Impact of message fatigue and individual behavioral responses on epidemiological spread in temporal simplicial networks

doi:10.1088/1674-1056/adaade

Abstract Health information spreads rapidly, which can effectively control epidemics. However, the swift dissemination of information also has potential negative impacts, which increasingly attracts 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 apply 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.

Keywords: Monte Carlo simulation microscopic Markov chain approach message fatigue information--epidemic coupled spreading simplicial complex

Received: 26 November 2024
Revised: 03 January 2025
Accepted manuscript online: 16 January 2025

PACS:	87.19.X-	(Diseases)
	02.50.Ga	(Markov processes)
	02.70.Uu	(Applications of Monte Carlo methods)
	05.10.-a	(Computational methods in statistical physics and nonlinear dynamics)

Fund: Project supported by the National Natural Science Foundation of China (Grant Nos. 72171136 and 72134004), Humanities and Social Science Research Project, Ministry of Education of China (Grant No. 21YJC630157), the Natural Science Foundation of Shandong Province (Grant No. ZR2022MG008), and Shandong Provincial Colleges and Universities Youth Innovation Technology of China (Grant No. 2022RW066).

Corresponding Authors: Xuemei You
E-mail: youxuemei@sdnu.edu.cn

Cite this article:

Xiao-Nan Fan(樊晓楠) and Xuemei You(由雪梅) Impact of message fatigue and individual behavioral responses on epidemiological spread in temporal simplicial networks 2025 Chin. Phys. B 34 038703

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Impact of message fatigue and individual behavioral responses on epidemiological spread in temporal simplicial networks

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