Analysis of SARS-CoV-2 RNA Persistence across Indoor Surface Materials Reveals Best Practices for Environmental Monitoring Programs.

1. Department of Bioengineering, University of California San Diego, La Jolla, California, USA.
Authors
Salido RA¹
Cantú VJ¹
Foroughishafiei A¹
Saha A¹
Knight R^{1,

3}
(5 authors)
2. Division of Infectious Diseases and Global Public Health, Department of Medicine, University of California San Diego School of Medicine, La Jolla, California, USA.
Authors
Clark AE²
Carlin AF²
(2 authors)
3. Department of Pediatrics, University of California San Diego, La Jolla, California, USA.
Authors
Leibel SL^{3,

6}
Marotz CA^{3,

4}
Betty M^{3,

4}
Belda-Ferre P^{3,

4}
Tsai R^{3,

4}
Knight R^{1,

3}
(6 authors)
4. Expedited COVID Identification Environment (EXCITE) Laboratory, Department of Pediatrics, University of California San Diego, La Jolla, California, USA.
Authors
Hakim A⁴
Nouri A⁴
Lastrella AL⁴
Castro-Martínez A⁴
Plascencia A⁴
Kapadia BK⁴
Xia B⁴
Ruiz CA⁴
Marotz CA^{3,

4}
Maunder D⁴
Lawrence ES⁴
Smoot EW⁴
Eisner E⁴
Crescini ES⁴
Franco Vargas L⁴
Chacón M⁴
Betty M^{3,

4}
Machnicki M⁴
Wu MY⁴
Baer NA⁴
Belda-Ferre P^{3,

4}
De Hoff P^{4,

6}
Seaver P⁴
Ostrander RT⁴
Tsai R^{3,

4}
Sathe S^{4,

6}
Aigner S^{4,

6}
Ngo TT⁴
Barber T⁴
Cheung W^{4,

6}
(30 authors)
5. Herbert Wertheim School of Public Health, University of California, La Jolla, California, USA.
Authors
Kohn L⁵
Fielding-Miller R⁵
(2 authors)

Show all (6)

ORCIDs linked to this article

Show all (6)

Msystems, 02 Nov 2021, 6(6):e0113621
https://doi.org/10.1128/msystems.01136-21 PMID: 34726486 PMCID: PMC8562474

This article is in the Europe PMC Open access subset. Refer to the copyright information in the article for licensing details.

Free full text in Europe PMC

Abstract

Environmental monitoring in public spaces can be used to identify surfaces contaminated by persons with coronavirus disease 2019 (COVID-19) and inform appropriate infection mitigation responses. Research groups have reported detection of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) on surfaces days or weeks after the virus has been deposited, making it difficult to estimate when an infected individual may have shed virus onto a SARS-CoV-2-positive surface, which in turn complicates the process of establishing effective quarantine measures. In this study, we determined that reverse transcription-quantitative PCR (RT-qPCR) detection of viral RNA from heat-inactivated particles experiences minimal decay over 7 days of monitoring on eight out of nine surfaces tested. The properties of the studied surfaces result in RT-qPCR signatures that can be segregated into two material categories, rough and smooth, where smooth surfaces have a lower limit of detection. RT-qPCR signal intensity (average quantification cycle [Cq]) can be correlated with surface viral load using only one linear regression model per material category. The same experiment was performed with untreated viral particles on one surface from each category, with essentially identical results. The stability of RT-qPCR viral signal demonstrates the need to clean monitored surfaces after sampling to establish temporal resolution. Additionally, these findings can be used to minimize the number of materials and time points tested and allow for the use of heat-inactivated viral particles when optimizing environmental monitoring methods. IMPORTANCE Environmental monitoring is an important tool for public health surveillance, particularly in settings with low rates of diagnostic testing. Time between sampling public environments, such as hospitals or schools, and notifying stakeholders of the results should be minimal, allowing decisions to be made toward containing outbreaks of coronavirus disease 2019 (COVID-19). The Safer At School Early Alert program (SASEA) (https://saseasystem.org/), a large-scale environmental monitoring effort in elementary school and child care settings, has processed >13,000 surface samples for SARS-CoV-2, detecting viral signals from 574 samples. However, consecutive detection events necessitated the present study to establish appropriate response practices around persistent viral signals on classroom surfaces. Other research groups and clinical labs developing environmental monitoring methods may need to establish their own correlation between RT-qPCR results and viral load, but this work provides evidence justifying simplified experimental designs, like reduced testing materials and the use of heat-inactivated viral particles.

Free full text

mSystems. 2021 Nov-Dec; 6(6): e01136-21.

Published online 2021 Nov 2. https://doi.org/10.1128/mSystems.01136-21

PMCID: PMC8562474

PMID: 34726486

Analysis of SARS-CoV-2 RNA Persistence across Indoor Surface Materials Reveals Best Practices for Environmental Monitoring Programs

Rodolfo A. Salido,^#^a Victor J. Cantú,^#^a Alex E. Clark,^b Sandra L. Leibel,^c
,^d Anahid Foroughishafiei,^a Anushka Saha,^a Abbas Hakim,^e Alhakam Nouri,^e Alma L. Lastrella,^e Anelizze Castro-Martínez,^e Ashley Plascencia,^e Bhavika K. Kapadia,^e Bing Xia,^e Christopher A. Ruiz,^e Clarisse A. Marotz,^c
,^e Daniel Maunder,^e Elijah S. Lawrence,^e Elizabeth W. Smoot,^e Emily Eisner,^e Evelyn S. Crescini,^e Laura Kohn,^f Lizbeth Franco Vargas,^e Marisol Chacón,^e Maryann Betty,^c
,^e
,^g Michal Machnicki,^e Min Yi Wu,^e Nathan A. Baer,^e Pedro Belda-Ferre,^c
,^e Peter De Hoff,^d
,^e
,^h Phoebe Seaver,^e R. Tyler Ostrander,^e Rebecca Tsai,^c
,^e Shashank Sathe,^d
,^e
,ⁱ Stefan Aigner,^d
,^e
,ⁱ Sydney C. Morgan,^d
,^h Toan T. Ngo,^e Tom Barber,^e Willi Cheung,^d
,^e
,^j Aaron F. Carlin,^b Gene W. Yeo,^d
,ⁱ Louise C. Laurent,^d
,^h Rebecca Fielding-Miller,^f and Rob Knight^a
,^c
,^k
,^l

