Stochasticity of disease spreading derived from the microscopic simulation approach for various physical contact networks

Tatsukawa, Yuichi; Arefin, Md. Rajib; Utsumi, Shinobu; Kuga, Kazuki; Tanimoto, Jun

doi:10.1016/j.amc.2022.127328

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Cited by 7 publications

(1 citation statement)

References 16 publications

(20 reference statements)

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“…These two contrasting effects generate the so-called "vaccination dilemma" [43] , [44] , [45] , [46] . Evolutionary game theory can well describe the decision-making behavior when individuals are faced with this dilemma [47] , [48] , [49] .…”

Section: Introductionmentioning

confidence: 99%

Coupled disease-vaccination behavior dynamic analysis and its application in COVID-19 pandemic

Meng

Lin

et al. 2023

Chaos, Solitons & Fractals

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Section: Introductionmentioning

confidence: 99%

Coupled disease-vaccination behavior dynamic analysis and its application in COVID-19 pandemic

Meng

Lin

et al. 2023

Chaos, Solitons & Fractals

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Assessing the impact of disease incidence and immunization on the resilience of complex networks during epidemics

Islam,

Sharif Ullah,

Kabir

2025

Infectious Disease Modelling

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Heterogeneous decision-making dynamics of threshold-switching agents on complex networks

Zhao,

Zhu

2023

Chaos: An Interdisciplinary Journal of Nonlinear Science

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In the classical two-player decision-making scenario, individuals may have different tendencies to take a certain action, given that there exists a sufficient number of neighbors adopting a particular option. This is ubiquitous in many real-life contexts including traffic congestion, crowd evacuation, and minimal vertex cover problem. Under best-response dynamics, we investigate the decision-making behaviors of heterogeneous agents on complex networks. Results of the networked games are twofold: for networks of uniform degree distribution (e.g., the lattice) and fraction of the strategy is of a linear function of the threshold setting. Moreover, the equilibrium analysis is provided and the relationship between the equilibrium dynamics and the change of the threshold value is given quantitatively. Next, if the games are played on networks with non-uniform degree distribution (e.g., random regular and scale-free networks), influence of the threshold-switching will be weakened. Robust experiments indicate that it is not the value of the average degree, but the degree distribution that influences how the strategy evolves affected by the threshold settings. Our result shows that the decision-making behaviors can be effectively manipulated by tuning the parameters in the utility function (i.e., thresholds) of some agents for more regular network structures.

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Stochasticity of disease spreading derived from the microscopic simulation approach for various physical contact networks

Cited by 7 publications

References 16 publications

Coupled disease-vaccination behavior dynamic analysis and its application in COVID-19 pandemic

Coupled disease-vaccination behavior dynamic analysis and its application in COVID-19 pandemic

Assessing the impact of disease incidence and immunization on the resilience of complex networks during epidemics

Heterogeneous decision-making dynamics of threshold-switching agents on complex networks

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