Can rewiring strategy control the epidemic spreading?

We review behavioural change models (BCMs) for infectious disease transmission in humans. Following the Cochrane collaboration guidelines and the PRISMA statement, our systematic search and selection yielded 178 papers covering the period 2010–2015. We observe an increasing trend in published BCMs, frequently coupled to (re)emergence events, and propose a categorization by distinguishing how information translates into preventive actions. Behaviour is usually captured by introducing information as a dynamic parameter (76/178) or by introducing an economic objective function, either with (26/178) or without (37/178) imitation. Approaches using information thresholds (29/178) and exogenous behaviour formation (16/178) are also popular. We further classify according to disease, prevention measure, transmission model (with 81/178 population, 6/178 metapopulation and 91/178 individual-level models) and the way prevention impacts transmission. We highlight the minority (15%) of studies that use any real-life data for parametrization or validation and note that BCMs increasingly use social media data and generally incorporate multiple sources of information (16/178), multiple types of information (17/178) or both (9/178). We conclude that individual-level models are increasingly used and useful to model behaviour changes. Despite recent advancements, we remain concerned that most models are purely theoretical and lack representative data and a validation process.

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“…[13], Dong et al . [143], Schimit & Monteiro [158], Wells et al . [159], Morsky & Bauch [160], Ruan et al .…”

Section: Resultsmentioning

confidence: 99%

“…We retrieved 26 papers [13,20,27–31,33,37–39,44,46,54,69,89,91,93,137,143,150,152,154,156,159,164] describing a BCM with an objective function with imitation. It is recognized that some social or peer influence should be incorporated in the decision-making process of the individuals (see also models with information as a dynamic parameter).…”

Section: Resultsmentioning

confidence: 99%

Behavioural change models for infectious disease transmission: a systematic review (2010–2015)

Verelst

Willem

Beutels

2016

J. R. Soc. Interface.

311

298

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“…Numerous studies [5]- [8] have highlighted the impact of the network structure on epidemic dissemination. These results have raised a growing concern about topology manipulation to curb the spreading of a disease in large-sized networks.…”

Section: Introductionmentioning

confidence: 99%

“…In realistic scenarios, for instance, individuals possessing more resources can afford more doctors or receive better medical treatment. Another method includes link removal [2], [5], [7], [10] and relocation [8], [11], [12]. The traditional approaches for shaping network structure are to execute independently of the states of dynamical processes occurring on networks.…”

Section: Introductionmentioning

confidence: 99%

Distributed Topology Manipulation to Control Epidemic Spreading Over Networks

Xue

Hirche

2019

IEEE Trans. Signal Process.

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This article considers the problem of network structure manipulation in the absence of a central coordinator which collects network information and makes decisions. The primary focus is on the spectral radius minimization problem by removing/rewiring links to control epidemic spreading over networks. The resulting optimizations are generally combinatorial and NP-hard. The lack of the central base prevents us from solving such problems in a centralized fashion, thus driving the need for distributed computation and collaborative decision-making to support effective topology modification. In this article, a distributed estimation scheme involving event-based communication and parallel algorithms is developed to enhance network capability against epidemics. Based on this low-complexity estimation algorithm, fully distributed strategies are proposed to enable individuals to sequentially discern the critical in-network contact, and to implement the desired link operation solely. We further extend the acquisitions to a more flexible framework based on a "coevolutionary networks" picture and discuss the optimality of solutions from an algebraic and topological perspective. Extensive simulation examples are presented to demonstrate the effectiveness of the proposed strategies. Index Termsepidemic spread, distributed computation, link operation, event-based control.

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“…The spontaneous protection of the public, combined with some compulsory measures implemented by governments or departments of health and hygiene, effectively controlled the spread of the disease [1][2][3][4][5][6][7]. about the researches on the effect of individual behavior on the transmission of disease attracted much attention [8][9][10][11][12][13][14][15][16][17]. For example, Funk et al studied the effect of locally spreading awareness on the transmission of disease, and they found that in a well-mixed population, the spread of awareness can reduce the size of the outbreak, but cannot enhance the transmission of threshold [18].…”

Section: Introductionmentioning

confidence: 99%

Effects of asymptomatic infection on the dynamical interplay between behavior and disease transmission in multiplex networks

Shi

Long

Pan

et al. 2019

Physica A: Statistical Mechanics and its Applications

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h i g h l i g h t s• We study the impact of asymptomatic infection on the epidemic spread dynamics in multiplex networks.• We assume infected can be isolation and non isolation, then compare the research results of these two cases.• We take the individual heterogeneity into consideration and study whether it affect research results. a b s t r a c tMultiplex network theory is widely introduced to deepen the understanding of the dynamical interplay between self-protective behavior and epidemic spreading. Most of the existing studies assumed that all infected individuals can transmit diseaserelated information or can be perceived by their neighbors. However, owing to lack of distinct symptoms for patients in the initial stage of infection, the disease information cannot be transmitted in the population, which may lead to the wrong perception of infection risk and inappropriate behavior response. In this work, we divide infected individuals into Exposed-state (without obvious clinical symptoms) individuals and Infected-state (with evident clinical symptoms) individuals, both of whom can spread disease, but only Infected-state individuals can diffuse disease information. Then, in this work we establish UAU-SEIS (Unaware-Aware-Unaware-Susceptible-Exposed-Infected-Susceptible) model in multiplex networks and analyze the effect of asymptomatic infection and the isolation of Infected-state individuals on the density of infection and the epidemic threshold. Furthermore, we extend the UAU-SEIS model by taking the individual heterogeneity into consideration. Combined Markov chain approach and Monte-Carlo Simulations, we find that asymptomatic infection has an effect on the density of infected individuals and the epidemic threshold, and the extent of the effect is influenced by whether Infected-state individuals are isolated or treated. In addition, results show that the individual heterogeneity can lower the density of infected individuals, but cannot enhance the epidemic threshold.

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Can rewiring strategy control the epidemic spreading?

Cited by 14 publications

References 50 publications

Behavioural change models for infectious disease transmission: a systematic review (2010–2015)

Behavioural change models for infectious disease transmission: a systematic review (2010–2015)

Distributed Topology Manipulation to Control Epidemic Spreading Over Networks

Effects of asymptomatic infection on the dynamical interplay between behavior and disease transmission in multiplex networks

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