Rodolfo A. Salido

^a Department of Bioengineering, University of California San Diego, La Jolla, California, USA,

Find articles by Rodolfo A. Salido

Victor J. Cantú

^a Department of Bioengineering, University of California San Diego, La Jolla, California, USA,

Find articles by Victor J. Cantú

Alex E. Clark

^b Division of Infectious Diseases and Global Public Health, Department of Medicine, University of California San Diego School of Medicine, La Jolla, California, USA,

Find articles by Alex E. Clark

Sandra L. Leibel

^c Department of Pediatrics, University of California San Diego, La Jolla, California, USA,

^d Sanford Consortium of Regenerative Medicine, University of California San Diego, La Jolla, California, USA,

Find articles by Sandra L. Leibel

Anahid Foroughishafiei

^a Department of Bioengineering, University of California San Diego, La Jolla, California, USA,

Find articles by Anahid Foroughishafiei

Anushka Saha

^a Department of Bioengineering, University of California San Diego, La Jolla, California, USA,

Find articles by Anushka Saha

Abbas Hakim

^e Expedited COVID Identification Environment (EXCITE) Laboratory, Department of Pediatrics, University of California San Diego, La Jolla, California, USA,

Find articles by Abbas Hakim

Alhakam Nouri

^e Expedited COVID Identification Environment (EXCITE) Laboratory, Department of Pediatrics, University of California San Diego, La Jolla, California, USA,

Find articles by Alhakam Nouri

Alma L. Lastrella

^e Expedited COVID Identification Environment (EXCITE) Laboratory, Department of Pediatrics, University of California San Diego, La Jolla, California, USA,

Find articles by Alma L. Lastrella

Anelizze Castro-Martínez

^e Expedited COVID Identification Environment (EXCITE) Laboratory, Department of Pediatrics, University of California San Diego, La Jolla, California, USA,

Find articles by Anelizze Castro-Martínez

Ashley Plascencia

^e Expedited COVID Identification Environment (EXCITE) Laboratory, Department of Pediatrics, University of California San Diego, La Jolla, California, USA,

Find articles by Ashley Plascencia

Bhavika K. Kapadia

^e Expedited COVID Identification Environment (EXCITE) Laboratory, Department of Pediatrics, University of California San Diego, La Jolla, California, USA,

Find articles by Bhavika K. Kapadia

Bing Xia

^e Expedited COVID Identification Environment (EXCITE) Laboratory, Department of Pediatrics, University of California San Diego, La Jolla, California, USA,

Find articles by Bing Xia

Christopher A. Ruiz

^e Expedited COVID Identification Environment (EXCITE) Laboratory, Department of Pediatrics, University of California San Diego, La Jolla, California, USA,

Find articles by Christopher A. Ruiz

Clarisse A. Marotz

^c Department of Pediatrics, University of California San Diego, La Jolla, California, USA,

^e Expedited COVID Identification Environment (EXCITE) Laboratory, Department of Pediatrics, University of California San Diego, La Jolla, California, USA,

Find articles by Clarisse A. Marotz

Daniel Maunder

^e Expedited COVID Identification Environment (EXCITE) Laboratory, Department of Pediatrics, University of California San Diego, La Jolla, California, USA,

Find articles by Daniel Maunder

Elijah S. Lawrence

^e Expedited COVID Identification Environment (EXCITE) Laboratory, Department of Pediatrics, University of California San Diego, La Jolla, California, USA,

Find articles by Elijah S. Lawrence

Elizabeth W. Smoot

^e Expedited COVID Identification Environment (EXCITE) Laboratory, Department of Pediatrics, University of California San Diego, La Jolla, California, USA,

Find articles by Elizabeth W. Smoot

Emily Eisner

^e Expedited COVID Identification Environment (EXCITE) Laboratory, Department of Pediatrics, University of California San Diego, La Jolla, California, USA,

Find articles by Emily Eisner

Evelyn S. Crescini

^e Expedited COVID Identification Environment (EXCITE) Laboratory, Department of Pediatrics, University of California San Diego, La Jolla, California, USA,

Find articles by Evelyn S. Crescini

Laura Kohn

^f Herbert Wertheim School of Public Health, University of California, La Jolla, California, USA,

Find articles by Laura Kohn

Lizbeth Franco Vargas

^e Expedited COVID Identification Environment (EXCITE) Laboratory, Department of Pediatrics, University of California San Diego, La Jolla, California, USA,

Find articles by Lizbeth Franco Vargas

Marisol Chacón

^e Expedited COVID Identification Environment (EXCITE) Laboratory, Department of Pediatrics, University of California San Diego, La Jolla, California, USA,

Find articles by Marisol Chacón

Maryann Betty

^c Department of Pediatrics, University of California San Diego, La Jolla, California, USA,

^e Expedited COVID Identification Environment (EXCITE) Laboratory, Department of Pediatrics, University of California San Diego, La Jolla, California, USA,

^g Rady Children’s Hospital, San Diego, California, USA,

Find articles by Maryann Betty

Michal Machnicki

^e Expedited COVID Identification Environment (EXCITE) Laboratory, Department of Pediatrics, University of California San Diego, La Jolla, California, USA,

Find articles by Michal Machnicki

Min Yi Wu

^e Expedited COVID Identification Environment (EXCITE) Laboratory, Department of Pediatrics, University of California San Diego, La Jolla, California, USA,

Find articles by Min Yi Wu

Nathan A. Baer

^e Expedited COVID Identification Environment (EXCITE) Laboratory, Department of Pediatrics, University of California San Diego, La Jolla, California, USA,

Find articles by Nathan A. Baer

Pedro Belda-Ferre

^c Department of Pediatrics, University of California San Diego, La Jolla, California, USA,

^e Expedited COVID Identification Environment (EXCITE) Laboratory, Department of Pediatrics, University of California San Diego, La Jolla, California, USA,

Find articles by Pedro Belda-Ferre

Peter De Hoff

^d Sanford Consortium of Regenerative Medicine, University of California San Diego, La Jolla, California, USA,

^e Expedited COVID Identification Environment (EXCITE) Laboratory, Department of Pediatrics, University of California San Diego, La Jolla, California, USA,

