Asymmetrically interacting dynamics with mutual confirmation from multi-source on multiplex networks
Jiaxing Chen, Ying Liu, Ming Tang, Jing Yue

TL;DR
This paper models how individuals' mutual confirmation of information from multiple sources influences protective behavior and epidemic spread on multiplex networks, providing analytical insights into epidemic thresholds and awareness levels.
Contribution
It introduces a mutual confirmation mechanism into multiplex network epidemic models and derives analytical expressions for epidemic thresholds and stationary states.
Findings
Confirmation from communication layers increases epidemic threshold more.
Confirmation from contact layers reduces infection density and increases awareness.
Explicit sharing of infection and awareness status helps suppress epidemic spreading.
Abstract
In the early stage of epidemics, individuals' determination on adopting protective measures, which can reduce their risk of infection and suppress disease spreading, is likely to depend on multiple information sources and their mutual confirmation due to inadequate exact information. Here we introduce the inter-layer mutual confirmation mechanism into the information-disease interacting dynamics on multiplex networks. In our model, an individual increases the information transmission rate and willingness to adopt protective measures once he confirms the authenticity of news and severity of disease from neighbors status in multiple layers. By using the microscopic Markov chain approach, we analytically calculate the epidemic threshold and the awareness and infected density in the stationary state, which agree well with simulation results. We find that the increment of epidemic threshold…
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Taxonomy
TopicsComplex Network Analysis Techniques · Opinion Dynamics and Social Influence · COVID-19 epidemiological studies
