Shidong Zhai

This study investigates the fundamental mechanisms underlying cross-species, multi-strain transmission in ecosystems from the opinion of group opinion dynamics. A multilayer interaction framework is proposed, incorporating signed-weighted social network dynamics to quantify group-level opinions and dynamically adjust key epidemiological parameters in real time. The analysis reveals that (1) infection pressure alters group opinion thresholds via cognitive-behavioral feedback, while the emerging collective consensus reciprocally regulates transmission intensity, forming a closed-loop feedback mechanism. (2) The topology of the opinion network governs epidemic phase transitions, inducing a bistable regime characterized by either low-risk (opinion cohesion) or high-risk (opinion polarization) states. By identifying critical nodes within the signed social graph, the study transforms group opinion intensity into dynamic warning thresholds, enabling targeted ecological interventions.

This article investigates cluster synchronization (CS) of individuals during an epidemic using a coupled nonlinear network that integrates diffusion-coupled nonlinear systems with an susceptible-infected-recovered (SIR) virus model. To better reflect real-life scenarios, individuals are grouped into clusters, and the model incorporates recovery rates that vary according to collective behavior patterns. The study focuses on analyzing the relationship between CS behavior and the progression of virus transmission within the network. By ensuring that the directed graph satisfies the cluster input equivalence condition and that the system’s Jacobian matrix remains bounded, contraction analysis is employed to establish conditions for achieving CS, which are influenced by the virus’s state. Furthermore, the impact of CS on epidemic dynamics is explored. Numerical simulations validate the theoretical findings.

In order to investigate how different levels of vigilance affect the spread of a virus and changes in public opinion, this article introduces a network-based susceptible-exposed-infectious-vigilant (SEIV)-Opinion model. The model incorporates vigilance influence functions that depend on infection and recovery rates, which are associated with opinion states. A basic reproduction number dependent on both the viral transmission state and public opinion dynamics is constructed to analyze the conditions for virus eradication or pandemic persistence. These findings indicate that during severe epidemics, people are very concerned about the epidemic, leading to an increased vigilance, thereby significantly slowing the spread of the virus. On the other hand, during milder epidemics, people do not respond adequately to the threat of the epidemic, and thus are less vigilant and have less impact on the spread of the virus. These insights correlate closely with real-world trends. This article uses numerical simulations to demonstrate and confirm these patterns under various conditions.