Influence of Opinion Dynamics to Inhibit Epidemic Spreading Over Multiplex Network

The co-evolution between information and epidemic in multilayer networks has attracted wide attention. However, previous studies usually assume that two networks with the same individuals are coupled into a multiplex network, ignoring the context that the individuals of each layer in the multilayer network are often different, especially in group structures with rich collective phenomena. In this paper, based on the scenario of group-based multilayer networks, we investigate the coupled UAU-SIS (Unaware-Aware-Unaware-Susceptible-Infected-Susceptible) model via microscopic Markov chain approach (MMCA). Importantly, the evolution of such transmission process with respective to various impact factors, especially for the group features, is captured by simulations. We further obtain the theoretical threshold for the onset of epidemic outbreaks and analyze its characteristics through numerical simulations. It is concluded that the growth of the group size of information (physical) layer effectively suppresses (enhances) epidemic spreading. Moreover, taking the context of epidemic immunization into account, we find that the propagation capacity and robustness of this type of network are greater than the conventional multiplex network.

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Modeling partial lockdowns in multiplex networks using partition strategies

Applied Network Science ◽

10.1007/s41109-021-00366-7 ◽

2021 ◽

Vol 6 (1) ◽

Author(s):

Adrià Plazas ◽

Irene Malvestio ◽

Michele Starnini ◽

Albert Díaz-Guilera

Keyword(s):

Social Interactions ◽

Economic Burden ◽

Economic Cost ◽

Network Approach ◽

Epidemic Outbreak ◽

Epidemic Spreading ◽

Network Partition ◽

Multiplex Network ◽

The Social ◽

Containment Measures

AbstractNational stay-at-home orders, or lockdowns, were imposed in several countries to drastically reduce the social interactions mainly responsible for the transmission of the SARS-CoV-2 virus. Despite being essential to slow down the COVID-19 pandemic, these containment measures are associated with an economic burden. In this work, we propose a network approach to model the implementation of a partial lockdown, breaking the society into disconnected components, or partitions. Our model is composed by two main ingredients: a multiplex network representing human contacts within different contexts, formed by a Household layer, a Work layer, and a Social layer including generic social interactions, and a Susceptible-Infected-Recovered process that mimics the epidemic spreading. We compare different partition strategies, with a twofold aim: reducing the epidemic outbreak and minimizing the economic cost associated to the partial lockdown. We also show that the inclusion of unconstrained social interactions dramatically increases the epidemic spreading, while different kinds of restrictions on social interactions help in keeping the benefices of the network partition.

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Role of Time Scales in the Coupled Epidemic-Opinion Dynamics on Multiplex Networks

Entropy ◽

10.3390/e24010105 ◽

2022 ◽

Vol 24 (1) ◽

pp. 105

Author(s):

Robert Jankowski ◽

Anna Chmiel

Keyword(s):

Dynamic Model ◽

Crucial Role ◽

Opinion Dynamics ◽

Point Of View ◽

Entire System ◽

Epidemic Spreading ◽

Multiplex Networks ◽

The Impact ◽

Opinion Dynamic

Modelling the epidemic’s spread on multiplex networks, considering complex human behaviours, has recently gained the attention of many scientists. In this work, we study the interplay between epidemic spreading and opinion dynamics on multiplex networks. An agent in the epidemic layer could remain in one of five distinct states, resulting in the SIRQD model. The agent’s attitude towards respecting the restrictions of the pandemic plays a crucial role in its prevalence. In our model, the agent’s point of view could be altered by either conformism mechanism, social pressure, or independent actions. As the underlying opinion model, we leverage the q-voter model. The entire system constitutes a coupled opinion–dynamic model where two distinct processes occur. The question arises of how to properly align these dynamics, i.e., whether they should possess equal or disparate timescales. This paper highlights the impact of different timescales of opinion dynamics on epidemic spreading, focusing on the time and the infection’s peak.

