Epidemic spreading and risk perception in multiplex networks: A self-organized percolation method

Massaro, Emanuele; Bagnoli, Franco

doi:10.1103/physreve.90.052817

Cited by 84 publications

(56 citation statements)

References 23 publications

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“…Others model populations as multiplex networks where the disease spreads over one layer and awareness spreads over another [7]. The influence of the perception of risk on the probability of infection also has been studied [8]. Several recent studies have shown how information spreads in complex networks [9,10].…”

Section: Introductionmentioning

confidence: 99%

Risk perception and behavioral change during epidemics: Comparing models of individual and collective learning

et al. 2020

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Modern societies are exposed to a myriad of risks ranging from disease to natural hazards and technological disruptions. Exploring how the awareness of risk spreads and how it triggers a diffusion of coping strategies is prominent in the research agenda of various domains. It requires a deep understanding of how individuals perceive risks and communicate about the effectiveness of protective measures, highlighting learning and social interaction as the core mechanisms driving such processes. Methodological approaches that range from purely physics-based diffusion models to data-driven environmental methods rely on agentbased modeling to accommodate context-dependent learning and social interactions in a diffusion process. Mixing agent-based modeling with data-driven machine learning has become popularity. However, little attention has been paid to the role of intelligent learning in risk appraisal and protective decisions, whether used in an individual or a collective process. The differences between collective learning and individual learning have not been sufficiently explored in diffusion modeling in general and in agent-based models of socioenvironmental systems in particular. To address this research gap, we explored the implications of intelligent learning on the gradient from individual to collective learning, using an agent-based model enhanced by machine learning. Our simulation experiments showed that individual intelligent judgement about risks and the selection of coping strategies by groups with majority votes were outperformed by leader-based groups and even individuals deciding alone. Social interactions appeared essential for both individual learning and group learning. The choice of how to represent social learning in an agent-based model could be driven by existing cultural and social norms prevalent in a modeled society.

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Section: Introductionmentioning

confidence: 99%

Risk perception and behavioral change during epidemics: Comparing models of individual and collective learning

et al. 2020

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“…When gauging infection risk, individuals may consider global information (e.g., from news media) or local first-hand encounters with disease (e.g., infected acquaintances, friends or family members) [30,31]. While traditional compartmental models assume homogeneity in both epidemiological risks and intervention benefits, network-based models provide a tractable framework for studying the complex interplay between contact networks, intervention decision making and disease transmission [34,[45][46][47][48][49][50][51].Here, we investigate the epidemiological impacts of different decision paradigms using a network-based SIR epidemic model, in which individuals also make vaccination or social distancing choices based on their perceived epidemiological risks. Depending on the decision model, they estimate either overall disease prevalence, their number of infected social contacts, or their fraction of infected social contacts.…”

mentioning

confidence: 99%

Local risk perception enhances epidemic control

Herrera-Diestra

Meyers

2019

PLoS ONE

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As infectious disease outbreaks emerge, public health agencies often enact vaccination and social distancing measures to slow transmission. Their success depends on not only strategies and resources, but also public adherence. Individual willingness to take precautions may be influenced by global factors, such as news media, or local factors, such as infected family members or friends. Here, we compare three modes of epidemiological decision-making in the midst of a growing outbreak using network-based mathematical models that capture plausible heterogeneity in human contact patterns. Individuals decide whether to adopt a recommended intervention based on overall disease prevalence, the proportion of social contacts infected, or the number of social contacts infected. While all strategies can substantially mitigate transmission, vaccinating (or self isolating) based on the number of infected acquaintances is expected to prevent the most infections while requiring the fewest intervention resources. Unlike the other strategies, it has a substantial herd effect, providing indirect protection to a large fraction of the population.

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“…Simulation studies often use networks based on the free-scale model proposed by Barabasi-Albert (BA) [35], small world model proposed by Watts-Strogatz (WS) [36] and random graph model introduced by Erdos-Renyi (ER) [37]. For example [38] used WS, ER and BA synthetic networks for modeling interacting processes, [12] analyzed the role of structures of ER, BA, and WS networks on campaign performance, [39] proposed a framework to analyze multiple spreading processes and verified it with the use of BA networks, [40] used WS networks for cooperative epidemic modeling.…”

Section: Methodsmentioning

confidence: 99%

Multi-criteria decision support for planning and evaluation of performance of viral marketing campaigns in social networks

2018

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The current marketing landscape, apart from conventional approaches, consists of campaigns designed especially for launching information diffusion processes within online networks. Associated research is focused on information propagation models, campaign initialization strategies and factors affecting campaign dynamics. In terms of algorithms and performance evaluation, the final coverage represented by the fraction of activated nodes within a target network is usually used. It is not necessarily consistent with the real marketing campaigns using various characteristics and parameters related to coverage, costs, behavioral patterns and time factors for overall evaluation. This paper presents assumptions for a decision support system for multi-criteria campaign planning and evaluation with inputs from agent-based simulations. The results, which are delivered from a simulation model based on synthetic networks in a form of decision scenarios, are verified within a real network. Last, but not least, the study proposes a multi-objective campaign evaluation framework with several campaign evaluation metrics integrated. The results showed that the recommendations generated with the use of synthetic networks applied to real networks delivered results according to the decision makers’ expectation in terms of the used evaluation criteria. Apart from practical applications, the proposed multi-objective approach creates new evaluation possibilities for theoretical studies focused on information spreading processes within complex networks.

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Epidemic spreading and risk perception in multiplex networks: A self-organized percolation method

Cited by 84 publications

References 23 publications

Risk perception and behavioral change during epidemics: Comparing models of individual and collective learning

Risk perception and behavioral change during epidemics: Comparing models of individual and collective learning

Local risk perception enhances epidemic control

Multi-criteria decision support for planning and evaluation of performance of viral marketing campaigns in social networks

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