The impact of the global and local awareness diffusion on epidemic transmission considering the heterogeneity of individual influences

Xu, Haidong; Zhao, Ye; Han, Dun

doi:10.1007/s11071-022-07640-y

Cited by 23 publications

(6 citation statements)

References 35 publications

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“…For instance, Xia et al introduced a coupled diffusion model that accounts for the interplay between disease transmission and awareness dissemination [35]. Xu et al investigated the impact of individual heterogeneity within a coupled disease transmission framework [36]. Furthermore, Huo et al delved into the effects of risk preference and herding behavior on risk dissemination through the construction of a supply chain risk propagation model incorporating multiple network warning mechanisms [37].…”

Section: Koreamentioning

confidence: 99%

Research on the Propagation Model of Unsafe Behaviors among Construction Workers Based on a Two-Layer NAN-SIRS Network

Hou,

Zhao

2024

Buildings

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Unsafe behaviors among construction workers are a leading cause of safety accidents in the construction industry, and studying the mechanism of unsafe behavior propagation among construction workers is essential for reducing the occurrence of safety accidents. Safety attitude plays a pivotal role in predicting workers’ behavioral intentions. We propose a propagation model of unsafe behaviors based on a two-layer complex network, in which the upper layer depicts the change in construction workers’ safety attitudes, and the lower layer represents the propagation of unsafe behaviors. In this model, we consider the impact of individual heterogeneity and herd mentality on the transmission rate, establishing a partial mapping relationship based on behavioral feedback. After that, by building a probability transition tree, we establish the risk state transition equation in detail using the microscopic Markov chain approach (MMCA) and analyze the established equations to deduce the propagation threshold of unsafe behaviors analytically. The results show that enhancing the influence of individual heterogeneity and behavioral feedback increases the threshold for the spread of unsafe behaviors, thereby reducing its scale, while herd mentality amplifies the spread. Furthermore, the coexistence of safety education and behavioral feedback may lead to one of the mechanisms fails. This research enhances understanding of the propagation mechanism of unsafe behaviors and provides a foundation for managers to implement effective measures to suppress the propagation of unsafe behaviors among construction workers.

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Section: Koreamentioning

confidence: 99%

Research on the Propagation Model of Unsafe Behaviors among Construction Workers Based on a Two-Layer NAN-SIRS Network

Hou,

Zhao

2024

Buildings

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“…In Figure 2, panel (f) illustrates the potential state transition mechanisms that individuals may undergo during the spread of infectious diseases. Unlike the SIS model [37][38][39][40], individuals, after becoming infected, first enter an asymptomatic latent period (i.e., the state E), and then progress to a symptomatic phase (i.e., the state I). All infected individuals only recover after entering the state I.…”

Section: Symbol Explanation βmentioning

confidence: 99%

Coupled Propagation Dynamics of Information and Infectious Disease on Two-Layer Complex Networks with Simplices

Hong,

Zhou,

Wang

et al. 2023

Mathematics

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The mutual influence between information and infectious diseases during the spreading process is becoming increasingly prominent. To elucidate the impact of factors such as higher-order interactions, interpersonal distances, and asymptomatic carriers on the coupled propagation of information and infectious diseases, a novel coupled spreading model is constructed based on a two-layer complex network, where one layer is a higher-order network and another layer is a weighted network. The higher-order interactions in information propagation are characterized using a 2-simplex, and a sUARU (simplicial unaware-aware-removed-unaware) model is employed to articulate information propagation. The inter-individual social distances in disease propagation are represented by the weights of a weighted network, and an SEIS (susceptible-exposed-infected-susceptible) model is utilized to describe disease propagation. The dynamic equations of coupled spreading are formulated utilizing the microscopic Markov chain approach. An analytical expression for the epidemic threshold is obtained by deriving it from the steady-state form of the dynamic equations. Comprehensive simulations are conducted to scrutinize the dynamic characteristics of the coupled spreading model. The findings indicate that enhancing the effects of higher-order interactions in information propagation and increasing inter-individual social distances both lead to higher outbreak thresholds and greater spreading of diseases. Additionally, a stronger infectivity among asymptomatic carriers and an extended incubation period are favorable for the outbreak and spread of an epidemic. These findings can provide meaningful guidance for the prevention and control of real-world epidemics.

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“…Meanwhile, Dong et al [69] develop the XYZ − ISR model considering event pulses to explore the effects of multi-channel and time delay in rumor propagation, validating the applicability and effectiveness of the model on a real dataset from Weibo. Xu et al [70] propose a doublelayer coupled SIS model considering the differences of individual influence, which utilize Markov and Mean Field Theory to analyze the interaction between awareness and epidemic. Experiments show that higher acceptance of information sharing among individuals facilitates epidemic propagation.…”

Section: Improved Modelmentioning

confidence: 99%

A Survey of Information Dissemination Model, Datasets, and Insight

Liu,

Zhang,

Shi

et al. 2023

Mathematics

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Information dissemination refers to how information spreads among users on social networks. With the widespread application of mobile communication and internet technologies, people increasingly rely on information on the internet, and the mode of information dissemination is constantly changing. Researchers have performed various studies from mathematical modeling and cascade prediction perspectives to explore the previous problem. However, lacking a comprehensive review of the latest information dissemination models hinders scientific development. As a result, it is essential to review the latest models or methods. In this paper, we review information dissemination models from the past three years and conduct a detailed analysis, such as explanatory and predictive models. Moreover, we provide public datasets, evaluation metrics, and interface tools for researchers focusing more on algorithm design and modeling. Finally, we discuss the model application and future research directions. This paper aims to understand better the research progress and development trends for beginners and guide future research endeavors. We believe this article will attract more researchers’ interest and attention to the information dissemination field on social networks.

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The impact of the global and local awareness diffusion on epidemic transmission considering the heterogeneity of individual influences

Cited by 23 publications

References 35 publications

Research on the Propagation Model of Unsafe Behaviors among Construction Workers Based on a Two-Layer NAN-SIRS Network

Research on the Propagation Model of Unsafe Behaviors among Construction Workers Based on a Two-Layer NAN-SIRS Network

Coupled Propagation Dynamics of Information and Infectious Disease on Two-Layer Complex Networks with Simplices

A Survey of Information Dissemination Model, Datasets, and Insight

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