DOI: 10.57760/sciencedb.17567
title: Impact of message fatigue and Individual behavioral responses on epidemiological spread in temporal simplicial networks
title: Impact of message fatigue and Individual behavioral responses on epidemiological spread in temporal simplicial networks

description: Health information spreads rapidly, which can effectively control epidemics. However, the swift dissemination of information also has potential negative impacts, increasingly attracting attention. Message fatigue refers to the psychological response characterized by feelings of boredom and anxiety that occur after receiving an excessive amount of similar information. This phenomenon can alter individual behaviors related to epidemic prevention. Additionally, recent studies indicate that pairwise interactions alone are insufficient to describe complex social transmission processes, and higher-order structures representing group interactions are crucial. To address this, we develop a novel epidemic model that investigates the interactions between information, behavioral responses, and epidemics. Our model incorporates the impact of message fatigue on the entire transmission system. The information layer is modeled using a static simplicial network to capture group interactions, while the disease layer uses a time-varying network based on activity-driven model with attractiveness to represent the self-protection behaviors of susceptible individuals and self-isolation behaviors of infected individuals. We theoretically describe the co-evolution equations using the microscopic Markov chain approach (MMCA) and get the epidemic threshold. Experimental results show that while the negative impact of message fatigue on epidemic transmission is limited, it significantly weakens the group interactions depicted by higher-order structures. Individual behavioral responses strongly inhibit the epidemic. Our simulations using the Monte Carlo (MC) method demonstrate that greater intensity in these responses leads to clustering of susceptible individuals in the disease layer. Finally, we applied the proposed model to real networks to verify its reliability. In summary, our research results enhance the understanding of the information-epidemic coupling dynamics, and we expect to provide valuable guidance for managing future emerging epidemics. 
description: Health information spreads rapidly, which can effectively control epidemics. However, the swift dissemination of information also has potential negative impacts, increasingly attracting attention. Message fatigue refers to the psychological response characterized by feelings of boredom and anxiety that occur after receiving an excessive amount of similar information. This phenomenon can alter individual behaviors related to epidemic prevention. Additionally, recent studies indicate that pairwise interactions alone are insufficient to describe complex social transmission processes, and higher-order structures representing group interactions are crucial. To address this, we develop a novel epidemic model that investigates the interactions between information, behavioral responses, and epidemics. Our model incorporates the impact of message fatigue on the entire transmission system. The information layer is modeled using a static simplicial network to capture group interactions, while the disease layer uses a time-varying network based on activity-driven model with attractiveness to represent the self-protection behaviors of susceptible individuals and self-isolation behaviors of infected individuals. We theoretically describe the co-evolution equations using the microscopic Markov chain approach (MMCA) and get the epidemic threshold. Experimental results show that while the negative impact of message fatigue on epidemic transmission is limited, it significantly weakens the group interactions depicted by higher-order structures. Individual behavioral responses strongly inhibit the epidemic. Our simulations using the Monte Carlo (MC) method demonstrate that greater intensity in these responses leads to clustering of susceptible individuals in the disease layer. Finally, we applied the proposed model to real networks to verify its reliability. In summary, our research results enhance the understanding of the information-epidemic coupling dynamics, and we expect to provide valuable guidance for managing future emerging epidemics.