Early detection of the COVID-19 virus, SARS-CoV-2, is key to mitigating the spread of new outbreaks. Data from individual testing is increasingly difficult to obtain as people conduct non-reported home tests, defer tests due to logistics or attitudes, or ignore testing altogether. Wastewater based epidemiology is an alternative method for surveilling a community while maintaining individual anonymity; however, a problem is that SARS-CoV-2 markers in wastewater vary throughout the day. Collecting grab samples at a single time may miss marker presence, while autosampling throughout a day is technically challenging and expensive. This study investigates a passive sampling method that would be expected to accumulate greater amounts of viral material from sewers over a period of time. Tampons were tested as passive swab sampling devices from which viral markers could be eluted with a Tween-20 surfactant wash. Six sewersheds in Detroit were sampled 16–22 times by paired swab (4 h immersion before retrieval) and grab methods over a five-month period and enumerated for N1 and N2 SARS-CoV-2 markers using ddPCR. Swabs detected SARS-CoV-2 markers significantly more frequently (P < 0.001) than grab samples, averaging two to three-fold more copies of SARS-CoV-2 markers than their paired grab samples (p < 0.0001) in the assayed volume (10 mL) of wastewater or swab eluate. No significant difference was observed in the recovery of a spiked-in control (Phi6), indicating that the improved sensitivity is not due to improvements in nucleic acid recovery or reduction of PCR inhibition. The outcomes of swab-based sampling varied significantly between sites, with swab samples providing the greatest improvements in counts for smaller sewersheds that otherwise tend to have greater variation in grab sample counts. Swab-sampling with tampons provides significant advantages in detection of SARS-CoV-2 wastewater markers and are expected to provide earlier detection of new outbreaks than grab samples, with consequent public health benefits.
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Subsewershed SARS-CoV-2 Wastewater Surveillance and COVID-19 Epidemiology Using Building-Specific Occupancy and Case Data
To evaluate the use of wastewater-based surveillance and epidemiology to monitor and predict SARS-CoV-2 virus trends, over the 2020–2021 academic year we collected wastewater samples twice weekly from 17 manholes across Virginia Tech’s main campus. We used data from external door swipe card readers and student isolation/quarantine status to estimate building-specific occupancy and COVID-19 case counts at a daily resolution. After analyzing 673 wastewater samples using reverse transcription quantitative polymerase chain reaction (RT-qPCR), we reanalyzed 329 samples from isolation and nonisolation dormitories and the campus sewage outflow using reverse transcription digital droplet polymerase chain reaction (RT-ddPCR). Population-adjusted viral copy means from isolation dormitory wastewater were 48% and 66% higher than unadjusted viral copy means for N and E genes (1846/100 mL to 2733/100 mL/100 people and 2312/100 mL to 3828/100 mL/100 people, respectively; n = 46). Prespecified analyses with random-effects Poisson regression and dormitory/cluster-robust standard errors showed that the detection of N and E genes were associated with increases of 85% and 99% in the likelihood of COVID-19 cases 8 days later (incident–rate ratio (IRR) = 1.845, p = 0.013 and IRR = 1.994, p = 0.007, respectively; n = 215), and one-log increases in swipe card normalized viral copies (copies/100 mL/100 people) for N and E were associated with increases of 21% and 27% in the likelihood of observing COVID-19 cases 8 days following sample collection (IRR = 1.206, p < 0.001, n = 211 for N; IRR = 1.265, p < 0.001, n = 211 for E). One-log increases in swipe normalized copies were also associated with 40% and 43% increases in the likelihood of observing COVID-19 cases 5 days after sample collection (IRR = 1.403, p = 0.002, n = 212 for N; IRR = 1.426, p < 0.001, n = 212 for E). Our findings highlight the use of building-specific occupancy data and add to the evidence for the potential of wastewater-based epidemiology to predict COVID-19 trends at subsewershed scales.
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- Award ID(s):
- 2004751
- NSF-PAR ID:
- 10337229
- Date Published:
- Journal Name:
- ACS ES&T Water
- ISSN:
- 2690-0637
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1021/acsestwater.2c00059
