Risk assessment for infectious disease and its impact on voluntary vaccination behavior in social networks

Fukuda, Eriko; Kokubo, Satoshi; Tanimoto, Jun; Wang, Zhen; Hagishima, Aya; Ikegaya, Naoki

doi:10.1016/j.chaos.2014.07.004

Cited by 100 publications

(106 citation statements)

References 48 publications

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“…[37], Xia & Liu [38], Fukuda et al . [39], Liao & You [40], Andrews & Bauch [41]Marathe et al . [42], Mei et al .…”

Section: Resultsmentioning

confidence: 99%

“…[33], Fukuda et al . [39], Reniers & Armbruster [78], Althouse & Hébert-Dufresne [88], Sahneh & Scoglio [122,175], Cao [125], Zhang et al . [126], Schumm 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%

See 2 more Smart Citations

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

Verelst

Willem

Beutels

2016

J. R. Soc. Interface.

340

311

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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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“…[37], Xia & Liu [38], Fukuda et al . [39], Liao & You [40], Andrews & Bauch [41]Marathe et al . [42], Mei et al .…”

Section: Resultsmentioning

confidence: 99%

“…[33], Fukuda et al . [39], Reniers & Armbruster [78], Althouse & Hébert-Dufresne [88], Sahneh & Scoglio [122,175], Cao [125], Zhang et al . [126], Schumm et al .…”

Section: Resultsmentioning

confidence: 99%

See 1 more Smart Citation

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

Verelst

Willem

Beutels

2016

J. R. Soc. Interface.

340

311

View full text Add to dashboard Cite

show abstract

“…Thus far, many achievements have shown that considering simultaneous diffusion of disease and prevention measures on the same single-layer network is an effective method to evaluate the incidence and onset of disease [94,85,95,96,97,98,114,79]. However, if both processes are coupled into the multilayer infrastructure, how does it affect the spreading and prevention of disease?…”

Section: Dynamics In Multilayer Networkmentioning

confidence: 97%

“…As the above subsection shows, research on disease-behavior dynamics on networks has become one of the most fruitful realms of statistical physics and non-linear science, as well as shedding novel light on how to predict the impact of individual behavior on disease spread and prevention [92,93,94,85,95,96,97,98,99,79]. However, in some scenarios, the simple hypothesis that individuals are connected to each other in the same infrastructure (namely, the so-called single-layer network in section 3.1) may generate overestimation or underestimation for the diffusion and prevention of disease, since agents can simultaneously be the elements of more than one network in most, yet not all, empirical systems [29,28,100].…”

Section: Dynamics In Multilayer Networkmentioning

confidence: 99%

Coupled disease–behavior dynamics on complex networks: A review

Wang

Andrews

et al. 2015

Physics of Life Reviews

Self Cite

463

304

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It is increasingly recognized that a key component of successful infection control efforts is understanding the complex, two-way interaction between disease dynamics and human behavioral and social dynamics. Human behavior such as contact precautions and social distancing clearly influence disease prevalence, but disease prevalence can in turn alter human behavior, forming a coupled, nonlinear system. Moreover, in many cases, the spatial structure of the population cannot be ignored, such that social and behavioral processes and/or transmission of infection must be represented with complex networks. Research on studying coupled disease-behavior dynamics in complex networks in particular is growing rapidly, and frequently makes use of analysis methods and concepts from statistical physics. Here, we review some of the growing literature in this area. We contrast network-based approaches to homogeneous-mixing approaches, point out how their predictions differ, and describe the rich and often surprising behavior of disease-behavior dynamics on complex networks, and compare them to processes in statistical physics. We discuss how these models can capture the dynamics that characterize many real-world scenarios, thereby suggesting ways that policy makers can better design effective prevention strategies. We also describe the growing sources of digital data that are facilitating research in this area. Finally, we suggest pitfalls which might be faced by researchers in the field, and we suggest several ways in which the field could move forward in the coming years.

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A Vaccination Game for Mitigation Active Worms Propagation in P2P Networks

Rguibi

Moussa

2019

Networked Systems

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Risk assessment for infectious disease and its impact on voluntary vaccination behavior in social networks

Cited by 100 publications

References 48 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)

Coupled disease–behavior dynamics on complex networks: A review

A Vaccination Game for Mitigation Active Worms Propagation in P2P Networks

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