The coevolution of the spread of a disease and competing opinions in multiplex networks

Fang, Fanshu; Ma, Jing; Li, Yanli

doi:10.1016/j.chaos.2023.113376

Cited by 17 publications

(4 citation statements)

References 34 publications

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“…We discuss this point in more detail in Section 3.3, where we exhibit the results of Monte Carlo simulations of the model. Equation (16) corresponds to the limit t → ∞ of the solution to Equation (17):…”

Section: Analytical Resultsmentioning

confidence: 99%

“…Concerning sociologists, these methods are useful to improve forecasting by means of controlled toy models that can be run multiple times and help fine-tune field studies as well [15]. In addition to the interesting properties of opinion dynamics models, per se, such dynamics have also been applied in various fields such as finance and business [16], and epidemic dynamics with the presence of conflicting opinions [17][18][19][20][21][22][23], among others [3].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Phase Transition in the Galam’s Majority-Rule Model with Information-Mediated Independence

Oestereich,

Pires,

Duarte Queirós

et al. 2023

Physics

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We study the Galam’s majority-rule model in the presence of an independent behavior that can be driven intrinsically or can be mediated by information regarding the collective opinion of the whole population. We first apply the mean-field approach where we obtained an explicit time-dependent solution for the order parameter of the model. We complement our results with Monte Carlo simulations where our findings indicate that independent opinion leads to order–disorder continuous nonequilibrium phase transitions. Finite-size scaling analysis show that the model belongs to the mean-field Ising model universality class. Moreover, results from an approach with the Kramers–Moyal coefficients provide insights about the social volatility.

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Section: Analytical Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Phase Transition in the Galam’s Majority-Rule Model with Information-Mediated Independence

Oestereich,

Pires,

Duarte Queirós

et al. 2023

Physics

View full text Add to dashboard Cite

show abstract

“…These include coevolving voter models [ 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 ], coevolving spin systems [ 39 , 40 ], coevolving models of opinion formation [ 41 , 42 ], epidemic models of adaptive networks [ 43 , 44 , 45 ], coevolving models of cultural evolution [ 46 , 47 ], and game theoretical models [ 48 ]. While we here focus on the coevolution of node states and network topology, there have been studies that address the coevolution between different dynamical processes in a static network [ 49 , 50 , 51 , 52 ]. In cases where cascading dynamics are coupled with the evolution of the network structure, it is essential to understand the coevolutionary dynamics of the network topology and threshold dynamics.…”

Section: Introductionmentioning

confidence: 99%

Threshold Cascade Dynamics in Coevolving Networks

Min

Miguel

2023

Entropy

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We study the coevolutionary dynamics of network topology and social complex contagion using a threshold cascade model. Our coevolving threshold model incorporates two mechanisms: the threshold mechanism for the spreading of a minority state such as a new opinion, idea, or innovation and the network plasticity, implemented as the rewiring of links to cut the connections between nodes in different states. Using numerical simulations and a mean-field theoretical analysis, we demonstrate that the coevolutionary dynamics can significantly affect the cascade dynamics. The domain of parameters, i.e., the threshold and mean degree, for which global cascades occur shrinks with an increasing network plasticity, indicating that the rewiring process suppresses the onset of global cascades. We also found that during evolution, non-adopting nodes form denser connections, resulting in a wider degree distribution and a non-monotonous dependence of cascades sizes on plasticity.

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“…Most of the prior research in coupled spreading dynamics on complex networks has conventionally operated under the assumption that information or diseases can exclusively disseminate through direct contact relationships between two individuals [24][25][26][27][28][29]. However, beyond these individual direct contact relationships, the high-order interactions collectively generated by multiple individuals also exert a noteworthy influence on the transmission patterns and rates of information or infectious diseases.…”

Section: Introductionmentioning

confidence: 99%

Coupled Propagation Dynamics of Information and Infectious Disease on Two-Layer Complex Networks with Simplices

Hong,

Zhou,

Wang

et al. 2023

Mathematics

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The mutual influence between information and infectious diseases during the spreading process is becoming increasingly prominent. To elucidate the impact of factors such as higher-order interactions, interpersonal distances, and asymptomatic carriers on the coupled propagation of information and infectious diseases, a novel coupled spreading model is constructed based on a two-layer complex network, where one layer is a higher-order network and another layer is a weighted network. The higher-order interactions in information propagation are characterized using a 2-simplex, and a sUARU (simplicial unaware-aware-removed-unaware) model is employed to articulate information propagation. The inter-individual social distances in disease propagation are represented by the weights of a weighted network, and an SEIS (susceptible-exposed-infected-susceptible) model is utilized to describe disease propagation. The dynamic equations of coupled spreading are formulated utilizing the microscopic Markov chain approach. An analytical expression for the epidemic threshold is obtained by deriving it from the steady-state form of the dynamic equations. Comprehensive simulations are conducted to scrutinize the dynamic characteristics of the coupled spreading model. The findings indicate that enhancing the effects of higher-order interactions in information propagation and increasing inter-individual social distances both lead to higher outbreak thresholds and greater spreading of diseases. Additionally, a stronger infectivity among asymptomatic carriers and an extended incubation period are favorable for the outbreak and spread of an epidemic. These findings can provide meaningful guidance for the prevention and control of real-world epidemics.

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The coevolution of the spread of a disease and competing opinions in multiplex networks

Cited by 17 publications

References 34 publications

Phase Transition in the Galam’s Majority-Rule Model with Information-Mediated Independence

Phase Transition in the Galam’s Majority-Rule Model with Information-Mediated Independence

Threshold Cascade Dynamics in Coevolving Networks

Coupled Propagation Dynamics of Information and Infectious Disease on Two-Layer Complex Networks with Simplices

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