关键词: 水痘暴发, 社交接触网络, 网络拓扑性, 感染风险

Abstract: Objective To investigate the key risk factors for varicella (chickenpox) infection by integrating epidemiological survey data with social contact network analysis. Methods A retrospective field study was conducted following a varicella outbreak in May 2024 at a primary school. We utilized wireless wearable devices equipped with Ultra-Wideband (UWB) positioning technology to reconstruct the social contact network of the affected class. High-precision data on key parameters, including the number of contacts, contact frequency, and total contact duration, were systematically collected. Descriptive analysis was employed to characterize the distribution of student contact patterns. Subsequently, a Bayesian logistic regression model was constructed to identify critical risk factors associated with varicella incidence and transmission. Results The varicella attack rate during this primary school outbreak was 21.7% (10/46). A total of 14 009 contact instances were recorded among 45 students and one teacher. The median number of contacts per individual was 57. Of all single interaction events, 59.8% had a duration of less than one minute, and the median cumulative contact duration for 1 019 contact dyads was 28.1 minutes. The median duration of a single close-proximity (≤1 m) contact (6.6 minutes) was substantially longer than that of more distant (>1 m) contacts (1.5 minutes). The Bayesian multivariate logistic regression model indicated that varicella vaccination was a significant protective factor (OR=0.031, 95% CI: 0.003-0.236). Conversely, each standard deviation increase in the average duration of close-proximity contact (1.938 min) (OR=4.621, 95% CI: 1.485-18.922) and in degree centrality (9.968) (OR=3.910, 95% CI: 1.494-12.555) was associated with an increased risk of infection. Conclusion This study innovatively applied UWB technology to quantify the influence of social network centrality on varicella transmission. The findings demonstrate that vaccination significantly reduces the risk of infection, whereas prolonged duration of individual close-proximity contact and higher degree centrality within the network elevate infection risk. These results provide a scientific foundation for the development of precise, contact network-based early warning systems.

Key words: Varicella outbreak, Social contact network, Network topology, Infection risk