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Wastewater based epidemiology (WBE) is useful for tracking and monitoring the level of disease prevalence in a community and has been used extensively to complement clinical testing during the current COVID-19 pandemic. Despite the numerous benefits, sources of variability in sample storage, handling, and processing methods can make WBE data difficult to generalize. We performed an experiment to determine sources of variability in WBE data including the impact of storage time, handling, and processing techniques on the concentration of SARS-CoV-2 in wastewater influent from three wastewater treatment plants (WWTP) in North Carolina over 19 days. The SARS-CoV-2 concentration in influent samples held at 4°C did not degrade significantly over the 19-day experiment. Heat pasteurization did not significantly impact the concentration of SARS-CoV-2 at two of the three WWTP but did reduce viral recovery at the WWTP with the smallest population size served. On each processing date, one filter from each sample was processed immediately while a replicate filter was frozen at -80°C. Once processed, filters previously frozen were found to contain slightly higher concentrations (<0.2 log copies/L) than their immediately processed counterparts, indicating freezing filters is a viable method for delayed quantification and may even improve recovery at WWTP with low viral concentrations. Investigation of factors contributing to variability during sample processing indicated that analyst experience level contributed significantly (p<0.001) to accepted droplet generation while extraction efficiency and reverse transcription efficiency contributed significantly (p<0.05) to day-to-day SARS-CoV-2 variability. This study provides valuable practical information for minimizing decay and/or loss of SARS CoV-2 in wastewater influent while adhering to safety procedures, promoting efficient laboratory workflows, and accounting for sources of variability. [ABSTRACT FROM AUTHOR]

Estuarine water quality is declining worldwide due to increased tourism, coastal development, and a changing climate. Although well-established methods are in place to monitor water quality, municipalities struggle to use the data to prioritize infrastructure for monitoring and repair and to determine sources of contamination when they occur. The objective of this study was to assess water quality and prioritize sources of contamination within Town Creek Estuary (TCE), Beaufort, North Carolina, by combining culture, molecular, and geographic information systems (GIS) data into a novel contamination source ranking system. Water samples were collected from TCE at ten locations on eight sampling dates in Fall 2021 (n = 80). Microbiological water quality was assessed using US Environmental Protection Agency (U.S. EPA) approved culture-based methods for fecal indicator bacteria (FIB), including analysis of total coliforms (TC), Escherichia coli (EC), and Enterococcus spp. (ENT). The quantitative microbial source tracking (qMST) human-associated fecal marker, HF183, was quantified using droplet digital PCR (ddPCR). This information was combined with environmental data and GIS information detailing proximal sewer, septic, and stormwater infrastructure to determine potential sources of fecal contamination in the estuary. Results indicated FIB concentrations were significantly and positively correlated with precipitation and increased throughout the estuary following rainfall events (p < 0.01). Sampling sites with FIB concentrations above the U.S. EPA threshold also had the highest percentages of aged, less durable piping materials. Using a novel ranking system combining concentrations of FIB, HF183, and sewer infrastructure data at each site, we found that the two sites nearest the most aged sewage infrastructure and stormwater outflows were found to have the highest levels of measurable fecal contamination. This case study supports the inclusion of both traditional water quality measurements and local infrastructure data to support the current need for municipalities to identify, prioritize, and remediate failing infrastructure. [ABSTRACT FROM AUTHOR]

Wastewater monitoring has provided health officials with early warnings for new COVID-19 outbreaks, but to date, no approach has been validated to distinguish signal (sustained surges) from noise (background variability) in wastewater data to alert officials to the need for heightened public health response. We analyzed 62 wk of data from 19 sites participating in the North Carolina Wastewater Monitoring Network to characterize wastewater metrics around the Delta and Omicron surges. We found that wastewater data identified outbreaks 4 to 5 d before case data (reported on the earlier of the symptom start date or test collection date), on average. At most sites, correlations between wastewater and case data were similar regardless of how wastewater concentrations were normalized and whether calculated with county-level or sewershed-level cases, suggesting that officials may not need to geospatially align case data with sewershed boundaries to gain insights into disease transmission. Although wastewater trend lines captured clear differences in the Delta versus Omicron surge trajectories, no single wastewater metric (detectability, percent change, or flow-population normalized viral concentrations) reliably signaled when these surges started. After iteratively examining different combinations of these three metrics, we developed the Covid-SURGE (Signaling Unprecedented Rises in Groupwide Exposure) algorithm, which identifies unprecedented signals in the wastewater data. With a true positive rate of 82%, a false positive rate of 7%, and strong performance during both surges and in small and large sites, our algorithm provides public health officials with an automated way to flag community-level COVID-19 surges in real time. [ABSTRACT FROM AUTHOR]

[Display omitted] • Wastewater surveillance methods captured the emergence of SARS-CoV-2 presence in a community. • In a direct comparison of workflows, RT-ddPCR demonstrated superior analytical sensitivity. • RT-ddPCR was resistant to inhibition in a wastewater matrix and was reproducible across laboratories. Throughout the COVID-19 global pandemic there has been significant interest and investment in using Wastewater-Based Epidemiology (WBE) for surveillance of viral pathogen presence and infections at the community level. There has been a push for widescale implementation of standardized protocols to quantify viral loads in a range of wastewater systems. To address concerns regarding sensitivity, limits of quantification, and large-scale reproducibility, a comparison of two similar workflows using RT-qPCR and RT-ddPCR was conducted. Sixty raw wastewater influent samples were acquired from nine distinct wastewater treatment plants (WWTP's) served by the Hampton Roads Sanitation District (HRSD, Virginia Beach, Virginia) over a 6-month period beginning March 9th, 2020. Common reagents, controls, master mixes and nucleic acid extracts were shared between two individual processing groups based out of HRSD and the UNC Chapel Hill Institute of Marine Sciences (IMS, Morehead City, North Carolina). Samples were analyzed in parallel using One-Step RT-qPCR and One-Step RT-ddPCR with Nucleocapsid Protein 2 (N2) specific primers and probe. Influent SARS-CoV-2 N2 concentrations steadily increased over time spanning a range from non-detectable to 2.13E + 05 copies/L. Systematic dilution of the extracts indicated that inhibitory components in the wastewater matrices did not significantly impede the detection of a positive N2 signal for either workflow. The RT-ddPCR workflow had a greater analytical sensitivity with a lower Limit of Detection (LOD) at 0.066 copies/μl of template compared to RT-qPCR with a calculated LOD of 12.0 copies/μL of template. Interlaboratory comparisons using non-parametric correlation analysis demonstrated that there was a strong, significant, positive correlation between split extracts when employing RT-ddPCR for analysis with a ρ value of 0.86. [ABSTRACT FROM AUTHOR]