Crossover from weak to strong disorder regime in the duration of epidemics

Buono, Camila; Lagorio, C.; Macri, P. A.; Braunstein, Lidia A.

doi:10.1016/j.physa.2012.04.002

Cited by 7 publications

(12 citation statements)

References 36 publications

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“…33 Using different methods like broadcasting, brochures or masks distributions, the public health agencies can induce people to change their effective contact time and therefore the heterogeneity of the interactions. This strategy was tacitly used by some governments in the recent wave of influenza A(H1N1) epidemic in 2009, but until now the effectiveness of the strategy and how it depends on the virulence and the structure of the disease has not been widely studied.…”

Section: Social Distancing Induced By Quenched Disordermentioning

confidence: 99%

Social distancing strategies against disease spreading

Valdez

Buono

Macri

et al. 2014

Perspectives and Challenges in Statistical Physics and Complex Systems for the Next Decade

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The recurrent infectious diseases and their increasing impact on the society has promoted the study of strategies to slow down the epidemic spreading. In this review we outline the applications of percolation theory to describe strategies against epidemic spreading on complex networks. We give a general outlook of the relation between link percolation and the susceptible-infected-recovered model, and introduce the node void percolation process to describe the dilution of the network composed by healthy individual, i.e, the network that sustain the functionality of a society. Then, we survey two strategies: the quenched disorder strategy where an heterogeneous distribution of contact intensities is induced in society, and the intermittent social distancing strategy where health individuals are persuaded to avoid contact with their neighbors for intermittent periods of time. Using percolation tools, we show that both strategies may halt the epidemic spreading. Finally, we discuss the role of the transmissibility, i.e, the effective probability to transmit a disease, on the performance of the strategies to slow down the epidemic spreading.

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Section: Social Distancing Induced By Quenched Disordermentioning

confidence: 99%

Social distancing strategies against disease spreading

Valdez

Buono

Macri

et al. 2014

Perspectives and Challenges in Statistical Physics and Complex Systems for the Next Decade

Self Cite

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“…On the other hand, we expect that ℓ subcluster ∼t b [34], so finally Integrating the equations of time evolution for the stochastic regime for TT c (r=0) (see appendix C) we obtain that t b as a function of r has two regimes, which are separated by a crossover at r=r * (see figures 4(a) and (b)). For r>r * we obtain that t r d b 1 l as in the previous section.…”

Section: Model and Dynamic Equationsmentioning

confidence: 93%

The role of bridge nodes between layers on epidemic spreading

et al. 2018

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Real networks, like the international airport network and the Internet, are composed of interconnected layers (or communities) through a small fraction of nodes that we call here 'bridge nodes'. These nodes are crucial in the spreading of epidemics because they enable the spread the disease to the entire system. In this work we study the effect of the bridge nodes on the susceptibleinfected-recovered model in a two layer network with a small fraction r of these nodes. In the dynamical process, we theoretically determine that at criticality and for the limit r→0, the time t b at which the first bridge node is infected diverges as a power-law with r, while above criticality, it appears a crossover between a logarithmic and a power-law behavior. Additionally, in the steady state at criticality, the fraction of recovered nodes scales with r as a power-law whose exponent can be understood from the finite size cluster distribution at criticality. We also test our model on the real international airline network and show that 'high-degree bridge nodes' reduce the time t b .

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“…We also assume that the contact times are heterogeneous and hence we use a weighted network, in which links are characterized by weights βω. As in "face-to-face" experiments [1], in which contact times follow a power law distribution, we take ω from a theoretical distribution of contact times P (ω) = 1/aω, where ω ǫ [e −a , 1] [29,34]. The parameter a is the disorder intensity and controls the width of the distribution.…”

Section: Model and Simulationsmentioning

confidence: 99%

“…This heterogeneity ("disorder") in the interactions is modeled using weighted complex networks, in which weights depend on the normalized contact times ω of the interactions. Previous SIR model research on weighted complex networks [29,34], takes values for ω from a theoretical power law distribution, with broadness a, that mimics the results of "face-to-face" experiments [1,2]. The larger the parameter a, the shorter the contact times.…”

Section: Introductionmentioning

confidence: 99%

Controlling distant contacts to reduce disease spreading on disordered complex networks

Perez

Trunfio

Rocca

et al. 2020

Physica A: Statistical Mechanics and its Applications

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Disorder is a characteristic of real social networks generated by heterogeneity in person-to-person interactions. This property affects the way a disease spreads through a population, reaches a tipping point in the fraction of infected individuals, and becomes an epidemic. Disorder is usually associated with contact times between individuals, and normalized contact time values ω are taken from the distribution P (ω) = 1/(aω) that mimics "face-to-face" experiments [1,2]. To model more realistic systems, we study how heterogeneity in person-to-person interactions affects the spreading of diseases when two different kinds of disorder are present, each with a particular value of a. This allows two different types of interaction to emerge, such as close (family, coworkers) and distant (neighbors, strangers) contacts. We also develop a strategy for controlling distant contact times, which are easier to alter in practice, so as to reduce the total number of infected individuals.Finally, we use "face-to-face" experiments to generate a more accurate distribution of normalized contact times, and we repeat the analysis for this distribution.

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Crossover from weak to strong disorder regime in the duration of epidemics

Cited by 7 publications

References 36 publications

Social distancing strategies against disease spreading

Social distancing strategies against disease spreading

The role of bridge nodes between layers on epidemic spreading

Controlling distant contacts to reduce disease spreading on disordered complex networks

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