^h Department of Obstetrics, Gynecology, and Reproductive Sciences, University of California San Diego, La Jolla, California, USA,

Find articles by Peter De Hoff

Phoebe Seaver

^e Expedited COVID Identification Environment (EXCITE) Laboratory, Department of Pediatrics, University of California San Diego, La Jolla, California, USA,

Find articles by Phoebe Seaver

R. Tyler Ostrander

^e Expedited COVID Identification Environment (EXCITE) Laboratory, Department of Pediatrics, University of California San Diego, La Jolla, California, USA,

Find articles by R. Tyler Ostrander

Rebecca Tsai

^c Department of Pediatrics, University of California San Diego, La Jolla, California, USA,

^e Expedited COVID Identification Environment (EXCITE) Laboratory, Department of Pediatrics, University of California San Diego, La Jolla, California, USA,

Find articles by Rebecca Tsai

Shashank Sathe

^d Sanford Consortium of Regenerative Medicine, University of California San Diego, La Jolla, California, USA,

^e Expedited COVID Identification Environment (EXCITE) Laboratory, Department of Pediatrics, University of California San Diego, La Jolla, California, USA,

ⁱ Department of Cellular and Molecular Medicine, University of California San Diego, La Jolla, California, USA,

Find articles by Shashank Sathe

Stefan Aigner

^d Sanford Consortium of Regenerative Medicine, University of California San Diego, La Jolla, California, USA,

^e Expedited COVID Identification Environment (EXCITE) Laboratory, Department of Pediatrics, University of California San Diego, La Jolla, California, USA,

ⁱ Department of Cellular and Molecular Medicine, University of California San Diego, La Jolla, California, USA,

Find articles by Stefan Aigner

Sydney C. Morgan

^d Sanford Consortium of Regenerative Medicine, University of California San Diego, La Jolla, California, USA,

^h Department of Obstetrics, Gynecology, and Reproductive Sciences, University of California San Diego, La Jolla, California, USA,

Find articles by Sydney C. Morgan

Toan T. Ngo

^e Expedited COVID Identification Environment (EXCITE) Laboratory, Department of Pediatrics, University of California San Diego, La Jolla, California, USA,

Find articles by Toan T. Ngo

Tom Barber

^e Expedited COVID Identification Environment (EXCITE) Laboratory, Department of Pediatrics, University of California San Diego, La Jolla, California, USA,

Find articles by Tom Barber

Willi Cheung

^d Sanford Consortium of Regenerative Medicine, University of California San Diego, La Jolla, California, USA,

^e Expedited COVID Identification Environment (EXCITE) Laboratory, Department of Pediatrics, University of California San Diego, La Jolla, California, USA,

^j San Diego State University, San Diego, California, USA,

Find articles by Willi Cheung

Aaron F. Carlin

^b Division of Infectious Diseases and Global Public Health, Department of Medicine, University of California San Diego School of Medicine, La Jolla, California, USA,

Find articles by Aaron F. Carlin

Gene W. Yeo

^d Sanford Consortium of Regenerative Medicine, University of California San Diego, La Jolla, California, USA,

ⁱ Department of Cellular and Molecular Medicine, University of California San Diego, La Jolla, California, USA,

Find articles by Gene W. Yeo

Louise C. Laurent

^d Sanford Consortium of Regenerative Medicine, University of California San Diego, La Jolla, California, USA,

^h Department of Obstetrics, Gynecology, and Reproductive Sciences, University of California San Diego, La Jolla, California, USA,

Find articles by Louise C. Laurent

Rebecca Fielding-Miller

^f Herbert Wertheim School of Public Health, University of California, La Jolla, California, USA,

Find articles by Rebecca Fielding-Miller

Rob Knight

^a Department of Bioengineering, University of California San Diego, La Jolla, California, USA,

^c Department of Pediatrics, University of California San Diego, La Jolla, California, USA,

^k Department of Computer Science and Engineering, University of California San Diego, La Jolla, California, USA,

^l Center for Microbiome Innovation, Jacobs School of Engineering, University of California San Diego, La Jolla, California, USA,

Find articles by Rob Knight

Marta M. Gaglia, Editor

Marta M. Gaglia, Tufts University;

Author information Article notes Copyright and License information Disclaimer

^a Department of Bioengineering, University of California San Diego, La Jolla, California, USA,

^b Division of Infectious Diseases and Global Public Health, Department of Medicine, University of California San Diego School of Medicine, La Jolla, California, USA,

^c Department of Pediatrics, University of California San Diego, La Jolla, California, USA,

^d Sanford Consortium of Regenerative Medicine, University of California San Diego, La Jolla, California, USA,

^e Expedited COVID Identification Environment (EXCITE) Laboratory, Department of Pediatrics, University of California San Diego, La Jolla, California, USA,

^f Herbert Wertheim School of Public Health, University of California, La Jolla, California, USA,

^g Rady Children’s Hospital, San Diego, California, USA,

^h Department of Obstetrics, Gynecology, and Reproductive Sciences, University of California San Diego, La Jolla, California, USA,

ⁱ Department of Cellular and Molecular Medicine, University of California San Diego, La Jolla, California, USA,

^j San Diego State University, San Diego, California, USA,

^k Department of Computer Science and Engineering, University of California San Diego, La Jolla, California, USA,

^l Center for Microbiome Innovation, Jacobs School of Engineering, University of California San Diego, La Jolla, California, USA,

Corresponding author.

^#Contributed equally.

Rebecca Fielding-Miller: ude.dscu.htlaeh@rellimgnidleifr; Rob Knight: ude.dscu.gne@thginkbor

Rodolfo A. Salido and Victor J. Cantú contributed equally to this work. Author order was determined by relative contribution to high throughput environmental monitoring method development.

Citation Salido RA, Cantú VJ, Clark AE, Leibel SL, Foroughishafiei A, Saha A, Hakim A, Nouri A, Lastrella AL, Castro-Martínez A, Plascencia A, Kapadia BK, Xia B, Ruiz CA, Marotz CA, Maunder D, Lawrence ES, Smoot EW, Eisner E, Crescini ES, Kohn L, Franco Vargas L, Chacón M, Betty M, Machnicki M, Wu MY, Baer NA, Belda-Ferre P, De Hoff P, Seaver P, Ostrander RT, Tsai R, Sathe S, Aigner S, Morgan SC, Ngo TT, Barber T, Cheung W, Carlin AF, Yeo GW, Laurent LC, Fielding-Miller R, Knight R. 2021. Analysis of SARS-CoV-2 RNA persistence across indoor surface materials reveals best practices for environmental monitoring programs. mSystems 6:e01136-21. https://doi.org/10.1128/mSystems.01136-21.

