key: cord-0943632-p3ziddrc
authors: Hinz, A.; Xing, L. Y.; Doukhanine, E.; Hug, L. A.; Kassen, R.; Ormeci, B.; Kibbee, R. J.; Wong, A.; MacFadden, D.; Nott, C.
title: SARS-CoV-2 Detection from the Built Environment and Wastewater and Its Use for Hospital Surveillance
date: 2021-04-13
journal: nan
DOI: 10.1101/2021.04.09.21255159
sha: d0f412a5db039281ad52255b71585ebfdf5122d7
doc_id: 943632
cord_uid: p3ziddrc

Background. SARS-CoV-2 causes significant morbidity and mortality in health care settings. Our understanding of the distribution of this virus in the built healthcare environment and wastewater, and relationship to disease burden, is limited. Methods. We performed a prospective multi-center study of environmental sampling of SARS-CoV-2 from hospital surfaces and wastewater and evaluated their relationships with regional and hospital COVID-19 burden. We developed and validated a qPCR-based approach to surface sampling, and swab samples were collected weekly from different locations and surfaces across two tertiary care hospital campuses for a 10-week period during the pandemic, along with wastewater samples. Results. Over a 10-week period, 963 swab samples were collected and analyzed. We found 61 (6%) of swabs were positive for SARS-CoV-2, with the majority of these (n=51) originating from floor samples. Wards that actively managed patients with COVID-19 had the highest frequency of positive samples (p<0.01). Detection frequency in built environment swabs was significantly associated with active cases in the hospital throughout the study (p<0.025). Wastewater viral signal changes appeared to predate change in case burden. Conclusions. Environment sampling for SARS-CoV-2, in particular from floors, may offer a unique and resolved approach to surveillance of COVID-19.

All rights reserved. No reuse allowed without permission.

(which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.

The copyright holder for this preprint this version posted April 13, 2021. The qPCR results were analyzed as described by Klymus et al. 12 using the Limit of Detection 136 (LOD) Calculator (R script available at: https://github.com/cmerkes/qPCR_LOD_Calc). The 137 standard curves were modeled as linear regressions based on the middle two quartiles of Cq 138 values for each standard concentration exhibiting >50% detection. The limit of detection was 139 defined as the lowest input amount that was detected in >95% of replicates, and the limit of 140 quantification was defined as the lowest input amount yielding reproducible Cq values with a 141 coefficient of variation less than 0.35. For the RNA standards, the LOD with 95% confidence 142 intervals and limit of quantitation (LOQ) were determined by a curve-fitting approach as 143 All rights reserved. No reuse allowed without permission.

(which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. was also sampled at Campus A. Built environment samples were collected by wetting a P-208 159 kit flocked swab with the stabilization media, and sweeping the swab tip across an 160 approximately 2" x 2" area (dependent upon the item/surface size) for approximately 30 161 seconds. The swab was then transported in the P-208 stabilization media, and stored at 162 ambient temperature to be processed/analyzed within 1 month of collection. Three study 163 authors (LX, DM, CN) collected the samples during the study period, and swab technique was 164 harmonized between them at onset of sample collection. A run-in week was performed prior to 165

week 1, in order to test sample collection, logistics, and sample processing, but is not included 166 All rights reserved. No reuse allowed without permission.

(which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. 8   in this analysis as a number of specimen sites were initially not included for this week, making  167 inter-week comparisons challenging. 168 169 Specific (non-critical care) wards in the hospital were designated for the ongoing inpatient care 170 of COVID patients, and these were designated as 'COVID Wards'. However, we classified both 171 the COVID Wards as well as the ICU and ED as 'COVID Risk Units', because they were 172 anticipated to be managing a large number of SARS-CoV-2 infected patients. 173

174

The visitor policy at the start of the study period limited visitation to 1 visitor/patient/day; 176 however, at the start of study week 4, visitors were further restricted to essential caregivers 177 only. Environmental cleaning was in keeping with provincial best practice and occurred daily in 178 both public and clinical areas, with all contact points disinfected with Vert-2-Go© everyday 179 disinfectant (quaternary-ammonium based product) and floors mopped with Vert-2-Go© Oxy 180 floor cleaner. The only deviation from this was once weekly cleaning of the Campus A parking 181 garage until sometime between study weeks 5-7, then once daily thereafter. Infection 182 Prevention and Control (IPAC) policies were also consistent with provincial best practice 6,13 . 183

Throughout the study period, all staff, visitors, and patients were screened for COVID-19 184 symptoms and risk factors (e.g. exposures, international travel) on entry to the hospital, in 185 accordance with Ontario Ministry of Health guidance 14 . Universal masking for all staff and 186 visitors was introduced prior to the study period. Universal eye protection (visor or goggles) for 187 staff working in clinical areas was introduced at the start of study week 1. (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.

