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利用数字接触者追踪数据估计特定环境接触对 SARS-CoV-2 传播的贡献。

Estimating the contribution of setting-specific contacts to SARS-CoV-2 transmission using digital contact tracing data.

机构信息

State Key Laboratory of Remote Sensing Science, Center for Global Change and Public Health, Faculty of Geographical Science, Beijing Normal University, Beijing, China.

Beijing Center for Disease Prevention and Control, Beijing, China.

出版信息

Nat Commun. 2024 Jul 19;15(1):6103. doi: 10.1038/s41467-024-50487-7.

Abstract

While many countries employed digital contact tracing to contain the spread of SARS-CoV-2, the contribution of cospace-time interaction (i.e., individuals who shared the same space and time) to transmission and to super-spreading in the real world has seldom been systematically studied due to the lack of systematic sampling and testing of contacts. To address this issue, we utilized data from 2230 cases and 220,878 contacts with detailed epidemiological information during the Omicron outbreak in Beijing in 2022. We observed that contact number per day of tracing for individuals in dwelling, workplace, cospace-time interactions, and community settings could be described by gamma distribution with distinct parameters. Our findings revealed that 38% of traced transmissions occurred through cospace-time interactions whilst control measures were in place. However, using a mathematical model to incorporate contacts in different locations, we found that without control measures, cospace-time interactions contributed to only 11% (95%CI: 10%-12%) of transmissions and the super-spreading risk for this setting was 4% (95%CI: 3%-5%), both the lowest among all settings studied. These results suggest that public health measures should be optimized to achieve a balance between the benefits of digital contact tracing for cospace-time interactions and the challenges posed by contact tracing within the same setting.

摘要

虽然许多国家采用了数字接触者追踪来控制 SARS-CoV-2 的传播,但由于缺乏对接触者的系统抽样和检测,很少有系统地研究共空间-时间相互作用(即共享相同空间和时间的个体)对传播和现实世界中超传播的贡献。为了解决这个问题,我们利用了 2022 年北京奥密克戎疫情期间 2230 例病例和 220878 名有详细流行病学信息的接触者的数据。我们观察到,居住、工作场所、共空间-时间相互作用和社区环境中个体每天的追踪接触人数可以用具有不同参数的伽马分布来描述。我们的研究结果表明,在实施控制措施的情况下,有 38%的追踪传播是通过共空间-时间相互作用发生的。然而,通过引入不同位置的接触者的数学模型,我们发现如果没有控制措施,共空间-时间相互作用仅导致 11%(95%CI:10%-12%)的传播,而这种情况下的超级传播风险为 4%(95%CI:3%-5%),均为所有研究环境中最低的。这些结果表明,应优化公共卫生措施,在数字接触者追踪对共空间-时间相互作用的益处和同一环境中接触者追踪带来的挑战之间取得平衡。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/248d/11271501/7932cd3805bb/41467_2024_50487_Fig1_HTML.jpg

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