Impact of different social attitudes on epidemic spreading in activity-driven networks

Hou, Yunxiang; Lu, Yikang; Dong, Yuting; Jin, Libin; Shi, Lei

doi:10.1016/j.amc.2023.127850

Cited by 10 publications

(4 citation statements)

References 49 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Theoretical estimation of the epidemic threshold and size can be derived as follows. [13] At a mean-field level, the population engaged in pairwise interactions at time t can be obtained as…”

Section: Epidemic Spreadingmentioning

confidence: 99%

See 1 more Smart Citation

Impact of different interaction behavior on epidemic spreading in time-dependent social networks

Huang 黄,

Chen 陈,

Li 李

et al. 2024

Chinese Phys. B

View full text Add to dashboard Cite

Social interactions account for the spread of epidemics in population. In this paper, we investigate the impact of pairwise and group interactions on the spread of epidemics through an activity-driven model based on time-dependent networks. The effects of pairwise/group interaction proportion and pairwise/group interaction intensity are explored by extensive simulation and theoretical analysis. It is demonstrated that altering the group interaction proportion can either hinder or enhance the spread of epidemics, depending on the relative social intensity of group and pairwise interactions. As the group interaction proportion decreases, the impact of reducing group social intensity diminishes. The ratio of group and pairwise social intensity can affect the effect of group interaction proportion on the scale of infection. A weak heterogeneous activity distribution can raise the epidemic threshold, and reduce the scale of infection. These results benefit the design of epidemic control strategy.

show abstract

Section: Epidemic Spreadingmentioning

confidence: 99%

“…Wang et al [12] utilized the AD network to investigate the effects of individual behaviors on epidemics. Hou et al [13] analyzed the impact of social attitudes on the spread of epidemics.…”

Section: Introductionmentioning

confidence: 99%

Impact of different interaction behavior on epidemic spreading in time-dependent social networks

Huang 黄,

Chen 陈,

Li 李

et al. 2024

Chinese Phys. B

View full text Add to dashboard Cite

show abstract

“…Since 0.001 ⩽ λ ⩽ 0.1 is used in the following simulations, in order to investigate the endemic state within a closed population, we set µ = 0.01 as in [17]. For the firing probability, we consider a heavy-tailed density function of the form F (a) ∝ a −γa , with 0.01 ⩽ a ⩽ 1 and γ a = 2.1 [37,38]. γ a is fixed at 2.1 in this paper.…”

Section: J Stat Mech (2023) 123405mentioning

confidence: 99%

Effect of behavioral changes on epidemic spreading in coupled simplicial activity driven networks

Huang,

Xu,

et al. 2023

J. Stat. Mech.

View full text Add to dashboard Cite

Despite intensive studies on the epidemic spreading problem in social networks, both intra-group and inter-group interactions are represented as dyadic links. In this study, using coupled simplicial activity driven networks, we examine the impact of behavioral modification on epidemic propagation while taking into account various intra-group and inter-group interactions. The intra-group interactions are represented as simplicial structures, while the inter-group connections are represented as pairwise links. Two distinct types of behavioral changes are considered: (i) infected individuals decrease their activity due to self-quarantine, (ii) susceptible individuals limit their contacts due to self-protection. Numerical simulation and theoretical analysis reveal that both self-quarantine and self-protection behavior can effectively suppress epidemic spreading, leading to increased thresholds of epidemics and reduced steady-state fractions of infected individuals. Increasing the simplex size will lead to less protective effect of these behavioral changes, while enhancing pairwise connections between groups will not decrease the effect of behavioral changes.

show abstract

“…To gain deeper insights into the dynamic properties of the proposed coupled spreading model, we employ the MMCA [41][42][43] to formulate the dynamic propagation equations. To delineate the configuration of the upper and lower layers, we utilized adjacency matrices A and C. Matrix A stores the structural information of the upper layer, where each element a ij can take on either a value of 1 or 0.…”

Section: Theoretical Analysismentioning

confidence: 99%

Coupled Propagation Dynamics of Information and Infectious Disease on Two-Layer Complex Networks with Simplices

Hong,

Zhou,

Wang

et al. 2023

Mathematics

View full text Add to dashboard Cite

The mutual influence between information and infectious diseases during the spreading process is becoming increasingly prominent. To elucidate the impact of factors such as higher-order interactions, interpersonal distances, and asymptomatic carriers on the coupled propagation of information and infectious diseases, a novel coupled spreading model is constructed based on a two-layer complex network, where one layer is a higher-order network and another layer is a weighted network. The higher-order interactions in information propagation are characterized using a 2-simplex, and a sUARU (simplicial unaware-aware-removed-unaware) model is employed to articulate information propagation. The inter-individual social distances in disease propagation are represented by the weights of a weighted network, and an SEIS (susceptible-exposed-infected-susceptible) model is utilized to describe disease propagation. The dynamic equations of coupled spreading are formulated utilizing the microscopic Markov chain approach. An analytical expression for the epidemic threshold is obtained by deriving it from the steady-state form of the dynamic equations. Comprehensive simulations are conducted to scrutinize the dynamic characteristics of the coupled spreading model. The findings indicate that enhancing the effects of higher-order interactions in information propagation and increasing inter-individual social distances both lead to higher outbreak thresholds and greater spreading of diseases. Additionally, a stronger infectivity among asymptomatic carriers and an extended incubation period are favorable for the outbreak and spread of an epidemic. These findings can provide meaningful guidance for the prevention and control of real-world epidemics.

show abstract

Impact of different social attitudes on epidemic spreading in activity-driven networks

Cited by 10 publications

References 49 publications

Impact of different interaction behavior on epidemic spreading in time-dependent social networks

Impact of different interaction behavior on epidemic spreading in time-dependent social networks

Effect of behavioral changes on epidemic spreading in coupled simplicial activity driven networks

Coupled Propagation Dynamics of Information and Infectious Disease on Two-Layer Complex Networks with Simplices

Contact Info

Product

Resources

About