The copyright holder for this preprint this version posted April 13, 2021. ; https://doi.org/10.1101/2021.04.09.21255159 doi: medRxiv preprint Raw sewage grab samples from Campus A were taken with a 5L bucket on a rope between 192 11:10am and 12:30pm each sampling day, which occurred once every 1 to 2 weeks during (and 193 immediately prior to) the study period. The samples were swirled in the bucket and quickly 194 poured into pre-labelled sterile 1L Nalgene bottles which were immediately placed into a cooler 195 with ice packs for transport back to the lab and processed within 1 hr. Temperature of the The RT RT-PCR thermal cycling protocol used to amplify the target region within the SARS-205

CoV-2 nucleocapsid gene, N2, was carried out at 50 °C for 30 minutes, followed by 95 °C for 15 206 minutes and 50 cycles of 95 °C for 15 and 60 °C for 30 seconds. Reaction mixture for RT RT-207 PCR, 5 μl RNA template, 4 μl of QIAGEN OneStep RT-PCR 5x Buffer, 2 μl dNTP mix (10 mM 208 each), 2 μl Enzyme Mix, Primers and Probes were used at 500nM/reaction, and 2 µl of 4 mg/ml 209 BSA. Each reaction volume was adjusted to a final volume of 20 µl with RNase-free water. Each 210 sample was run in duplicate. An MS-2 bacteriophage matrix spike whole process control was 211 used to assess the efficiency of the concentration process and RNA extraction as well as 212 confirm RT RT-PCR protocols. RT RT-PCR inhibition control using MS2 bacteriophage spiked 213 into raw sewage RNA extracts and molecular grade water was used to assess the performance 214 of the RNA-extraction, RT-PCR and to detect the presence of inhibitors. Quantification of the N2 215 gene target in the raw sewage sample concentrates was done using standard curves created 216 with a 2019-nCoV_N_Positive Control plasmid (IDT, Inc.) Non-template controls (NTC) were run 217 All rights reserved. No reuse allowed without permission.

(which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. To evaluate the characteristics associated with positive swab results, we performed a linear 240 regression model using specimen characteristics as predictors of built environment sample 241 positivity (total or floor specific specimens). Specimen characteristics were coded as categorical 242 variables, including unit COVID Risk type, object/site, material, study week. The outcome 243 All rights reserved. No reuse allowed without permission.

(which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. We detected and quantified SARS-CoV-2 RNA by qPCR analysis using the N1 primer and 269 probe set targeting the nucleocapsid gene 11 . A standard curve relating the quantification cycle 270 (Cq) values to viral genome copy number was produced by analyzing a 10-fold dilution series of 271 a SARS-CoV-2 RNA standard (Fig. 1A) . We used a model-fitting approach to determine the 272 LOD, defined as a detection rate greater than 95%, and LOQ defined as a Cq coefficient of 273 variation less than 0.35 12 . Reactions containing at least 3 genome copies were reliably detected 274 under our qPCR conditions, whereas precise quantification required at least 150 copies (Cq < 275 30) (Fig. 1A) . 276 277 Analysis of the built environment swab samples involved a nucleic acid extraction step prior to 278 qPCR analysis. We evaluated the extraction method by qPCR analysis of RNA extracted from 279 samples of a heat-inactivated SARS-CoV-2 virus standard (Fig. 1B) . The LOD for the virus 280 standard (28 copies per reaction) was ~10-fold higher than that of the RNA standard, which can 281 be due to RNA loss during the extraction procedure, presence of PCR inhibitors in the eluant, or 282 inaccuracies in the reported titers of the standards. Nevertheless, the procedure was sensitive 283 and reproducible, and the results yielded a linear standard curve for converting Cq values to viral 284 copies while accounting for inaccuracies introduced by sample processing. 285 286 All rights reserved. No reuse allowed without permission.

(which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.

