Department of Bioengineering, University of California San Diegogrid.266100.3, La Jolla, California, USA.
Department of Pediatrics, University of California San Diego, La Jolla, California, USA.
mSystems. 2022 Aug 30;7(4):e0010322. doi: 10.1128/msystems.00103-22. Epub 2022 Jun 15.
Surface sampling for SARS-CoV-2 RNA detection has shown considerable promise to detect exposure of built environments to infected individuals shedding virus who would not otherwise be detected. Here, we compare two popular sampling media (VTM and SDS) and two popular workflows (Thermo and PerkinElmer) for implementation of a surface sampling program suitable for environmental monitoring in public schools. We find that the SDS/Thermo pipeline shows superior sensitivity and specificity, but that the VTM/PerkinElmer pipeline is still sufficient to support surface surveillance in any indoor setting with stable cohorts of occupants (e.g., schools, prisons, group homes, etc.) and may be used to leverage existing investments in infrastructure. The ongoing COVID-19 pandemic has claimed the lives of over 5 million people worldwide. Due to high density occupancy of indoor spaces for prolonged periods of time, schools are often of concern for transmission, leading to widespread school closings to combat pandemic spread when cases rise. Since pediatric clinical testing is expensive and difficult from a consent perspective, we have deployed surface sampling in SASEA (Safer at School Early Alert), which allows for detection of SARS-CoV-2 from surfaces within a classroom. In this previous work, we developed a high-throughput method which requires robotic automation and specific reagents that are often not available for public health laboratories such as the San Diego County Public Health Laboratory (SDPHL). Therefore, we benchmarked our method (Thermo pipeline) against SDPHL's (PerkinElmer) more widely used method for the detection and prediction of SARS-CoV-2 exposure. While our method shows superior sensitivity (false-negative rate of 9% versus 27% for SDPHL), the SDPHL pipeline is sufficient to support surface surveillance in indoor settings. These findings are important since they show that existing investments in infrastructure can be leveraged to slow the spread of SARS-CoV-2 not in just the classroom but also in prisons, nursing homes, and other high-risk, indoor settings.
表面采样用于检测 SARS-CoV-2 RNA 已显示出很大的潜力，可以检测到那些因未被检测到而感染病毒的感染者所接触的建筑环境中的暴露情况。在这里，我们比较了两种流行的采样介质（VTM 和 SDS）和两种流行的工作流程（Thermo 和 PerkinElmer），以实施适合于公立学校环境监测的表面采样计划。我们发现 SDS/Thermo 方案具有更高的灵敏度和特异性，但 VTM/PerkinElmer 方案仍然足以支持任何室内环境中的表面监测，这些环境的居住者具有稳定的人群（例如学校、监狱、集体宿舍等），并且可以利用现有的基础设施投资。
目前的 COVID-19 大流行已在全球范围内夺走了超过 500 万人的生命。由于室内空间的高密度占用时间较长，学校通常是关注的焦点，因为病例增加时，学校会关闭以控制疫情蔓延。由于儿科临床检测从同意的角度来看既昂贵又困难，因此我们已经在 SASEA（学校早期预警更安全）中部署了表面采样，该方法可以从教室中的表面检测到 SARS-CoV-2。在之前的工作中，我们开发了一种高通量方法，该方法需要机器人自动化和特定的试剂，而这些试剂通常不适用于公共卫生实验室，例如圣地亚哥县公共卫生实验室（SDPHL）。因此，我们将我们的方法（Thermo 方案）与 SDPHL（PerkinElmer）更广泛使用的检测和预测 SARS-CoV-2 暴露的方法进行了基准测试。虽然我们的方法显示出更高的灵敏度（假阴性率为 9%，而 SDPHL 为 27%），但 SDPHL 方案足以支持室内环境中的表面监测。这些发现很重要，因为它们表明可以利用现有的基础设施投资来减缓 SARS-CoV-2 的传播，不仅在教室中，而且在监狱、养老院和其他高风险的室内环境中也是如此。
