Epidemic spreading on mixing group with face-to-face interaction

The mixing groups gathered in the enclosed space form a complex contact network due to face-to-face interaction, which affects the status and role of different groups in social communication. The intricacies of epidemic spreading in mixing groups are intrinsically complicated. Multiple interactions and transmission add to the difficulties of understanding and forecasting the spread of infectious diseases in mixing groups. Despite the critical relevance of face-to-face interactions in real-world situations, there is a significant lack of comprehensive study addressing the unique issues of mixed groups, particularly those with complex face-to-face interactions. We introduce a novel model employing an agent-based approach to elucidate the nuances of face-to-face interactions within mixing groups. In this paper, we apply a susceptible-infected-susceptible process to mixing groups and integrate a temporal network within a specified time window to distinguish between individual movement patterns and epidemic spreading dynamics. Our findings highlight the significant impact of both the relative size of mixing groups and the groups' mixing patterns on the trajectory of disease spread within the mixing groups. When group sizes differ significantly, high inter-group contact preference limits disease spread. However, if the minority reduces their intra-group preferences while the majority maintains high inter-group contact, disease spread increases. In balanced group sizes, high intra-group contact preferences can limit transmission, but asymmetrically reducing any group's intra-group preference can lead to increased spread.

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Publication Details

Journal
Chaos An Interdisciplinary Journal of Nonlinear Science
Published
2024-09-01
DOI
https://doi.org/10.1063/5.0222847
Citations
2
Primary Topic
COVID-19 epidemiological studies
Type
article
Field-Weighted Citation Impact
0.79
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article

Epidemic spreading on mixing group with face-to-face interaction

Feng Gao, Wenbin Gu, Sheng Su, Xiaoyang Liu et al.
2 citations
Chaos An Interdisciplinary Journal of Nonlinear Science
COVID-19 epidemiological studies
0.79
article

Epidemic spreading on mixing group with face-to-face interaction

Feng Gao, Wenbin Gu, Sheng Su, Xiaoyang Liu, Wenjie Li, Wei Wang, Zengping Zhang
article en
2 citations

Abstract

The mixing groups gathered in the enclosed space form a complex contact network due to face-to-face interaction, which affects the status and role of different groups in social communication. The intricacies of epidemic spreading in mixing groups are intrinsically complicated. Multiple interactions and transmission add to the difficulties of understanding and forecasting the spread of infectious diseases in mixing groups. Despite the critical relevance of face-to-face interactions in real-world situations, there is a significant lack of comprehensive study addressing the unique issues of mixed groups, particularly those with complex face-to-face interactions. We introduce a novel model employing an agent-based approach to elucidate the nuances of face-to-face interactions within mixing groups. In this paper, we apply a susceptible-infected-susceptible process to mixing groups and integrate a temporal network within a specified time window to distinguish between individual movement patterns and epidemic spreading dynamics. Our findings highlight the significant impact of both the relative size of mixing groups and the groups' mixing patterns on the trajectory of disease spread within the mixing groups. When group sizes differ significantly, high inter-group contact preference limits disease spread. However, if the minority reduces their intra-group preferences while the majority maintains high inter-group contact, disease spread increases. In balanced group sizes, high intra-group contact preferences can limit transmission, but asymmetrically reducing any group's intra-group preference can lead to increased spread.

Chaos An Interdisciplinary Journal of Nonlinear ScienceVol. 34(9)
University of Electronic Science and Technology of China (CN), Inner Mongolia University of Finance and Economics (CN), Chongqing University of Arts and Sciences (CN), Chongqing Public Health Medical Center (CN), Chongqing University of Technology (CN), Chongqing Medical University (CN)
Good health and well-being
Openalex Percentile: Top 31%
COVID-19 epidemiological studies
0.79
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