The copyright holder for this preprint this version posted April 13, 2021. ; https://doi.org/10.1101/2021.04.09.21255159 doi: medRxiv preprint

The built environment sampling approach relied on recovery of virus particles and nucleic acid 287 from surfaces. In preliminary tests, we determined how efficiently our swabbing technique 288 recovered virus from surface types representative of the built environment: acrylic, stainless 289 steel, and vinyl. The surfaces were spiked with equal amounts of heat-inactivated virus, allowed 290 to dry, and swabbed by the procedure used for built environment sampling. Following incubation 291 of the swabs in stabilization solution and qPCR analysis of extracted RNA, we estimated the 292 amount of recovered virus using the virus standard curve and calculated the percent of virus 293 recovered from each surface (Fig. 2) . The mean recovery efficiencies ranged from 28 to 42% 294 across the three surfaces, values similar to those reported in a previous study of SARS-CoV-2 295 surface swabbing 19 . 296 297

Over a 10-week period from September 28th to December 6th 2020 we systematically 299 performed 963 built environment swabs across 2 tertiary care hospital sites. SARS-CoV-2 was 300 detected from 6% of swabs (61/963), and was most commonly found from floor samples, with a 301 detection prevalence of 27% (51/188). SARS-CoV-2 was less frequently identified from elevator 302 buttons, benches, items (e.g., hand sanitizer pumps), and computer keyboards. 303 304 COVID Risk units, namely COVID treatment wards, intensive care units, and emergency rooms, 305

were the most common locations to find SARS-CoV-2 in the hospital environment, with 306 detection prevalence of 18%, 8%, and 7% respectively (Table 2, Fig. 3 ). Non-COVID and 307 public/HCW spaces tended to have a low frequency of SARS-CoV-2 detection from the 308 environment. During the course of this study, there were 2 outbreaks involving study units, at 309

Campus B in the ED (1 patient and 2 HCW) and at Campus A on Ward 3 (5 patients and 1 310 All rights reserved. No reuse allowed without permission.

(which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.

The copyright holder for this preprint this version posted April 13, 2021. ; https://doi.org/10.1101/2021.04.09.21255159 doi: medRxiv preprint 14 HCW). A third outbreak occurred on a non-COVID inpatient unit at Campus A but this unit was 311 not sampled in the study. 312 313 Across the 10-week study period, SARS-CoV-2 detection prevalence paralleled total active 314 cases across the two hospital campuses. Viral prevalence gradually declined over time, but with 315 spikes in hospitalized cases being clearly reflected in built environment burden ( Figure 4A ), as 316 well as an increase reflecting the start of hospitalizations in the second wave of the pandemic 317 within the city. Built environment burden also paralleled citywide cases, but with less 318 concordance ( Figure 4B ). Campus B tended to have SARS-CoV-2 positivity largely restricted to 319 COVID Risk units, but Campus A seemed to have more activity in non-COVID risk units/public 320 spaces. A multi-variable logistic regression model identified that increasing study week was 321 associated significantly with reduced detection of SARS-CoV-2 (p<0.05), and that COVID Risk 322 units and Campus A were significantly associated with increased SARS-CoV-2 detection (Table  323 3, Table 4 ). (Table 4 ). We found that floor swabs best 336 All rights reserved. No reuse allowed without permission.

(which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.

The copyright holder for this preprint this version posted April 13, 2021. ; https://doi.org/10.1101/2021.04.09.21255159 doi: medRxiv preprint described the hospital case burden, having a measure of association with the greatest 337 precision, and the model with the lowest AIC (Table 4) in the hospital or other settings 5 . While our PCR-based approach for SARS-CoV-2 RNA 361 detection cannot identify whether viral particles present in our samples were viable or not, our 362 All rights reserved. No reuse allowed without permission.

(which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.

The copyright holder for this preprint this version posted April 13, 2021. ; https://doi.org/10.1101/2021.04.09.21255159 doi: medRxiv preprint results do indicate that identifying any RNA from high touch surfaces is relatively rare, further 363 supporting these as unlikely pathways for transmission in hospitals. Floors, however, were a 364 common location for SARS-CoV-2 RNA in our study. It is likely they represent a sink for viral 365 particles which originate from the respiratory droplets/aerosol from patients, health workers, or 366 visitors, and it is unlikely that floors represent a reservoir for transmission 7 . studies have shown the potential benefit of wastewater monitoring in both regional as well as 386 facility-based surveillance 20,21 , and our study links this surveillance as complementary to built 387 environment screening. Combined approaches may ultimately be applicable to other respiratory 388 All rights reserved. No reuse allowed without permission.

