Spatiotemporal heterogeneity of social contact patterns related to infectious diseases in the Guangdong Province, China

The social contact patterns associated with the infectious disease transmitted by airborne droplets or close contact follow specific rules. Understanding these processes can improve the accuracy of disease transmission models, permitting their integration into model simulations. In this study, we pe...

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Published inScientific reports Vol. 10; no. 1; p. 6119
Main Authors Huang, Yulin, Cai, Xiaoshuang, Zhang, Bing, Zhu, Guanghu, Liu, Tao, Guo, Pi, Xiao, Jianpeng, Li, Xing, Zeng, Weilin, Hu, Jianxiong, Ma, Wenjun
Format Journal Article
LanguageEnglish
Published London Nature Publishing Group UK 15.04.2020
Nature Publishing Group
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Summary:The social contact patterns associated with the infectious disease transmitted by airborne droplets or close contact follow specific rules. Understanding these processes can improve the accuracy of disease transmission models, permitting their integration into model simulations. In this study, we performed a large-scale population-based survey to collect social contact patterns in three cities on the Pearl River Delta of China in winter and summer. A total of 5,818 participants were face-to-face interviewed and 35,542 contacts were recorded. The average number of contacts per person each day was 16.7 considering supplementary professional contacts (SPCs). Contacts that occurred on a daily basis, lasted more than 4 hours, and took place in households were more likely to involve physical contact. The seasonal characteristics of social contact were heterogeneous, such that contact in the winter was more likely to involve physical contact compared to summer months. The spatial characteristics of the contacts were similar. Social mixing patterns differed according to age, but all ages maintained regular contact with their peers. Taken together, these findings describe the spatiotemporal distribution of social contact patterns relevant to infections in the Guangdong Province of China. This information provides important parameters for mathematical models of infectious diseases.
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ISSN:2045-2322
2045-2322
DOI:10.1038/s41598-020-63383-z