This article has been cited by other articles in PMC.

Go to:

Associated Data

Supplementary Materials: TABLE S1.
Statistically significant differences from pairwise Mann-Whitney U tests between ranked values of average Cqs from viral gene calls grouped by surface type after correction for multiple comparisons (**, FDR-Benjamin/Hochberg, alpha=0.005; N.s. = not significant). Download Table S1, DOCX file, 0.01 MB.
Copyright © 2021 Salido et al. This content is distributed under the terms of the Creative Commons Attribution 4.0 International license.
FIG S1.
(A) Swarm plot showing distribution of average Cqs of viral gene calls for acrylic and carpet (olefin) surfaces for both heat-inactivated and untreated samples. (B) Swarm plot comparing distribution of average Cqs of viral gene calls for heat-inactivated or untreated samples. (C) Linear regression on Cqs from paired samples between heat-inactivated and untreated samples. Download FIG S1, TIF file, 5 MB.
Copyright © 2021 Salido et al. This content is distributed under the terms of the Creative Commons Attribution 4.0 International license.
TEXT S1.
Materials and Methods. Description of materials and methods for experimental design and sample processing. Download Text S1, DOCX file, 0.02 MB.
Copyright © 2021 Salido et al. This content is distributed under the terms of the Creative Commons Attribution 4.0 International license.
FIG S2.
Line plots showing the average Cqs of RT-qPCR viral signals for positive samples (circles) over 7 days with overlaid scatterplots showing Cqs for inconclusive samples (diamonds). Inconclusive samples increase sensitivity of viral detection through surface swabs, seen as increased data points for low viral spike-ins in comparison to positive samples alone. Viral spike-in concentrations reported as GEs from ddPCR. Download FIG S2, TIF file, 6.6 MB.
Copyright © 2021 Salido et al. This content is distributed under the terms of the Creative Commons Attribution 4.0 International license.
TABLE S2.
Primer and probe sequences used for digital droplet PCR quantification of viral genome equivalents. Download Table S2, DOCX file, 0.01 MB.
Copyright © 2021 Salido et al. This content is distributed under the terms of the Creative Commons Attribution 4.0 International license.
TABLE S3.
Individual SARS-CoV-2 target gene-positive criteria. Positive results were called on individual SARS-CoV-2 target genes that had a Cq of <37 with a confidence of >0.7. A positive result on the extraction control gene (MS2) was called when it had a Cq of <37 with a confidence of >0.3. Download Table S3, DOCX file, 0.01 MB.
Copyright © 2021 Salido et al. This content is distributed under the terms of the Creative Commons Attribution 4.0 International license.
TABLE S4.
RT-qPCR test result-positive criteria. A positive status was called for samples that had at least 2/3 positive calls on the SARS-CoV-2 target genes. An inconclusive status was called on samples with only 1/3 positive calls on the SARS-CoV-2 target genes. A SARS-CoV-2 detected result was called on samples where at least one out of three SARS-CoV-2 targets had a positive call; both positive and inconclusive status samples yielded SARS-CoV-2 detected results. A negative status was called on samples that had a positive call on the control gene and no positive calls on the SARS-CoV-2 target genes. An invalid result was called when neither the control gene nor the viral target genes generated a positive call. Download Table S4, DOCX file, 0.01 MB.
Copyright © 2021 Salido et al. This content is distributed under the terms of the Creative Commons Attribution 4.0 International license.
Reviewer comments
reviewer-comments.pdf (257K)

Go to:

ABSTRACT

IMPORTANCE Environmental monitoring is an important tool for public health surveillance, particularly in settings with low rates of diagnostic testing. Time between sampling public environments, such as hospitals or schools, and notifying stakeholders of the results should be minimal, allowing decisions to be made toward containing outbreaks of coronavirus disease 2019 (COVID-19). The Safer At School Early Alert program (SASEA) (https://saseasystem.org/), a large-scale environmental monitoring effort in elementary school and child care settings, has processed >13,000 surface samples for SARS-CoV-2, detecting viral signals from 574 samples. However, consecutive detection events necessitated the present study to establish appropriate response practices around persistent viral signals on classroom surfaces. Other research groups and clinical labs developing environmental monitoring methods may need to establish their own correlation between RT-qPCR results and viral load, but this work provides evidence justifying simplified experimental designs, like reduced testing materials and the use of heat-inactivated viral particles.

KEYWORDS: COVID-19, RT-qPCR, SARS-CoV-2, environmental monitoring, heat-inactivated, surface sampling, swab

Go to:

OBSERVATION

Development and characterization of methods for environmental monitoring of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) remain important areas of research for identifying and mitigating potential outbreaks as the global pandemic continues. Environmental monitoring offers indirect detection of possibly infectious individuals through noninvasive sampling. In spaces with relatively consistent occupants, detection of SARS-CoV-2 from environmental samples can help identify coronavirus disease 2019 (COVID-19)-infected individuals, ideally before further transmission. Environmental monitoring can also alert public health leadership to the potential presence of an infection even in settings with low diagnostic testing uptake, allowing for the implementation of enhanced nonpharmaceutical interventions (i.e., double masking, increased hand hygiene, improved ventilation efforts) even in the absence of positive diagnostic tests.

SARS-CoV-2 particles are shed by symptomatic and asymptomatic carriers (1) and have been detected on various surfaces (2,–5). Viral signatures have been demonstrated to persist up to 4weeks in bulk floor dust collected from a room with a quarantined individual (6). Previous environmental monitoring studies have detected SARS-CoV-2 on surfaces contaminated by infected individuals in hospitals and congregate care facilities (6,–10). Thus, indoor surface sampling can be valuable for detection of infected persons indoors, where transmission risk is highest (11). The Safer At School Early Alert program (SASEA) (https://saseasystem.org/) uses environmental monitoring and collected over 13,000 surface swabs, but we need more information to clarify what these data are telling us over time.

