Epidemic spreading on uniform networks with two interacting diseases

Feng, Yongjin; Fan, Qingli; Ma, Lin; Ding, Li

doi:10.1016/j.physa.2013.08.080

Cited by 13 publications

(4 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For z > 1, it is difficult to solve Eqs. (17) and (19), but the variety of h c is similar with that with z = 1. When the shortcut probability p increases, the percolation threshold h c keeps decreasing.…”

Section: Percolation Thresholdsmentioning

confidence: 68%

“…18 Y. Feng et al considered a pair of homogeneous diseases spreading concurrently on uniform networks based on the Susceptible-Infectious-Susceptible (SIS) model. 19 A new model describing the transmission process of the interacting diseases was established. C. H. Li et al studied the spreading of infections in complex heterogeneous networks based on a Susceptible-Infectious-Recovered-Susceptible (SIRS) epidemic model with birth and death rates.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Percolation Thresholds on Tree-Based Communities of Wireless Sensor Networks

Qiao¹,

Niu²,

Zhang³

et al. 2016

IJNDC

View full text Add to dashboard Cite

Many efficient deployments of large-scale wireless sensor networks based on the tree-based community rise into view recently. Sensor nodes are severely resource constrained, and lack sophisticated defense mechanisms to fight virus attacks. Cyber viruses spread through node populations over the networks, and a number of results about the prevalence have been derived in recent years by exploiting epidemic behaviors and the percolation processes on networks. A network model based on the Cayley tree is proposed to depict the underlying tree-based architectures of the network and the community. The percolation thresholds are calculated and analyzed in two cases. Due to random links in the communities, the sensor virus extends drastically on the network. The analysis and evaluation shows that the percolation threshold keeps decreasing with the increase of the shortcut probability. There is the smallest percolation threshold in a random network, where the virus easily attacks the network from one side to another. The conclusions can further our understanding of epidemic dynamics on tree-based communities of wireless sensor networks.

show abstract

Section: Percolation Thresholdsmentioning

confidence: 68%

Section: Introductionmentioning

confidence: 99%

Percolation Thresholds on Tree-Based Communities of Wireless Sensor Networks

Qiao¹,

Niu²,

Zhang³

et al. 2016

IJNDC

View full text Add to dashboard Cite

show abstract

“…In most of the literature dealing with the epidemic spreading behavior, the topology structure of the underlying network is assumed to be static. [1][2][3][4][5][6][7][8][9][10] However, in practice, the relations between individuals are unlikely to keep unchanged all the time, the movements of individuals cause a dynamic topology structure. In fact, some recent study results indicate that the mobility of individuals can play a significant role in the epidemic spreading process.…”

Section: Introductionmentioning

confidence: 99%

Epidemic spreading on random surfer networks with infected avoidance strategy

et al. 2016

Self Cite

View full text Add to dashboard Cite

In this paper, we study epidemic spreading on random surfer networks with infected avoidance (IA) strategy. In particular, we consider that susceptible individuals' moving direction angles are affected by the current location information received from infected individuals through a directed information network. The model is mainly analyzed by discrete-time numerical simulations. The results indicate that the IA strategy can restrain epidemic spreading effectively. However, when long-distance jumps of individuals exist, the IA strategy's effectiveness on restraining epidemic spreading is heavily reduced. Finally, it is found that the influence of the noises from information transferring process on epidemic spreading is indistinctive.

show abstract

“…Now more and more researchers are devoted to investigate the network spreading dynamics. [1][2][3][4][5][6][7][8][9] And along with the further study, a vast amount of work has been produced in the field of mathematical epidemiology.…”

Section: Introductionmentioning

confidence: 99%

Multi-granularity immunization strategy based on SIRS model in scale-free network

Nian

Wang

2015

Mod. Phys. Lett. B

View full text Add to dashboard Cite

In this paper, a new immunization strategy was established to prevent the epidemic spreading based on the principle of "Multi-granularity" and "Pre-warning Mechanism", which send different pre-warning signal with the risk rank of the susceptible node to be infected. The pre-warning means there is a higher risk that the susceptible node is more likely to be infected. The multi-granularity means the susceptible node is linked with multi-infected nodes. In our model, the effect of the different situation of the multigranularity immunizations is compared and different spreading rates are adopted to describe the epidemic behavior of nodes. In addition the threshold value of epidemic outbreak is investigated, which makes the result more convincing. The theoretical analysis and the simulations indicate that the proposed immunization strategy is effective and it is also economic and feasible.

show abstract

Epidemic spreading on uniform networks with two interacting diseases

Cited by 13 publications

References 21 publications

Percolation Thresholds on Tree-Based Communities of Wireless Sensor Networks

Percolation Thresholds on Tree-Based Communities of Wireless Sensor Networks

Epidemic spreading on random surfer networks with infected avoidance strategy

Multi-granularity immunization strategy based on SIRS model in scale-free network

Contact Info

Product

Resources

About