Impact of Opinion-Driven Adaptive Vigilance on Virus Spread and Opinion Evolution
研究提出一个网络模型，分析公众警惕性如何随疫情严重程度变化，进而影响病毒传播和观点演化，发现严重疫情时警惕性高能减缓传播，轻微疫情时警惕性不足则影响有限。
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.