Many in-sewer dynamics that can impact the fidelity of wastewater-based surveillance results remain understudied. Some conventional approaches for normalizing pathogen signals in sewersheds may not be appropriate when there is substantial inflow and infiltration (I&I). Our objective for this study was to evaluate the effect of multiple normalization approaches on wastewater pathogen signals at the WWTP influent and across a small rural sewershed (<3000 people) with different levels of I&I. We collected wastewater samples monthly, from 2022 to 2023, from the wastewater treatment plant (WWTP) influent and 11 additional sewer system nodes with well-characterized I&I impacts. We quantified concentrations of SARS-CoV-2, norovirus GII, and rotavirus at the WWTP influent and subsewershed sites, and compared normalization approaches using flow, population, physicochemical parameters (COD, TSS, NH₃(aq), PO₄₃^––P), and human fecal markers (crAssphage, HF183, mtDNA). Overall, our findings suggest that in systems with substantial I&I, some commonly used normalization approaches, particularly those using physicochemical parameters, may inadvertently introduce additional variability in viral signals measured at WWTP influent and result in wastewater measures that are more closely associated with precipitation trends rather than with pathogen signals. Normalization with human fecal markers appears to be a relatively robust option for sewersheds impacted by substantial I&I.

Abstract/Summary: As wastewater-based surveillance (WBS) is increasingly used to track community-level disease trends, it is important to understand how pathogen signals can be altered by phenomena that occur within sewersheds such as inflow and infiltration (I&I). Our objectives were to characterize I&I across a rural sewershed and assess potential impacts on viral (rotavirus, norovirus GII, and SARS-CoV-2), fecal indicator (HF183, the hCYTB484 gene specific to the human mitochondrial genome, and crAssphage), and antimicrobial resistance (intI1, blaCTX−M-1) targets. In a small town in Virginia (USA), we collected 107 wastewater samples at monthly intervals over a 12-month period (2022–2023) at the wastewater treatment plant (WWTP) influent and 11 up-sewer sites. Viral, fecal indicator, and antimicrobial resistance targets were enumerated using ddPCR. We observed the highest concentrations of human fecal markers and a measure anthropogenic pollution and antibiotic resistance (intI1) in up-sewer sites with limited I&I. However, median viral gene copy concentrations were highest at the WWTP, compared to 100 % (n = 11), 90 % (n = 10), and 55 % (n = 6) of up-sewer sites for rotavirus, norovirus GII, and SARS-CoV-2, respectively. After adjusting for covariates (Ba, COD, dissolved oxygen, groundwater depth, precipitation, sampling date) using generalized linear models, moderate to high I&I was associated with statistically significant reductions in log10-transformed rotavirus and norovirus GII concentrations across the sewershed (coefficients = -0.7 and -0.9, p < 0.001, n = 95), though not for SARS-CoV-2 (coefficient = -0.2, p = 0.181, n = 95). Overall, we found that while I&I can diminish biomarker signals throughout a sewershed, including at the WWTP influent, I&I impacts vary depending on the target, and pathogen biomarker signals were, on average, higher and less variable over time at the WWTP compared to most up-sewer sites. As far as we are aware, this is the first study to assess in situ I&I impacts on multiple WBS targets. Taken together, our findings highlight challenges and tradeoffs associated with different sampling strategies for different WBS targets in heavily I&I impacted systems.

Abstract/Summary: 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.