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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Direct wastewater extraction as a simple and effective method for SARS-CoV-2 surveillance and COVID-19 community-level monitoring
Wastewater surveillance has proven to be an effective tool to monitor the transmission and emergence of infectious agents at a community scale. Workflows for wastewater surveillance generally rely on concentration steps to increase the probability of detection of low-abundance targets, but preconcentration can substantially increase the time and cost of analyses while also introducing additional loss of target during processing. To address some of these issues, we conducted a longitudinal study implementing a simplified workflow for SARS-CoV-2 detection from wastewater, using a direct column-based extraction approach. Composite influent wastewater samples were collected weekly for 1 year between June 2020 and June 2021 in Athens-Clarke County, Georgia, USA. Bypassing any concentration step, low volumes (280 µl) of influent wastewater were extracted using a commercial kit, and immediately analyzed by RT-qPCR for the SARS-CoV-2 N1 and N2 gene targets. SARS-CoV-2 viral RNA was detected in 76% (193/254) of influent samples, and the recovery of the surrogate bovine coronavirus was 42% (IQR: 28%, 59%). N1 and N2 assay positivity, viral concentration, and flow-adjusted daily viral load correlated significantly with per-capita case reports of COVID-19 at the county-level (ρ = 0.69–0.82). To compensate for the method’s high limit of detection (approximately 106–107 copies l−1 in wastewater), we extracted multiple small-volume replicates of each wastewater sample. With this approach, we detected as few as five cases of COVID-19 per 100 000 individuals. These results indicate that a direct-extraction-based workflow for SARS-CoV-2 wastewater surveillance can provide informative and actionable results.
more » « less- NSF-PAR ID:
- 10397657
- Publisher / Repository:
- Oxford University Press
- Date Published:
- Journal Name:
- FEMS Microbes
- Volume:
- 4
- ISSN:
- 2633-6685
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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