact on Virus Spreading

How the individuals’ risk aversion affect the epidemic spreading

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