Epidemic process on activity-driven modular networks

Han, Dun; Sun, Mei; Li, Dandan

doi:10.1016/j.physa.2015.03.062

Cited by 28 publications

(21 citation statements)

References 40 publications

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“…The largest category embodies 76 references managing information as a dynamic parameter [3,25,34,40,43,47–50,53,55,60,61,65–68,70,71,74,77,80,82,86,92,94–96,99,100,102,104–109,113,115–117,119–126,128–131,133,134,137,148,149,153,157,161,165,166,168,171–178,184–186,188,189]. In this category, instead of a threshold, the information is a continuous input in the decision-making process of individuals.…”

Section: Resultsmentioning

confidence: 99%

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

Verelst

Willem

Beutels

2016

J. R. Soc. Interface.

324

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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Section: Resultsmentioning

confidence: 99%

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

Verelst

Willem

Beutels

2016

J. R. Soc. Interface.

324

311

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“…In other words, the mechanisms driving the formation of contacts at the time-scale of minutes are different than those at the time scale of months [675]. This is of particular importance when studying the spreading of infectious diseases [230,593,596,684,[686][687][688][689][690][691][692][693][694][695][696][697][698][699][700][701][702][703][704]. Indeed, contacts dynamics that affect the spreading of transmissible illnesses are those unfolding at comparable time-scale respect to the disease [219,652,676,686].…”

Section: Measuring and Understanding Close Proximity Interactionsmentioning

confidence: 99%

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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“…In Ref. [36], the exponents are taken γ ∈ [2,3], however, in this paper we used a deterministic power law distribution F (α) ∝ α −γ with γ = 1.63 and the lower cutoff ξ = 0.01. We start our discussion by briefly reporting the behavior of the epidemic model when no vaccination is implemented.…”

Section: Resultsmentioning

confidence: 99%

“…Infectious diseases, such as the MERS (Middle East Respiratory Syndrome), smallpox and human immunodeficiency virus, continue to significantly impact morbidity, mortality, and economic outcomes in many population [1][2][3][4][5]. The primary intervention method for preventing transmission of infectious diseases is vaccination.…”

Section: Introductionmentioning

confidence: 99%

An evolutionary vaccination game in the modified activity driven network by considering the closeness

Han

Sun

2016

Physica A: Statistical Mechanics and its Applications

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h i g h l i g h t s• The evolutionary vaccination game is considered in a modified activity driven network. • A closeness parameter p which is used to describe the connection between individuals is presented. • The closeness p may have an active role in weakening both the spreading of epidemic and the vaccination.• Infected frequency increases with the increase of the individuals' activity.• Results indicate some better hope (such as reducing the connection) may lead to backfire. a b s t r a c tIn this paper, we explore an evolutionary vaccination game in the modified activity driven network by considering the closeness. We set a closeness parameter p which is used to describe the way of connection between two individuals. The simulation results show that the closeness p may have an active role in weakening both the spreading of epidemic and the vaccination. Besides, when vaccination is not allowed, the final recovered density increases with the value of the ratio of the infection rate to the recovery rate λ/µ. However, when vaccination is allowed the final density of recovered individual first increases and then decreases with the value of λ/µ. Two variables are designed to identify the relation between the individuals' activities and their states. The results draw that both recovered and vaccinated frequency increase with the increase of the individuals' activities. Meanwhile, the immune fee has less impact on the individuals' vaccination than the closeness.While the λ/µ is in a certain range, with the increase of the value of λ/µ, the recovered frequency of the whole crowds reduces. Our results, therefore, reveal the fact that the best of intentions may lead to backfire.

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Epidemic process on activity-driven modular networks

Cited by 28 publications

References 40 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)

Statistical physics of vaccination

An evolutionary vaccination game in the modified activity driven network by considering the closeness

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