(which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. Our study has some limitations which have not already been noted. Firstly, our study was 392 performed during a period when there was relatively low activity, with regional case counts 393 ranging from approximately 2 to 7 cases per 100,000 population per week, and it is possible that 394 in higher burden settings there could be saturation of ward-based built environment detection 395 which could limit the correlation with burden or outbreaks. Secondly, outbreaks were relatively 396 rare in our hospital, owing in part to the low regional burden, and this limits our ability to 397 evaluate the ability of built environment screening to consistently detect outbreaks. (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.

The copyright holder for this preprint this version posted April 13, 2021. ; https://doi.org/10.1101/2021.04.09.21255159 doi: medRxiv preprint Table 2 . Built environment swab SARS-CoV-2 detection by location and characteristics. 482 483 484 All rights reserved. No reuse allowed without permission.

(which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.

The copyright holder for this preprint this version posted April 13, 2021. ; https://doi.org/10.1101/2021.04.09.21255159 doi: medRxiv preprint Table 3 . Swab characteristics and measure of association with SARS-CoV-2 detection. 485 486 487 All rights reserved. No reuse allowed without permission.

(which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission.

(which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.

The copyright holder for this preprint this version posted April 13, 2021. ; https://doi.org/10.1101/2021.04.09.21255159 doi: medRxiv preprint 

Drug treatments for covid-19: living systematic review and 429 network meta-analysis

Efficacy and Safety of the mRNA-1273 SARS-CoV-2 Vaccine

The Visual and Data Journalism Team. Covid map: Coronavirus cases, deaths, 433 vaccinations by country

Current knowledge of COVID-19 and infection prevention and control 435 strategies in healthcare settings: A global analysis

CoV-2 transmission by fomites in real-life conditions

Aerosol and Surface Stability of SARS-CoV-2 as Compared with 441 SARS-CoV-1

Aerosol and Surface Distribution of Severe Acute Respiratory Syndrome 443

Coronavirus 2 in Hospital Wards

Optimizing COVID-19 surveillance in long-term care facilities: a 446 modelling study

Detection of SARS-CoV-2 in raw and treated wastewater in Germany -448

Suitability for COVID-19 surveillance and potential transmission risks

Detection of SARS-CoV-2 RNA in hospital wastewater from a low 451 COVID-19 disease prevalence area

Reporting the limits of detection and quantification for environmental 455 DNA assays

Coronavirus-2 RNA in sewage and correlation with reported COVID-19 prevalence in the 462 early stage of the epidemic in the Netherlands

Ministry of Health

Logistic or linear? Estimating causal effects of experimental treatments on 469 binary outcomes using regression analysis

End-to-End Protocol for the Detection of SARS-CoV-2 from Built 472

SARS-CoV-2 RNA in wastewater anticipated COVID-19 occurrence in a low prevalence 474 area

Detection of SARs-CoV-2 in wastewater, using the existing environmental 476 surveillance network: An epidemiological gateway to an early warning for COVID-19 in 477 communities

All rights reserved. No reuse allowed without permission.(which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.The copyright holder for this preprint this version posted April 13, 2021. (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.The copyright holder for this preprint this version posted April 13, 2021. surfaces were spotted with 10 5 copies of the virus standard, allowed to dry, and swabbed for 30 508 seconds. Swabs were stored in nucleic acid stabilization solution overnight, RNA was extracted, 509 and recovered genome copies were quantified by qPCR analysis. Virus recovery was 510 determined by dividing the copies estimated for each sample by the copies estimated for a "no 511 swabbing" control. The mean and standard error for three replicates is shown. Letters indicate 512 significant differences between groups, determined by one-way ANOVA followed by Tukey's 513 post hoc test. 514 515 All rights reserved. No reuse allowed without permission.(which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.The copyright holder for this preprint this version posted April 13, 2021. location. *Blank (white) cells represent weeks where no swab was collected for a given location. 519 520 All rights reserved. No reuse allowed without permission.(which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.The copyright holder for this preprint this version posted April 13, 2021. (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.The copyright holder for this preprint this version posted April 13, 2021. (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.The copyright holder for this preprint this version posted April 13, 2021. (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.The copyright holder for this preprint this version posted April 13, 2021. ; https://doi.org/10.1101/2021.04.09.21255159 doi: medRxiv preprint