We sought to characterize temporal dynamics underlying detection of SARS-CoV-2 signals from surface swabs from a variety of common indoor surface types using reverse transcription-quantitative PCR (RT-qPCR). The Centers for Disease Control and Prevention (CDC) maintains that the risk of fomite transmission of SARS-CoV-2 is low (12). This study makes no claims of attempting to understand the possibility of or mechanisms behind infection of virus transmitted by fomites but rather on whether and how negative and positive RT-qPCR detection from surface swabs can enable decision-making in outbreak mitigation, focused clinical testing of individuals, and safe reopening of high-traffic, public spaces.

We used RT-qPCR to detect heat-inactivated viral particles on nine surface materials and monitored the persistence of the heat-inactivated virus for 7days. Each material—acrylic, steel, glass, ceramic tile, melamine-finished particleboard (MFP), painted drywall, vinyl flooring, and two different carpets (olefin and polyester)—was divided into 5-cm by 5-cm grids, and each 25-cm² square surface of the grid was inoculated with 10μl of either a dilution series of heat-inactivated SARS-CoV-2 particles or water. The eight-point dilution series was based on viral genomic equivalents (GEs) as measured by digital droplet PCR (ddPCR). The inoculum dried for 1h before swabbing. Every 24h postinoculation, an unswabbed section of each material grid was sampled, for a total of 7 days, including the initial postinoculation swab.

To determine whether use of heat-inactivated viral particles in testing and validating environmental monitoring methods reflects results obtained using untreated virus, we compared detection of heat-inactivated SARS-CoV-2 (strain WA-1, SA-WA1/2020) and of authentic, untreated SARS-CoV-2 (variant of concern Beta, isolate B.1.351, hCoV-19/USA/MD-HP01542/2021) on two materials under biosafety level 3 (BSL-3) conditions.

Findings.

Linear regression of signal intensity (average Cq of viral gene calls) on elapsed time since inoculation (days) for each dilution showed minimal decay of viral RNA on eight of nine surface types over 6days (Fig. 1). The average decay slope for each surface type (m-bar) did not differ significantly from zero (mean=0.0407, standard deviation [SD] =0.210). RT-qPCR signal decayed with time only on glass (m-bar=0.401, SD =0.212, differing from the population mean by >1.5 standard deviations).

An external file that holds a picture, illustration, etc.
Object name is msystems.01136-21-f001.jpg

FIG 1

Scatterplots showing the average Cq values of RT-qPCR viral gene calls for corresponding heat-inactivated viral spike-in over 7 days. Viral spike-in concentrations reported as GEs from ddPCR. Linear regressions of average Cq values on days since inoculation per spike-in were overlaid on the measured data. The average decay slope (m-bar) is reported alongside each surface type.

A two-way repeated measure analysis of variance (ANOVA) on viral signal intensity (average Cq) revealed that surface type explains more observed variation in Cq than does time since inoculation at the highest concentration (5×10⁵ GEs) (Fig. 2A). A Kruskal-Wallis H test confirmed that mean Cqs differ significantly across surface types (H=60.86, P = 2.49×10⁻⁹) (Fig. 2B), but not across days since inoculation (H=1.34, P = 0.97) (Fig. 2C). Pairwise Mann-Whitney U tests comparing ranked values of Cqs from samples grouped by surface type highlight that both carpet materials (olefin and polyester) are significantly different, after correcting for multiple comparisons (false discovery rate [FDR]-Benjamini/Hochberg, alpha=0.005), from all other surfaces, but not from each other (Fig. 2B). Other pairwise, significant differences between materials are summarized in Table S1 in the supplemental material. A clustermap of the U statistic from the pairwise comparisons effectively clusters samples by material properties, with rough surfaces clustering away from smooth ones (Fig. 2D).

An external file that holds a picture, illustration, etc.
Object name is msystems.01136-21-f002.jpg

FIG 2

(A to C) 3D scatterplots showing distribution of average Cq values of viral gene calls over 7 days for nine different surfaces inoculated with 5×10⁵ GEs (nine surfaces for heat-inactivated virus [circles], two surfaces [acrylic and olefin carpet] for infectious virus [diamonds]). The distribution of Cqs differs significantly across surface types (B), but not across days since inoculation (C). (D) Clustermap of the U statistic from pairwise Mann-Whitney U tests between surface types. (E and F) Standard curves relating surface viral load (log₂ spike-in) to average Cq values across all time points for smooth (E) and rough (F) surface types.

TABLE S1

Statistically significant differences from pairwise Mann-Whitney U tests between ranked values of average Cqs from viral gene calls grouped by surface type after correction for multiple comparisons (**, FDR-Benjamin/Hochberg, alpha=0.005; N.s. = not significant). Download Table S1, DOCX file, 0.01 MB.

This content is distributed under the terms of the Creative Commons Attribution 4.0 International license.

Because RT-qPCR signal intensity for most surfaces was time invariant, time-collapsed linear regression models relating viral spike-in concentration (log₂ spike-in) to average Cq act as standard curves for estimating viral load on different monitored surfaces from Cq. After segregating samples based on the qualitative material categories of smooth or rough, linear regressions aggregating all time points yielded one standard curve for smooth surfaces (m = −0.77, b=40.41, r = −0.93) (Fig. 2E) and another for rough surfaces (m = −0.52, b=39.90, r = −0.84) (Fig. 2F). The reduced slope of the latter curve stems from higher loss of spiked-in viral signal to the rough surface matrix.

To ensure that viral signal stability was not a consequence of selection for resilient viral particles through heat inactivation, we repeated a subset of experiments using infectious virus (untreated) in a BSL-3 laboratory using the B.1.351/Beta variant of SARS-CoV-2 originally identified in South Africa. Due to space limitations in the BSL-3 facility, the untreated virus experiment only included two surface types, acrylic and carpet (olefin) but used the same dilution series and sampling plan.

Results from untreated and heat-inactivated virus are concordant. Untreated virus samples cluster with respect to surface type rather than virion status (heat inactivated or untreated) (Fig. 2D). When evaluating acrylic and carpet (olefin) samples alone, a Kruskal-Wallis H test shows significant differences in the means of Cqs across all groups when samples are grouped by surface type (H=16.37, P = 0.00095) (see Fig. S1A in the supplemental material), but not when grouped by virion status (H=1.96, P = 0.161) (Fig. S1B). Furthermore, linear regression on Cq from paired samples between the heat-inactivated and untreated virus experiments show nearly exact correlation despite the use of different variants (m=1.05, r=0.97) (Fig. S1C).

