Evolutionary vaccination dilemma in complex networks

Cardillo, Alessio; Suàrez, Nydia Catalina Reyes; Naranjo, Fernando; Gómez‐Gardeñes, Jesús

doi:10.1103/physreve.88.032803

Cited by 83 publications

(59 citation statements)

References 48 publications

(56 reference statements)

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“…However, if the vaccine is imperfect, a crossover effect occurs such that the ER graphs are more effective than SF networks in enhancing vaccination. In [526], these results are mainly attributed to the competition of two processes taking place. One is the propagation of the diseases, and second is the uptake of vaccination behavior.…”

Section: Impact Of Interaction Network Topologymentioning

confidence: 99%

“…Despite of the inspiring progress, there does not exist a direct comparison between a perfect and an imperfect vaccine in [525]. Aiming to clarify this, Cardillo et al introduced a tunable parameter τ to modulate the quality of the vaccine in influenza-type disease, the vaccine being perfect at τ = 0 and useless at τ = 1 [526]. When the vaccine is perfect, the research revealed that SF networks outperform ER graphs, since the overall vaccinated (infected) number is larger (smaller) on SF networks.…”

Section: Impact Of Interaction Network Topologymentioning

confidence: 99%

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Statistical physics of vaccination

et al. 2016

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Historically, infectious diseases caused considerable damage to human societies, and they continue to do so today. To help reduce their impact, mathematical models of disease transmission have been studied to help understand disease dynamics and inform prevention strategies. Vaccination-one of the most important preventive measures of modern times-is of great interest both theoretically and empirically. And in contrast to traditional approaches, recent research increasingly explores the pivotal implications of individual behavior and heterogeneous contact patterns in populations. Our report reviews the developmental arc of theoretical epidemiology with emphasis on vaccination, as it led from classical models assuming homogeneously mixing (mean-field) populations and ignoring human behavior, to recent models that account for behavioral feedback and/or population spatial/social structure. Many of the methods used originated in statistical physics, such as lattice and network models, and their associated analytical frameworks. Similarly, the feedback loop between vaccinating behavior and disease propagation forms a coupled nonlinear system with analogs in physics. We also review the new paradigm of digital epidemiology, wherein sources of digital data such as online social media are mined for high-resolution information on epidemiologically relevant individual behavior. Armed with the tools and concepts of statistical physics, and further assisted by new sources of digital data, models that capture nonlinear interactions between behavior and disease dynamics offer a novel way of modeling real-world phenomena, and can help improve health outcomes. We conclude the review by discussing open problems in the field and promising directions for future research.

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Section: Impact Of Interaction Network Topologymentioning

confidence: 99%

Section: Impact Of Interaction Network Topologymentioning

confidence: 99%

Statistical physics of vaccination

et al. 2016

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“…Such human behavioral responses can have significant effects on the epidemic dynamics, 1-5 a topic of great recent interest. [6][7][8][9][10][11][12][13][14] The individual reactions to an epidemic often rely on detailed information about the disease. Broadly, there are two types of information: local or global.…”

Section: Introductionmentioning

confidence: 99%

Suppression of epidemic spreading in complex networks by local information based behavioral responses

Zhang

Xie

Tang

et al. 2014

Chaos: An Interdisciplinary Journal of Nonlinear Science

115

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The interplay between individual behaviors and epidemic dynamics in complex networks is a topic of recent interest. In particular, individuals can obtain different types of information about the disease and respond by altering their behaviors, and this can affect the spreading dynamics, possibly in a significant way. We propose a model where individuals' behavioral response is based on a generic type of local information, i.e., the number of neighbors that has been infected with the disease. Mathematically, the response can be characterized by a reduction in the transmission rate by a factor that depends on the number of infected neighbors. Utilizing the standard susceptible-infected-susceptible and susceptible-infected-recovery dynamical models for epidemic spreading, we derive a theoretical formula for the epidemic threshold and provide numerical verification. Our analysis lays on a solid quantitative footing the intuition that individual behavioral response can in general suppress epidemic spreading. Furthermore, we find that the hub nodes play the role of “double-edged sword” in that they can either suppress or promote outbreak, depending on their responses to the epidemic, providing additional support for the idea that these nodes are key to controlling epidemic spreading in complex networks.

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“…In other studies, it has been argued that efficient immunization strategies can be developed by considering the higher-order organization of connectivity patterns [57,69,70,71,72,73,74]. Further studies proposed vaccination strategies using evolutionary games [75], or considering complex contagion processes [76]. For a recent review on the subject we refer the readers to Ref.…”

Section: Introductionmentioning

confidence: 99%

Control Strategies of Contagion Processes in Time-Varying Networks

Karsai

Perra

2017

Theoretical Biology

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The vast majority of strategies aimed at controlling contagion processes on networks consider a timescale separation between the evolution of the system and the unfolding of the process. However, in the real world, many networks are highly dynamical and evolve, in time, concurrently to the contagion phenomena. Here, we review the most commonly used immunization strategies on networks. In the first part of the chapter, we focus on controlling strategies in the limit of timescale separation. In the second part instead, we introduce results and methods that relax this approximation. In doing so, we summarize the main findings considering both numerical and analytically approaches in real as well as synthetic time-varying networks.

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Evolutionary vaccination dilemma in complex networks

Cited by 83 publications

References 48 publications

Statistical physics of vaccination

Statistical physics of vaccination

Suppression of epidemic spreading in complex networks by local information based behavioral responses

Control Strategies of Contagion Processes in Time-Varying Networks

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