Abstract
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.
Highlights
The work by Kermack and McKendrick [1] is acclaimed as the primary mathematical modelling tool of infectious diseases
We study the interplay between epidemic spreading and opinion dynamics on multiplex networks
To study the role of timescales in a more precise manner, we introduce a parameter vstep, which controls the speed of state change in the q-voter model, i.e., per each timestep on the epidemic layer, we perform vstep updates on the opinion layer
Summary
The work by Kermack and McKendrick [1] is acclaimed as the primary mathematical modelling tool of infectious diseases. The model can be used to highlight the importance of social distancing and safety measures such as using face masks or hand-washing. By incorporating these health-prevention recommendations, we could mitigate the disease spread, i.e., reduce the infection probability [2]. Epidemic spreading has been studied on scale-free networks [4], hierarchical social networks [5], networks with community structure [6], and correlated [7] or weighted [8] complex networks All of these works operate on the single-layer network
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