FIG S1

(A) Swarm plot showing distribution of average Cqs of viral gene calls for acrylic and carpet (olefin) surfaces for both heat-inactivated and untreated samples. (B) Swarm plot comparing distribution of average Cqs of viral gene calls for heat-inactivated or untreated samples. (C) Linear regression on Cqs from paired samples between heat-inactivated and untreated samples. Download FIG S1, TIF file, 5 MB.

This content is distributed under the terms of the Creative Commons Attribution 4.0 International license.

Discussion.

We show that detecting SARS-CoV-2 RNA on indoor surfaces in environments potentially exposed to COVID-19-infected individuals is effective across a variety of surfaces and a range of initial viral loads. Our swabbing and RT-qPCR methods have greater sensitivity from smooth surfaces (such as MFP—commonly found on desktops—or vinyl flooring) than rough surfaces (carpet). The stability of the viral signal across time limits the ability to estimate when the surface was inoculated but demonstrates that signal can be detected a week postexposure. There is a possibility that viral signal could decay over a longer period of time, but because the motivation behind this study was to improve temporal resolution over shorter periods, this was beyond the scope of the present work. To improve temporal resolution, surfaces swabbed for environmental monitoring should be cleaned with soap and water, following CDC recommendations (13), in order to remove viral signals (12). Previous work with comparable methods for SARS-CoV-2 detection from surfaces demonstrated that washing contaminated objects with household dishwashing detergent for≥1 min removed enough viral RNA traces so that only 20% of the severely contaminated objects had detectable viral RNA. Furthermore, the average viral load of the washed surfaces was reduced by ~2.5 Cqs in comparison to untreated objects (14). Thus, cleaning monitored surfaces with soap and water improves the probability of distinction between persistent or separate exposures in subsequent SARS-CoV-2 detection events.

Although direct inoculation of surfaces with viral particles does not represent interaction with an infected individual in a real-world scenario, we do directly show that untreated and heat-inactivated SARS-CoV-2 particles have similar detectability and stability across surface types. These findings allow the use of heat-inactivated particles in testing and validating environmental monitoring methods and remove the burden of performing such experiments in BSL-3 laboratories.

TEXT S1

Materials and Methods. Description of materials and methods for experimental design and sample processing. Download Text S1, DOCX file, 0.02 MB.

This content is distributed under the terms of the Creative Commons Attribution 4.0 International license.

FIG S2

Line plots showing the average Cqs of RT-qPCR viral signals for positive samples (circles) over 7 days with overlaid scatterplots showing Cqs for inconclusive samples (diamonds). Inconclusive samples increase sensitivity of viral detection through surface swabs, seen as increased data points for low viral spike-ins in comparison to positive samples alone. Viral spike-in concentrations reported as GEs from ddPCR. Download FIG S2, TIF file, 6.6 MB.

This content is distributed under the terms of the Creative Commons Attribution 4.0 International license.

TABLE S2

Primer and probe sequences used for digital droplet PCR quantification of viral genome equivalents. Download Table S2, DOCX file, 0.01 MB.

This content is distributed under the terms of the Creative Commons Attribution 4.0 International license.

TABLE S3

Individual SARS-CoV-2 target gene-positive criteria. Positive results were called on individual SARS-CoV-2 target genes that had a Cq of <37 with a confidence of >0.7. A positive result on the extraction control gene (MS2) was called when it had a Cq of <37 with a confidence of >0.3. Download Table S3, DOCX file, 0.01 MB.

This content is distributed under the terms of the Creative Commons Attribution 4.0 International license.

TABLE S4

RT-qPCR test result-positive criteria. A positive status was called for samples that had at least 2/3 positive calls on the SARS-CoV-2 target genes. An inconclusive status was called on samples with only 1/3 positive calls on the SARS-CoV-2 target genes. A SARS-CoV-2 detected result was called on samples where at least one out of three SARS-CoV-2 targets had a positive call; both positive and inconclusive status samples yielded SARS-CoV-2 detected results. A negative status was called on samples that had a positive call on the control gene and no positive calls on the SARS-CoV-2 target genes. An invalid result was called when neither the control gene nor the viral target genes generated a positive call. Download Table S4, DOCX file, 0.01 MB.

This content is distributed under the terms of the Creative Commons Attribution 4.0 International license.

Go to:

Supplementary Material

Reviewer comments:

Click here to view.^{(257K, pdf)}

Go to:

ACKNOWLEDGMENTS

We thank our partner schools and citizen scientists at 15 sites across five districts in San Diego county.

This research was supported by NIH grant (K08AI130381) and a Career Award for Medical Scientists from the Burroughs Wellcome Fund to A.F.C., NIH grant (K01MH112436) to R.F.-M., and the County of San Diego Health and Human Services Agency (contract 563236). This work was performed with the support of the Genomics and Sequencing Core at the UC San Diego Center for AIDS Research (P30 AI036214), the VA San Diego Healthcare System, and the Veterans Medical Research Foundation. The following reagent was deposited by the Centers for Disease Control and Prevention and obtained through BEI Resources, NIAID, NIH: SARS-related coronavirus 2, isolate USA-WA1/2020, NR-52281. The following reagent was obtained through BEI Resources, NIAID, NIH: SARS-related coronavirus 2, Isolate hCoV-19/South Africa/KRISP-K005325/2020, NR-54009, contributed by Alex Sigal and Tulio de Oliveira.

