Epidemic threshold and topological structure of susceptible-infectious-susceptible epidemics in adaptive networks

Guo, Dongchao; Trajanovski, Stojan; Bovenkamp, Ruud van de; Wang, Huijuan; Mieghem, Piet Van

doi:10.1103/physreve.88.042802

Cited by 74 publications

(64 citation statements)

References 25 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Lagorio et al studied the critical effect of quarantine on the epidemic propagation on an adaptive social contact network [12]. Guo et al proposed a continuous-time Adaptive Susceptible-Infected-Susceptible (ASIS) model to study the interplay between epidemic dynamics and the dynamics of network structure [13]. Song et al introduced a new preferentially reconnecting edge strategy of adaptive networks depending on spatial distance.…”

Section: Introductionmentioning

confidence: 99%

Can rewiring strategy control the epidemic spreading?

Dong

Yin

Liu

et al. 2015

Physica A: Statistical Mechanics and its Applications

View full text Add to dashboard Cite

h i g h l i g h t s• A novel epidemic spreading model in weighted adaptive network is proposed. • The relation intimacy among individuals is considered in rewiring strategy. • The effect of the modified rewiring strategy is evaluated.• As well as strong links, weak links play an important role in the epidemic spreading. a b s t r a c tRelation existed in the social contact network can affect individuals' behaviors greatly. Considering the diversity of relation intimacy among network nodes, an epidemic propagation model is proposed by incorporating the link-breaking threshold, which is normally neglected in the rewiring strategy. The impact of rewiring strategy on the epidemic spreading in the weighted adaptive network is explored. The results show that the rewiring strategy cannot always control the epidemic prevalence, especially when the link-breaking threshold is low. Meanwhile, as well as strong links, weak links also play a significant role on epidemic spreading.

show abstract

Section: Introductionmentioning

confidence: 99%

Can rewiring strategy control the epidemic spreading?

Dong

Yin

Liu

et al. 2015

Physica A: Statistical Mechanics and its Applications

View full text Add to dashboard Cite

show abstract

“…5 The ASIS metastable state prevalence is [34] (6) where the value under the square root in (6) is always positive. Hence, the metastable state always exists [33] and is given by (7) with a minus sign. The prevalence y as a function of τ is shown in Fig.…”

Section: De[a Ij ] Dtmentioning

confidence: 99%

“…In the ASIS model [33], the topology changes in the opposite way: (a) While either node i or j (but not both) is infected, the link between node i and j is removed with rate ζ in order to protect the susceptible node from infection and (b) while both node i and j are susceptible, a link is created between them with rate ξ . For the ASIS model, in the case that both node i and j are infected (i.e., X i = X j = 1), the link is preserved, whose link dynamic, opposite to (2), is…”

Section: De[a Ij ] Dtmentioning

confidence: 99%

From epidemics to information propagation: Striking differences in structurally similar adaptive network models

2015

Self Cite

View full text Add to dashboard Cite

The continuous-time adaptive susceptible-infected-susceptible (ASIS) epidemic model and the adaptive information diffusion (AID) model are two adaptive spreading processes on networks, in which a link in the network changes depending on the infectious state of its end nodes, but in opposite ways: (i) In the ASIS model a link is removed between two nodes if exactly one of the nodes is infected to suppress the epidemic, while a link is created in the AID model to speed up the information diffusion; (ii) a link is created between two susceptible nodes in the ASIS model to strengthen the healthy part of the network, while a link is broken in the AID model due to the lack of interest in informationless nodes. The ASIS and AID models may be considered as first-order models for cascades in real-world networks. While the ASIS model has been exploited in the literature, we show that the AID model is realistic by obtaining a good fit with Facebook data. Contrary to the common belief and intuition for such similar models, we show that the ASIS and AID models exhibit different but not opposite properties. Most remarkably, a unique metastable state always exists in the ASIS model, while there an hourglass-shaped region of instability in the AID model. Moreover, the epidemic threshold is a linear function in the effective link-breaking rate in the AID model, while it is almost constant but noisy in the AID model. Over the past decade, many real-world networks have been characterized via graph metrics [1-3] such as clustering, assortativity, modularity, degree distribution, and spectral properties. Recently, robustness characteristics and complex dependences have been analyzed in networks of networks [4], while a parallel track in network science has focused on relatively simple dynamic processes on networks such as epidemics [5,6], synchronization [7], and opinion diffusion [8][9][10]. In most studies so far, the networks are considered fixed or independent of the dynamic process. After the seminal work of Gross et al. [11], the coupling between epidemic processes and the underlying network topology has been extensively studied [12][13][14][15].The coupling between process and topology is natural in many cases. In epidemics [16], for example, after the observation of an infectious relative, one may either avoid him or her (by changing the social contact network) or increase the protection against the virus (without altering the topology). In human brain networks, Hebbian learning alters the connectivity between brain regions that are trained or neurally excited. Although self-adaptation naturally occurs in biology, adaptive networks, in which the process interacts with the topology, are unfortunately difficult to analyze and we barely understand the interplay between process and topology. Twitter measurements [17,18] show that the topology of the network adaptively changes connectivity towards users with high popularity and the ordinary users tend to directly follow the popular ones to get the information faster, instead of a...

show abstract

“…Interfering synergistic effects associated with, e.g. behavioural responses to epidemic spread [25,[34][35][36] or competition for resources [29], can also play an important role in spreading dynamics.…”

Section: Introductionmentioning

confidence: 99%

“…These effects are not captured by simple epidemiological models but they can significantly change the dynamics of spreading processes. The knowledge and understanding of complex transmission dynamics on spreading and its interplay with the network topology is rather limited and is a topic of active research [7,[25][26][27][28][29][30][31][32][33][34][35][36].…”

Section: Introductionmentioning

confidence: 99%

Effects of local and global network connectivity on synergistic epidemics

2015

View full text Add to dashboard Cite

Epidemics in networks can be affected by cooperation in transmission of infection and also connectivity between nodes. An interplay between these two properties and their influence on epidemic spread are addressed in the paper. A particular type of cooperative effects (called synergy effects) is considered, where the transmission rate between a pair of nodes depends on the number of infected neighbours. The connectivity effects are studied by constructing networks of different topology, starting with lattices with only local connectivity and then with networks which have both local and global connectivity obtained by random bond-rewiring to nodes within certain distance. The susceptible-infected-removed epidemics were found to exhibit several interesting effects: (i) for epidemics with strong constructive synergy spreading in networks with high local connectivity, the bond rewiring has a negative role on epidemic spread, i.e. it reduces invasion probability; (ii) in contrast, for epidemics with destructive or weak constructive synergy spreading on networks of arbitrary local connectivity, rewiring helps epidemics to spread; (iii) and, finally, rewiring always enhances the spread of epidemics, independent of synergy, if the local connectivity is low. * db560@cam.ac.uk † fperez-reche@abdn.ac.uk ‡ snt1000@cam.ac.uk 2

show abstract

Epidemic threshold and topological structure of susceptible-infectious-susceptible epidemics in adaptive networks

Cited by 74 publications

References 25 publications

Can rewiring strategy control the epidemic spreading?

Can rewiring strategy control the epidemic spreading?

From epidemics to information propagation: Striking differences in structurally similar adaptive network models

Effects of local and global network connectivity on synergistic epidemics

Contact Info

Product

Resources

About