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Reachability analysis and optimal control for epidemic spreading model on multiplex network

2019 Fifth Indian Control Conference (ICC) ◽

10.1109/indiancc.2019.8715623 ◽

2019 ◽

Author(s):

M.V. Surya Prakash ◽

Arun D. Mahindrakar ◽

Ramkrishna Pasumarthy

Keyword(s):

Optimal Control ◽

Reachability Analysis ◽

Epidemic Spreading ◽

Spreading Model ◽

Multiplex Network

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A multilayer network model of the coevolution of the spread of a disease and competing opinions

Mathematical Models and Methods in Applied Sciences ◽

10.1142/s0218202521500536 ◽

2021 ◽

pp. 1-40

Author(s):

Kaiyan Peng ◽

Zheng Lu ◽

Vanessa Lin ◽

Michael R. Lindstrom ◽

Christian Parkinson ◽

...

Keyword(s):

Social Media ◽

Monte Carlo ◽

Disease Transmission ◽

Mean Field ◽

Opinion Dynamics ◽

The Other ◽

Cross Layer ◽

Multilayer Network ◽

Complex Interactions ◽

Multiplex Network

During the COVID-19 pandemic, conflicting opinions on physical distancing swept across social media, affecting both human behavior and the spread of COVID-19. Inspired by such phenomena, we construct a two-layer multiplex network for the coupled spread of a disease and conflicting opinions. We model each process as a contagion. On one layer, we consider the concurrent evolution of two opinions — pro-physical-distancing and anti-physical-distancing — that compete with each other and have mutual immunity to each other. The disease evolves on the other layer, and individuals are less likely (respectively, more likely) to become infected when they adopt the pro-physical-distancing (respectively, anti-physical-distancing) opinion. We develop approximations of mean-field type by generalizing monolayer pair approximations to multilayer networks; these approximations agree well with Monte Carlo simulations for a broad range of parameters and several network structures. Through numerical simulations, we illustrate the influence of opinion dynamics on the spread of the disease from complex interactions both between the two conflicting opinions and between the opinions and the disease. We find that lengthening the duration that individuals hold an opinion may help suppress disease transmission, and we demonstrate that increasing the cross-layer correlations or intra-layer correlations of node degrees may lead to fewer individuals becoming infected with the disease.

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Coupled Dynamic Model of Resource Diffusion and Epidemic Spreading in Time-Varying Multiplex Networks

Complexity ◽

10.1155/2021/6629105 ◽

2021 ◽

Vol 2021 ◽

pp. 1-11

Author(s):

Ping Huang ◽

Xiao-Long Chen ◽

Ming Tang ◽

Shi-Min Cai

Keyword(s):

Social Networks ◽

Dynamic Model ◽

Coupled Model ◽

Resource Depletion ◽

Equilibrium Analysis ◽

Time Varying ◽

Epidemic Spreading ◽

Coupled Dynamics ◽

Dynamic Social Networks ◽

Multiplex Network

In the real world, individual resources are crucial for patients when epidemics outbreak. Thus, the coupled dynamics of resource diffusion and epidemic spreading have been widely investigated when the recovery of diseases significantly depends on the resources from neighbors in static social networks. However, the social relationships of individuals are time-varying, which affects such coupled dynamics. For that, we propose a coupled resource-epidemic (RNR-SIS) dynamic model (coupled model for short) on a time-varying multiplex network to synchronously simulate the resource diffusion and epidemic spreading in dynamic social networks. The equilibrium analysis of the coupled model is conducted in a general scenario where the resource generation varies between susceptible and infected states and the recovery rate changes between resourceful and noresource states. By using the microscopic Markov chain approach and Monte Carlo simulations, we determine a probabilistic framework of the intralayer and interlayer dynamic processes of the coupled model and obtain the outbreak threshold of epidemic spreading. Meanwhile, the experimental results show the trivially asymmetric interactions between resource diffusion and epidemic spreading. They also indicate that the stronger activity heterogeneity and the larger contact capacity of individuals in the resource layer can more greatly promote resource diffusion, effectively suppressing epidemic spreading. However, these two individual characters in the epidemic layer can cause more resource depletion, which greatly promotes epidemic spreading. Furthermore, we also find that the contact capacity finitely impacts the coupled dynamics of resource diffusion and epidemic spreading.

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