Slow epidemic extinction in populations with heterogeneous infection rates

Buono, Camila; Vázquez, Federico; Macri, P. A.; Braunstein, Lidia A.

doi:10.1103/physreve.88.022813

Cited by 51 publications

(49 citation statements)

References 26 publications

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“…However, apart from the heterogeneity of degree distribution, the heterogeneity of weights on edges makes the CHMF theory be hard to accurately describe the spreading dynamics on weighted networks [30]. To solve this question, we develop an edge-weight based compartmental theory, which is inspired by Refs.…”

Section: A Spreading Dynamicsmentioning

confidence: 99%

“…The HMF theory assumes that the nodes of the same degrees will show the same dynamical characteristics [15,31,32], and can only qualitatively understand the effects of heterogeneous structural properties on quenched networks [24,30]. Similar to the HMF theory, the analytical results derived from the percolation theory will also obviously deviate from the numerical results in the case of strong structural heterogeneity [21], which is caused by the strong dynamic correlations between two connected nodes [33].…”

Section: Introductionmentioning

confidence: 99%

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Epidemic spreading on complex networks with general degree and weight distributions

et al. 2014

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The spread of disease on complex networks has attracted widely attention in the physics community. Recent works have demonstrated that heterogeneous degree and weight distributions have a significant influence on the epidemic dynamics. In this study, a novel edge-weight based compartmental approach is developed to estimate the epidemic threshold and epidemic size (final infected density) on networks with general degree and weight distributions, and a remarkable agreement with numerics is obtained. Even in complex network with the strong heterogeneous degree and weight distributions, this approach is worked. We then propose an edge-weight based removal strategy with different biases, and find that such a strategy can effectively control the spread of epidemic when the highly weighted edges are preferentially removed, especially when the weight distribution of a network is extremely heterogenous. The theoretical results from the suggested method can accurately predict the above removal effectiveness.

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Section: A Spreading Dynamicsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Epidemic spreading on complex networks with general degree and weight distributions

et al. 2014

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“…Theoretically existing studies found that promoting the spreading dynamics could induce distinct critical phenomena with different outbreak thresholds and critical exponents [8]. Practically speaking, promoting the spreading dynamics could shed some lights on the propagation of information [9][10][11], marketing management [12,13], disease spreading [14][15][16][17][18], etc. Many strategies for promoting spreading dynamics have been proposed, such as choosing influential seeds [19,20] and designing effective contact strategies [21,22].…”

Section: Introductionmentioning

confidence: 99%

Promoting information spreading by using contact memory

Gao¹,

Wang²,

Shu³

et al. 2017

EPL

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Promoting information spreading is a booming research topic in network science community. However, the exiting studies about promoting information spreading seldom took into account the human memory, which plays an important role in the spreading dynamics. In this paper we propose a non-Markovian information spreading model on complex networks, in which every informed node contacts a neighbor by using the memory of neighbor's accumulated contact numbers in the past. We systematically study the information spreading dynamics on uncorrelated configuration networks and a group of 22 real-world networks, and find an effective contact strategy of promoting information spreading, i.e., the informed nodes preferentially contact neighbors with small number of accumulated contacts. According to the effective contact strategy, the high degree nodes are more likely to be chosen as the contacted neighbors in the early stage of the spreading, while in the late stage of the dynamics, the nodes with small degrees are preferentially contacted. We also propose a mean-field theory to describe our model, which qualitatively agrees well with the stochastic simulations on both artificial and real-world networks.

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“…In this approach, individuals are represented as nodes on a network and their interactions by edges [13][14][15]. Analytical solutions arising from the graph theory [16,17] and percolation [18,19] or simulations can be used to answer questions concerning the potential for a particular disease to invade the population and persist there [20,21], the relationship between the network structure and rate of spread [22][23][24], the future course of an unfolding epidemic [25], and, finally, to assess control strategies that either prevent the disease from invading [26] or aim at its eradication [27][28][29]. Network models are particularly suitable for the latter task, as they allow to represent spatial aspects of the disease spread [30,31] and, therefore, help in designing responsive and local control strategies that target particular individuals or their connections [32].…”

Section: Introductionmentioning

confidence: 99%

Cost-benefit Analysis of Epidemics Spreading on Clustered Random Networks

Oleś¹,

Gudowska–Nowak²,

Kleczkowski³

2014

Acta Phys. Pol. B

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We study, control of infectious disease epidemics spreading on random networks with different levels of clustering. We use Gleeson's et al., Phys. Rev. E80, 036107 (2009) algorithm to create clustered networks in which a proportion of individuals is located in fully-connected cliques of certain size. A SIR model is extended to include delayed and imperfect detection of infectious individuals. We also include a combination of responsive (palliative) and preventive (vaccination) treatments and design cost-effective disease control strategies. Cost-benefit analysis is used in combination with epidemiological simulations to identify an optimal radius for a treatment centred upon the symptomatic individual. Three general control strategies occur depending on the relative cost of treatment and prevention. Network topology and, in particular, clustering also affects the applicability of the control strategy. The average path length appears to be more important; the range for the control strategy is wider with the length, but the optimal radius of control also extends. As the proportion of individuals in cliques and therefore the coefficient of clustering is higher, the range of the costs for which control scenario is optimal is greater. This results have important consequences for designing disease control strategies that also satisfy economic optimality criteria.

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Slow epidemic extinction in populations with heterogeneous infection rates

Cited by 51 publications

References 26 publications

Epidemic spreading on complex networks with general degree and weight distributions

Epidemic spreading on complex networks with general degree and weight distributions

Promoting information spreading by using contact memory

Cost-benefit Analysis of Epidemics Spreading on Clustered Random Networks

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