Community Structure in Social Networks: Applications for Epidemiological Modelling

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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“…[181], Liu et al . [182], Chen et al [183]Goyal & Vigier [179], Kitchovitch and Lio [184,185], Ni et al . [186], Rutherford et al .…”

Section: Resultsmentioning

confidence: 99%

“…[42], Larson & Nigmatulina [49], Barrett et al . [58], Kassa & Ouhinou [82], Maharaj & Kleczkowski [134], Schimit & Monteiro [158], Kitchovitch & Lio [184,185], Shang [188]Tully et al . [75], Wells et al .…”

Section: Resultsmentioning

confidence: 99%

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

Verelst

Willem

Beutels

2016

J. R. Soc. Interface.

326

311

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“…Despite its wide use, the core-periphery concept still lacks a formal definition for the separation of the core from the periphery, though a number of intuitive understandings have been mentioned in the literature (e.g., Breiger 1981, Wasserman and Faust 1994, Scott 2013, Rombach et al 2014) and reviewed by Borgatti and Everett (2000). Empirically supported examples of the core-periphery concept include networks of disease dynamics (Kitchovitch and Liò 2011), spatial group interaction (Onnela et al 2011), scientific collaboration and citations (Mullins et al 1977, Newman 2004, friendship (Adaic and Adar 2003), advice in the workplace (Cross et al 2001), and interlocking directorates of corporations (Mizruchi 1996).…”

Section: Network Structure and Governancementioning

confidence: 99%

Resilience to climate change in a cross-scale tourism governance context: a combined quantitative-qualitative network analysis

Luthe¹,

Wyss²

2016

E&S

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Resilience to climate change in a cross-scale tourism governance context: a combined quantitativequalitative network analysis. Ecology and Society 21 (1) ABSTRACT. Social systems in mountain regions are exposed to a number of disturbances, such as climate change. Calls for conceptual and practical approaches on how to address climate change have been taken up in the literature. The resilience concept as a comprehensive theory-driven approach to address climate change has only recently increased in importance. Limited research has been undertaken concerning tourism and resilience from a network governance point of view. We analyze tourism supply chain networks with regard to resilience to climate change at the municipal governance scale of three Alpine villages. We compare these with a planned destination management organization (DMO) as a governance entity of the same three municipalities on the regional scale. Network measures are analyzed via a quantitative social network analysis (SNA) focusing on resilience from a tourism governance point of view. Results indicate higher resilience of the regional DMO because of a more flexible and diverse governance structure, more centralized steering of fast collective action, and improved innovative capacity, because of higher modularity and better core-periphery integration.Interpretations of quantitative results have been qualitatively validated by interviews and a workshop. We conclude that adaptation of tourism-dependent municipalities to gradual climate change should be dealt with at a regional governance scale and adaptation to sudden changes at a municipal scale. Overall, DMO building at a regional scale may enhance the resilience of tourism destinations, if the municipalities are well integrated.

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“…First, a general result from previous works is that the local information-based responses can enhance the epidemic threshold and reduce its prevalence, but global informationbased awareness, although being capable of altering the epidemic size, has little effect on the threshold. 6,24,25 In these works on the interplay between epidemic spreading in complex networks and human behavioral responses, a tacit hypothesis [24][25][26]28 is that local information-based behavioral response is a function of the density of infection among the local neighborhood, denoted as s/k, where s is the number of infected neighbors among a total of k neighbors. However, simple situations can be conceived where this assumption does not hold.…”

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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Community Structure in Social Networks: Applications for Epidemiological Modelling

Cited by 34 publications

References 38 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)

Resilience to climate change in a cross-scale tourism governance context: a combined quantitative-qualitative network analysis

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

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