Go to:

REFERENCES

1. Meyerowitz EA, Richterman A, Gandhi RT, Sax PE. 2021. Transmission of SARS-CoV-2: a review of viral, host, and environmental factors. Ann Intern Med 174:1037. 10.7326/L21-0166. [Abstract] [CrossRef] [Google Scholar]

2. Parker CW, Singh N, Tighe S, Blachowicz A, Wood JM, Seuylemezian A, Vaishampayan P, Urbaniak C, Hendrickson R, Laaguiby P, Clark K, Clement BG, O’Hara NB, Couto-Rodriguez M, Bezdan D, Mason CE, Venkateswaran K. 2020. End-to-end protocol for the detection of SARS-CoV-2 from built environments. mSystems 5:e00771-20. 10.1128/mSystems.00771-20. [Europe PMC free article] [Abstract] [CrossRef] [Google Scholar]

3. van Doremalen N, Bushmaker T, Morris DH, Holbrook MG, Gamble A, Williamson BN, Tamin A, Harcourt JL, Thornburg NJ, Gerber SI, Lloyd-Smith JO, de Wit E, Munster VJ. 2020. Aerosol and surface stability of SARS-CoV-2 as compared with SARS-CoV-1. N Engl J Med 382:1564–1567. 10.1056/NEJMc2004973. [Europe PMC free article] [Abstract] [CrossRef] [Google Scholar]

4. Chin AWH, Chu JTS, Perera MRA, Hui KPY, Yen H-L, Chan MCW, Peiris M, Poon LLM. 2020. Stability of SARS-CoV-2 in different environmental conditions. Lancet Microbe 1:e10. 10.1016/S2666-5247(20)30003-3. [Europe PMC free article] [Abstract] [CrossRef] [Google Scholar]

5. Harbourt DE, Haddow AD, Piper AE, Bloomfield H, Kearney BJ, Fetterer D, Gibson K, Minogue T. 2020. Modeling the stability of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) on skin, currency, and clothing. PLoS Negl Trop Dis 14:e0008831. 10.1371/journal.pntd.0008831. [Europe PMC free article] [Abstract] [CrossRef] [Google Scholar]

6. Renninger N, Nastasi N, Bope A, Cochran SJ, Haines SR, Balasubrahmaniam N, Stuart K, Bivins A, Bibby K, Hull NM, Dannemiller KC. 2021. Indoor dust as a matrix for surveillance of COVID-19. mSystems 6:e01350-20. 10.1128/mSystems.01350-20. [Europe PMC free article] [Abstract] [CrossRef] [Google Scholar]

7. Ye G, Lin H, Chen S, Wang S, Zeng Z, Wang W, Zhang S, Rebmann T, Li Y, Pan Z, Yang Z, Wang Y, Wang F, Qian Z, Wang X. 2020. Environmental contamination of SARS-CoV-2 in healthcare premises. J Infect 81:e1–e5. 10.1016/j.jinf.2020.04.034. [Europe PMC free article] [Abstract] [CrossRef] [Google Scholar]

8. Ben-Shmuel A, Brosh-Nissimov T, Glinert I, Bar-David E, Sittner A, Poni R, Cohen R, Achdout H, Tamir H, Yahalom-Ronen Y, Politi B, Melamed S, Vitner E, Cherry L, Israeli O, Beth-Din A, Paran N, Israely T, Yitzhaki S, Levy H, Weiss S. 2020. Detection and infectivity potential of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) environmental contamination in isolation units and quarantine facilities. Clin Microbiol Infect 26:1658–1662. 10.1016/j.cmi.2020.09.004. [Europe PMC free article] [Abstract] [CrossRef] [Google Scholar]

9. Jiang FC, Jiang XL, Wang ZG, Meng ZH, Shao SF, Anderson BD, Ma MJ. 2020. Detection of severe acute respiratory syndrome coronavirus 2 RNA on surfaces in quarantine rooms. Emerg Infect Dis 26:2162–2164. 10.3201/eid2609.201435. [Europe PMC free article] [Abstract] [CrossRef] [Google Scholar]

10. Marotz C, Belda-Ferre P, Ali F, Das P, Huang S, Cantrell K, Jiang L, Martino C, Diner RE, Rahman G, McDonald D, Armstrong G, Kodera S, Donato S, Ecklu-Mensah G, Gottel N, Salas Garcia MC, Chiang LY, Salido RA, Shaffer JP, Bryant MK, Sanders K, Humphrey G, Ackermann G, Haiminen N, Beck KL, Kim HC, Carrieri AP, Parida L, Vázquez-Baeza Y, Torriani FJ, Knight R, Gilbert J, Sweeney DA, Allard SM. 2021. SARS-CoV-2 detection status associates with bacterial community composition in patients and the hospital environment. Microbiome 9:132. 10.1186/s40168-021-01083-0. [Europe PMC free article] [Abstract] [CrossRef] [Google Scholar]

11. Centers for Disease Control and Prevention. Coronavirus (COVID-19) frequently asked questions. Centers for Disease Control and Prevention, Atlanta, GA. https://www.cdc.gov/coronavirus/2019-ncov/faq.html#Spread. Accessed 1 July 2021. [Google Scholar]

12. Centers for Disease Control and Prevention. 2021. SARS-CoV-2 and surface (fomite) transmission for indoor community environments. Centers for Disease Control and Prevention, Atlanta, GA. https://www.cdc.gov/coronavirus/2019-ncov/more/science-and-research/surface-transmission.html. Accessed 1 July 2021. [Abstract] [Google Scholar]

13. Centers for Disease Control and Prevention. 2021. Cleaning and disinfecting your home. Centers for Disease Control and Prevention, Atlanta, GA. https://www.cdc.gov/coronavirus/2019-ncov/prevent-getting-sick/disinfecting-your-home.html. Accessed 10 September 2021. [Google Scholar]

14. Salido RA, Morgan SC, Rojas MI, Magallanes CG, Marotz C, DeHoff P, Belda-Ferre P, Aigner S, Kado DM, Yeo GW, Gilbert JA, Laurent L, Rohwer F, Knight R. 2020. Handwashing and detergent treatment greatly reduce SARS-CoV-2 viral load on Halloween candy handled by COVID-19 patients. mSystems 5:e01074-20. 10.1128/mSystems.01074-20. [Europe PMC free article] [Abstract] [CrossRef] [Google Scholar]

Articles from mSystems are provided here courtesy of American Society for Microbiology (ASM)

Full text links

Read article at publisher's site: https://doi.org/10.1128/msystems.01136-21

Citations & impact

Impact metrics

Citations

Jump to Citations

Citations of article over time

Alternative metrics

Altmetric item for https://www.altmetric.com/details/116155979

Altmetric
Discover the attention surrounding your research
https://www.altmetric.com/details/116155979

Smart citations by scite.ai
Explore citation contexts and check if this article has been supported or disputed.
https://scite.ai/reports/10.1128/msystems.01136-21

