Towards a Characterization of Behavior-Disease Models

Perra, Nicola; Balcan, Duygu; Gonçalves, Bruno; Vespignani, Alessandro

doi:10.1371/journal.pone.0023084

Cited by 240 publications

(240 citation statements)

References 46 publications

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“…We also note that our goal here is to characterize different ways of doing behavioral modelling, rather than different ways of modelling coupled behavior-disease interactions per se (see Refs. [405,406] for classifications of coupled behavior-disease models).…”

Section: Basic Concepts In Behavioral Modelingmentioning

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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Section: Basic Concepts In Behavioral Modelingmentioning

confidence: 99%

Statistical physics of vaccination

et al. 2016

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“…15 For example, news obtained from the social neighborhood of an individual is local, but information from the mass media or from the public health authorities can be regarded as global. The influences of local [16][17][18][19][20] or global [21][22][23] information based behavioral responses, or awareness, on the epidemic dynamics can in general be quite different, and there is also recent work on a)…”

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

117

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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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“…Finally, a discontinuous transition in the number of infected individuals at the end of the epidemic is observed as a function of the transmissibility of fear of the disease contagion. The results of this study have been collected in paper published on the interdisciplinary journal [38]. The second line of work concerned the coupling of behavioral changes of the population with the large-scale network describing the mobility of people across location in the USA.…”

Section: ) Resilience Of Structured Population Networkmentioning

confidence: 99%

“…80, pp. [37][38][39][40][41][42][43][44][45]2013. Abstract: Numerous studies have investigated inter-areal cortical networks using either diffusion MRI or axonal tract-tracing.…”

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confidence: 99%

Understanding the Fundamental Principles Underlying the Survival and Efficient Recovery of Multi-Scale Techno-Social Networks Following a WMD Event (A)

Toroczkai¹,

Vespignani²

2016

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The public reporting burden for this collection of information is estimated to average 1 hour per respon se. including the time for reviewing instructions, searching existing data sources, gathering and maintaining the data needed, and completing and reviewing the collection of information . Send comments regarding this burden estimate or any other aspect of this collection of information. including suggestions for reducing the burden, to Department of Defense, Washington Headquarters Services, Directorate for Information . 1215 Jefferson Davis Highway, Sutte 1204, Arlington, VA 22202-4302. Respondents should be aware that notwithstand ing any other provision of law, no person shall be subject to any penalty for failing to comply with a collection of information if it does not display a currentiy valid OMB control number. PLEASE DO NOT RETURN YOUR FORM TO THE ABOVE ADDRESS. REPORT DATE (DD-MM· I. Executive summaryMotivation: Understanding the fundamental principles underlying the functional robustness of Techno-Social Networks (TSN-s) is a tall, but inevitable challenge. As the society becomes increasingly dependent on infrastructure transport networks, the implications of large-scale disruptions (e.g., due to WMD events) of these systems also become increasingly important. In our ever more connected and interdependent world, small perturbations can have far reaching effects. Large-scale events, such as cascading failures (e.g., the East coast blackout on Aug. 14, 2003) or epidemics (SARS and most recently Ebola) are typical to complex networks, and they serve as warnings about the type of behaviors one can expect in strongly interconnected systems. As a result, we demand ever-increasing predictive power and ability to evaluate the impact of potential failures. There has been, however, little to no mathematical and analytical methodology that would allow a realistic assessment of the consequences of large-scale failures in critical infrastructure TSN-s, largely due to two major aspects of these networks: i) Multivariate and heterogeneous constraints. Real-world TSNs typically operate under a wide range of constraints and limitations imposed both from the technical (physical) side and the social (usage) side. Physical constraints include geographical embeddedness; energy limitations (transportation costs, battery life); device capacity, latency and bandwidth, while usage constraints include software limitations, communication protocols and regulations. To advance empirical applications, network models will need to address these constraints since they strongly influence the response and failure modes of a network in case of WMDs. The dynamics of constrained complex networks, especially if they are directed, weighted and hierarchical are not well understood. The adaptive capabilities, including recovery of TSNs facing major destructive events has been a relatively uncharted territory. ii) Multiscale nature. Most of the TSNs are characterized by inherently complex processes involving many different scales in time a...

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Towards a Characterization of Behavior-Disease Models

Cited by 240 publications

References 46 publications

Statistical physics of vaccination

Statistical physics of vaccination

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

Understanding the Fundamental Principles Underlying the Survival and Efficient Recovery of Multi-Scale Techno-Social Networks Following a WMD Event (A)

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