Performance of Digital Contact Tracing Tools for COVID-19 Response in Singapore: Cross-Sectional Study.

Performance of Digital Contact Tracing Tools for COVID-19 Response in Singapore: Cross-Sectional Study.
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DOI:
10.2196/23148
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发表时间:
2020-10-29
影响因子:
5
通讯作者:
Chow A
Chow A
中科院分区:
医学2区
文献类型:
--
作者:
Huang Z;Guo H;Lee YM;Ho EC;Ang H;Chow A

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在 COVID-19 大流行期间,有效的接触者追踪是一项劳动密集型工作且时间敏感,但在缺乏有效治疗和疫苗的情况下也至关重要。新加坡于 2020 年 3 月推出了首款基于蓝牙的接触者追踪应用程序——TraceTogether,以增强新加坡的接触者追踪能力。本研究旨在将接触者追踪应用程序 TraceTogether 与基于可穿戴标签的实时定位系统 (RTLS) 的性能进行比较,并根据国家传染病中心 (NCID)(国家 COVID-19 筛查转诊中心)的电子病历对其进行验证。 NCID 筛查中心的所有患者和医生都获得了 RTLS 标签 (CADI Scientific),用于追踪接触者。 2020 年 5 月 10 日至 20 日,共有 18 名医生被部署到 NCID 筛查中心。医生在轮班期间激活智能手机上的 TraceTogether 应用程序(1.6 版;GovTech),并敦促患者使用该应用程序。我们比较了由 TraceTogether 识别的患者接触者和在医生发布的 10 天期间在 NCID 附近由 RTLS 标签识别的患者接触者。我们还通过在研究期间 24 小时内到 NCID 筛查中心就诊的 156 名患者的电子病历来验证医患接触情况,从而验证了这两种数字接触追踪工具。 RTLS 标签对于检测系统或 TraceTogether 识别的患者接触者具有 95.3% 的高灵敏度,而 TraceTogether 的总体灵敏度为 6.5%,并且在 Android 手机上的表现明显优于 iPhone(Android:9.7%,iPhone:2.7%;P<.001)。根据电子病历进行验证时,RTLS 标签的灵敏度为 96.9%,特异性为 83.1%,而 TraceTogether 仅检测到 2 名患者与未对其进行治疗的医生有过接触。在临床环境中识别患者接触者时,TraceTogether 的灵敏度比 RTLS 标签低得多。尽管基于标签的 RTLS 在临床环境中的接触者追踪方面表现良好,但其在社区中的实施将比 TraceTogether 更具挑战性。鉴于接触者追踪应用程序的采用和功能的不确定性,政策制定者应注意不要过度依赖此类应用程序进行接触者追踪。尽管如此,利用技术来增强传统的手动接触者追踪是在 COVID-19 大流行的长期过程中恢复正常生活的必要举措。
Effective contact tracing is labor intensive and time sensitive during the COVID-19 pandemic, but also essential in the absence of effective treatment and vaccines. Singapore launched the first Bluetooth-based contact tracing app—TraceTogether—in March 2020 to augment Singapore’s contact tracing capabilities. This study aims to compare the performance of the contact tracing app—TraceTogether—with that of a wearable tag-based real-time locating system (RTLS) and to validate them against the electronic medical records at the National Centre for Infectious Diseases (NCID), the national referral center for COVID-19 screening. All patients and physicians in the NCID screening center were issued RTLS tags (CADI Scientific) for contact tracing. In total, 18 physicians were deployed to the NCID screening center from May 10 to May 20, 2020. The physicians activated the TraceTogether app (version 1.6; GovTech) on their smartphones during shifts and urged their patients to use the app. We compared patient contacts identified by TraceTogether and those identified by RTLS tags within the NCID vicinity during physicians’ 10-day posting. We also validated both digital contact tracing tools by verifying the physician-patient contacts with the electronic medical records of 156 patients who attended the NCID screening center over a 24-hour time frame within the study period. RTLS tags had a high sensitivity of 95.3% for detecting patient contacts identified either by the system or TraceTogether while TraceTogether had an overall sensitivity of 6.5% and performed significantly better on Android phones than iPhones (Android: 9.7%, iPhone: 2.7%; P<.001). When validated against the electronic medical records, RTLS tags had a sensitivity of 96.9% and specificity of 83.1%, while TraceTogether only detected 2 patient contacts with physicians who did not attend to them. TraceTogether had a much lower sensitivity than RTLS tags for identifying patient contacts in a clinical setting. Although the tag-based RTLS performed well for contact tracing in a clinical setting, its implementation in the community would be more challenging than TraceTogether. Given the uncertainty of the adoption and capabilities of contact tracing apps, policy makers should be cautioned against overreliance on such apps for contact tracing. Nonetheless, leveraging technology to augment conventional manual contact tracing is a necessary move for returning some normalcy to life during the long haul of the COVID-19 pandemic.
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