Implementation of Practical Surface SARS-CoV-2 Surveillance in School Settings

Victor J Cantú; Pedro Belda-Ferre; Rodolfo A Salido; Rebecca Tsai; Brett Austin; William Jordan; Menka Asudani; Amanda Walster; Celestine G Magallanes; Holly Valentine; Araz Manjoonian; Carrissa Wijaya; Vinton Omaleki; Karenina Sanders; Stefan Aigner; Nathan A Baer; Maryann Betty; Anelizze Castro-Martínez; Willi Cheung; Evelyn S Crescini; Peter De Hoff; Emily Eisner; Abbas Hakim; Bhavika Kapadia; Alma L Lastrella; Elijah S Lawrence; Toan T Ngo; Tyler Ostrander; Shashank Sathe; Phoebe Seaver; Elizabeth W Smoot; Aaron F Carlin; Gene W Yeo; Louise C Laurent; Anna Liza Manlutac; Rebecca Fielding-Miller; Rob Knight

doi:10.1128/msystems.00103-22

Implementation of Practical Surface SARS-CoV-2 Surveillance in School Settings

mSystems. 2022 Aug 30;7(4):e0010322. doi: 10.1128/msystems.00103-22. Epub 2022 Jun 15.

Authors

Victor J Cantú¹, Pedro Belda-Ferre^{2

3}, Rodolfo A Salido¹, Rebecca Tsai², Brett Austin⁴, William Jordan⁴, Menka Asudani⁴, Amanda Walster⁴, Celestine G Magallanes^{5

6}, Holly Valentine^{5

6}, Araz Manjoonian^{7

8}, Carrissa Wijaya⁷, Vinton Omaleki⁷, Karenina Sanders², Stefan Aigner^{3

6

9}, Nathan A Baer³, Maryann Betty^{2

3

10}, Anelizze Castro-Martínez³, Willi Cheung^{3

6

8}, Evelyn S Crescini³, Peter De Hoff^{3

5

6}, Emily Eisner³, Abbas Hakim³, Bhavika Kapadia³, Alma L Lastrella³, Elijah S Lawrence³, Toan T Ngo³, Tyler Ostrander³, Shashank Sathe^{3

6

9}, Phoebe Seaver³, Elizabeth W Smoot³, Aaron F Carlin¹¹, Gene W Yeo^{3

6

9}, Louise C Laurent^{5

6}, Anna Liza Manlutac⁴, Rebecca Fielding-Miller⁷, Rob Knight^{1

12

13}

Affiliations

¹ Department of Bioengineering, University of California San Diegogrid.266100.3, La Jolla, California, USA.
² Department of Pediatrics, University of California San Diego, La Jolla, California, USA.
³ Expedited COVID Identification Environment (EXCITE) Laboratory, Department of Pediatrics, University of California San Diego, La Jolla, California, USA.
⁴ San Diego County Public Health Lab, San Diego, California, USA.
⁵ Department of Obstetrics, Gynecology, and Reproductive Sciences, University of California San Diego, La Jolla, California, USA.
⁶ Sanford Consortium of Regenerative Medicine, University of California San Diego, La Jolla, California, USA.
⁷ Herbert Wertheim School of Public Health, University of California San Diego, La Jolla, California, USA.
⁸ San Diego State University, San Diego, California, USA.
⁹ Department of Cellular and Molecular Medicine, University of California San Diego, La Jolla, California, USA.
¹⁰ Rady Children's Hospital, San Diego, California, USA.
¹¹ Division of Infectious Diseases and Global Public Health, Department of Medicine, University of California San Diego, La Jolla, California, USA.
¹² Department of Computer Science and Engineering, University of California San Diego, La Jolla, California, USA.
¹³ Center for Microbiome Innovation, University of California San Diego, La Jolla, California, USA.

Abstract

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. IMPORTANCE 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.

Keywords: COVID; SARS-CoV-2; environmental sampling; public health; qPCR.

Publication types

Research Support, N.I.H., Extramural
Research Support, Non-U.S. Gov't

MeSH terms

Automation
COVID-19* / diagnosis
Child
Humans
Pandemics / prevention & control
RNA, Viral
SARS-CoV-2*

Substances

RNA, Viral

Abstract

Publication types

MeSH terms

Substances

Grants and funding