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比利时对 COVID-19 的数字接触追踪的个体层面分析凸显出主要瓶颈。

Individual level analysis of digital proximity tracing for COVID-19 in Belgium highlights major bottlenecks.

机构信息

KU Leuven, Dept of Microbiology, Immunology and Transplantation, Laboratory of Clinical Microbiology, Leuven, Belgium.

Department of Infectious Diseases, The University of Melbourne at the Peter Doherty Institute for Infection and Immunity, Melbourne, Victoria, Australia.

出版信息

Nat Commun. 2023 Oct 23;14(1):6717. doi: 10.1038/s41467-023-42518-6.

Abstract

To complement labour-intensive conventional contact tracing, digital proximity tracing was implemented widely during the COVID-19 pandemic. However, the privacy-centred design of the dominant Google-Apple exposure notification framework has hindered assessment of its effectiveness. Between October 2021 and January 2022, we systematically collected app use and notification receipt data within a test and trace programme targeting around 50,000 university students in Leuven, Belgium. Due to low success rates in each studied step of the digital notification cascade, only 4.3% of exposed contacts (CI: 2.8-6.1%) received such notifications, resulting in 10 times more cases detected through conventional contact tracing. Moreover, the infection risk of digitally traced contacts (5.0%; CI: 3.0-7.7%) was lower than that of conventionally traced non-app users (9.8%; CI: 8.8-10.7%; p = 0.002). Contrary to common perception as near instantaneous, there was a 1.2-day delay (CI: 0.6-2.2) between case PCR result and digital contact notification. These results highlight major limitations of a digital proximity tracing system based on the dominant framework.

摘要

为了补充劳动密集型的传统接触者追踪,在 COVID-19 大流行期间广泛实施了数字接近追踪。然而,主导的谷歌-苹果接触者暴露通知框架以隐私为中心的设计阻碍了对其有效性的评估。2021 年 10 月至 2022 年 1 月期间,我们在针对比利时鲁汶约 50000 名大学生的测试和追踪计划中系统地收集了应用程序使用和通知接收数据。由于数字通知级联的每个研究步骤的成功率都很低,只有 4.3%(95%CI:2.8-6.1%)的接触者收到了此类通知,导致通过传统接触者追踪检测到的病例增加了 10 倍。此外,数字追踪接触者的感染风险(5.0%;95%CI:3.0-7.7%)低于传统追踪非应用用户的感染风险(9.8%;95%CI:8.8-10.7%;p=0.002)。与普遍认为的近乎即时相反,病例 PCR 结果和数字接触通知之间存在 1.2 天的延迟(95%CI:0.6-2.2)。这些结果突出了基于主导框架的数字接近追踪系统的主要局限性。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5af2/10593825/c3df98a52623/41467_2023_42518_Fig1_HTML.jpg

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