Supporting

Mentioning

Contrasting

Article citations

Exploring indoor and outdoor dust as a potential tool for detection and monitoring of COVID-19 transmission.
Anupong S, Chadsuthi S, Hongsing P, Hurst C, Phattharapornjaroen P, Rad S M AH, Fernandez S, Huang AT, Vatanaprasan P, Saethang T, Luk-In S, Storer RJ, Ounjai P, Devanga Ragupathi NK, Kanthawee P, Ngamwongsatit N, Badavath VN, Thuptimdang W, Leelahavanichkul A, [...] Wannigama DL
iScience, 27(3):109043, 26 Jan 2024
Cited by: 1 article | PMID: 38375225 | PMCID: PMC10875567
This article is in the Europe PMC Open access subset. Refer to the copyright information in the article for licensing details.
Free full text in Europe PMC
Environmental surface surveillance during a COVID-19 outbreak in a school community: implications for controlling indirect transmission.
Cherem J, Alvarenga IM, Coelho ALRM, Souza KST, Rocha DA, de Castro PH, Pylro VS, Barçante JMP
Braz J Microbiol, 55(1):441-445, 24 Nov 2023
Cited by: 0 articles | PMID: 37996692
Safer at school early alert: an observational study of wastewater and surface monitoring to detect COVID-19 in elementary schools.
Fielding-Miller R, Karthikeyan S, Gaines T, Garfein RS, Salido RA, Cantu VJ, Kohn L, Martin NK, Wynn A, Wijaya C, Flores M, Omaleki V, Majnoonian A, Gonzalez-Zuniga P, Nguyen M, Vo AV, Le T, Duong D, Hassani A, [...] SEARCH
Lancet Reg Health Am, 19:100449, 20 Feb 2023
Cited by: 20 articles | PMID: 36844610 | PMCID: PMC9939935
This article is in the Europe PMC Open access subset. Refer to the copyright information in the article for licensing details.
Free full text in Europe PMC
SARS-CoV-2 surveillance with environmental surface sampling in public areas.
Mihajlovski K, Buttner MP, Cruz P, Labus B, St Pierre Schneider B, Detrick E
PLoS One, 17(11):e0278061, 23 Nov 2022
Cited by: 2 articles | PMID: 36417446 | PMCID: PMC9683569
This article is in the Europe PMC Open access subset. Refer to the copyright information in the article for licensing details.
Free full text in Europe PMC
SARS-CoV-2 RNA Is Readily Detectable at Least 8 Months after Shedding in an Isolation Facility.
Coil DA, Pechacek R, Kaze M, Zuniga-Montanez R, Guerrero RG, Eisen JA, Shapiro K, Bischel HN
mSphere, 7(6):e0017722, 11 Oct 2022
Cited by: 1 article | PMID: 36218344 | PMCID: PMC9769851
This article is in the Europe PMC Open access subset. Refer to the copyright information in the article for licensing details.
Free full text in Europe PMC

Go to all (9) article citations

Data

Data behind the article

This data has been text mined from the article, or deposited into data resources.

BioStudies: supplemental material and supporting data

http://www.ebi.ac.uk/biostudies/studies/S-EPMC8562474?xr=true

Funding

Funders who supported this work.

Burroughs Wellcome Fund (1)

Grant ID: Career Award for Medical Scientists
79 publications

Burroughs Wellcome Fund (BWF) (1)

Grant ID: Career Award for Medical Scientists
8 publications

County of San Diego Health and Human Services Agency (1)

Grant ID: 563236
1 publication

HHS | National Institutes of Health (2)

Grant ID: K08AI130381
1 publication
Grant ID: K01MH112436
2 publications

HHS | National Institutes of Health (NIH) (2)

Grant ID: K08AI130381
1 publication
Grant ID: K01MH112436
1 publication

NIAID NIH HHS (2)

Grant ID: K08 AI130381
28 publications
Grant ID: P30 AI036214
1387 publications

NIGMS NIH HHS (1)

Grant ID: T32 GM127235
24 publications

NIMH NIH HHS (1)

Grant ID: K01 MH112436
27 publications

Search life-sciences literature (45,105,671 articles, preprints and more)

Analysis of SARS-CoV-2 RNA Persistence across Indoor Surface Materials Reveals Best Practices for Environmental Monitoring Programs.

Author information

Affiliations

Authors

Authors

Authors

Authors

Authors

ORCIDs linked to this article

Abstract

Free full text

Analysis of SARS-CoV-2 RNA Persistence across Indoor Surface Materials Reveals Best Practices for Environmental Monitoring Programs

Rodolfo A. Salido

Victor J. Cantú

Alex E. Clark

Sandra L. Leibel

Anahid Foroughishafiei

Anushka Saha

Abbas Hakim

Alhakam Nouri

Alma L. Lastrella

Anelizze Castro-Martínez

Ashley Plascencia

Bhavika K. Kapadia

Bing Xia

Christopher A. Ruiz

Clarisse A. Marotz

Daniel Maunder

Elijah S. Lawrence

Elizabeth W. Smoot

Emily Eisner

Evelyn S. Crescini

Laura Kohn

Lizbeth Franco Vargas

Marisol Chacón

Maryann Betty

Michal Machnicki

Min Yi Wu

Nathan A. Baer

Pedro Belda-Ferre

Peter De Hoff

Phoebe Seaver

R. Tyler Ostrander

Rebecca Tsai

Shashank Sathe

Stefan Aigner

Sydney C. Morgan

Toan T. Ngo

Tom Barber

Willi Cheung

Aaron F. Carlin

Gene W. Yeo

Louise C. Laurent

Rebecca Fielding-Miller

Rob Knight

Associated Data

ABSTRACT

OBSERVATION

Findings.

TABLE S1

FIG S1

Discussion.

TEXT S1

FIG S2

TABLE S2

TABLE S3

TABLE S4

Supplementary Material

ACKNOWLEDGMENTS

REFERENCES

Full text links

Citations & impact

Impact metrics

Citations of article over time

Alternative metrics

Article citations

Data

Data behind the article

BioStudies: supplemental material and supporting data

Burroughs Wellcome Fund (1)

Burroughs Wellcome Fund (BWF) (1)

County of San Diego Health and Human Services Agency (1)

HHS | National Institutes of Health (2)

HHS | National Institutes of Health (NIH) (2)

NIAID NIH HHS (2)

NIGMS NIH HHS (1)

NIMH NIH HHS (1)