Braess's Paradox in Epidemic Game: Better Condition Results in Less Payoff

Hai-feng, Zhang; Yang, Zimo; Wu, Zhi-Xi; Wang, Bing–Hong; Zhou, Tao

doi:10.1038/srep03292

Cited by 82 publications

(78 citation statements)

References 44 publications

(79 reference statements)

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“…Here, C R denotes the cost of infection to a partially resistant (immune) agent, while o k (α k ) is the perceived probability of becoming infected if vaccinated (non-vaccinated). Thus, different from respective expressions of discrete strategies [433,525,547], the second term in the right hand side of Eq. 231 is the perceived payoff of vaccination selection, and the third accounts for the perceived payoff of non-vaccinated behavior.…”

Section: How Does Feedback Influence Well-known Passive and Pro-activmentioning

confidence: 92%

“…The superscripts H and I denote healthy and infected states, while w is the probability of a susceptible to become infected. For more details, please refer to [547].…”

Section: Multiple-strategy Dilemma Framework: a Counter-intuitive Obsmentioning

confidence: 99%

“…neither vaccination nor self-protection), were considered in the realm of an evolutionary epidemic game [547].…”

Section: Multiple-strategy Dilemma Framework: a Counter-intuitive Obsmentioning

confidence: 99%

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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: How Does Feedback Influence Well-known Passive and Pro-activmentioning

confidence: 92%

“…The superscripts H and I denote healthy and infected states, while w is the probability of a susceptible to become infected. For more details, please refer to [547].…”

Section: Multiple-strategy Dilemma Framework: a Counter-intuitive Obsmentioning

confidence: 99%

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Statistical physics of vaccination

et al. 2016

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“…[87] is that we might never guess what would happen by just looking at the decision-making rules alone, in particular when our choices will influence, and be influenced by, the choice of other people. Another good example can be found in a recent work [89], in which Zhang et al proposed an evolutionary epidemic game where individuals can choose their strategies as vaccination, self-protection or laissez faire, towards infectious diseases and adjust their strategies according to their neighbors' strategies and payoffs. The "disease-behavior" coupling dynamical process is similar to the one implemented by Ref.…”

Section: Dynamics On Lattices and Static Networkmentioning

confidence: 99%

Coupled disease–behavior dynamics on complex networks: A review

Wang

Andrews

et al. 2015

Physics of Life Reviews

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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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“…Bauch and Earn [4] investigated the impacts of vaccination policy on the level of vaccination coverage and found that voluntary vaccination was unlikely to reach the group-optimal level. Zhang et al [37] studied the epidemic spreading with voluntary vaccination strategy on both Erdos-Renyi random graphs and Barabasi-Albert scale-free networks and Zhang et al [36] investigated effects of behavioral response and vaccination policy on epidemic spreading. The transmission dynamics of measles epidemics have been extensively studied.…”

Section: Introductionmentioning

confidence: 99%