Tools for interpretation of wastewater SARS-CoV-2 temporal and spatial trends demonstrated with data collected in the San Francisco Bay Area

Comparative assessment of sewer sampling methods for infectious disease surveillance: Insights from transport modeling and simulations of SARS-CoV-2 emissions

Emerging infectious diseases like COVID-19 present significant public health challenges, necessitating effective surveillance methods. Wastewater-based epidemiology (WBE), detecting viral pathogens in wastewater, has emerged as a proactive tool for monitoring infections. This study evaluated various wastewter sampling methods through SARS-CoV-2 transport simulations in an urban sewer network in Sejong City, South Korea, to identify cost-effective strategies for accurate infection monitoring. Using the U.S. EPA's Storm Water Management Model (SWMM) and Markov chain Monte Carlo (MCMC) sampling, we simulated wastewater flow and viral concentrations based on reported COVID-19 case data for the year 2021. In this study, we used reported COVID-19 cases as a hypothetical estimate of the number of infected individuals in the simulation. The SWMM effectively replicated daily and monthly patterns in sewer flow rates. Combining the SWMM with MCMC sampling from the probability distributions of spatio-temporal virus emission patterns, we generated an ensemble time series dataset of hourly virus concentrations based on 200 simulations, forming the basis for evaluating sampling alternatives. Results showed a strong correlation (R2 = 0.81) between daily average virus concentrations and daily infection rates on the fifth day following new infections, consistent with simulated viral emission patterns. Flow-weighted and equally timed sampling methods provided highly reliable infection pattern estimates, suggesting that equally timed sampling is a cost-effective alternative. In contrast, grab sampling performed poorly due to difficulties in capturing peak viral emission periods. We found that a minimum sampling duration of four to six hours was crucial for accurate detection, with performance increasing if the sampling was applied in the morning (R2 ≈ 0.7). Longer durations steadily, but only slightly, improved results. While this simulation-based approach focused on predicting daily virus concentration patterns in wastewater rather than precisely estimating its absolute levels, it provides valuable insights for optimizing WBE in public health surveillance and underscores the need for further validation with real-world data.

Subsewershed analyses of the impacts of inflow and infiltration on viral pathogens and antibiotic resistance markers across a rural sewer system

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. Physicochemical proxies for organics and nutrient levels in sewage (chemical oxygen demand (COD), total suspended solids (TSS), and NH3(aq)) and genetic markers of anthropogenic impact were used to characterize I&I across the sewershed. Overall, precipitation was negatively associated (Spearman test; ρ < 0; p < 0.01) with physicochemical markers (TSS, COD, K, PO43--P, NH3(aq)) in the WWTP influent. 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.

Wastewater Speaks: Evaluating SARS-CoV-2 Surveillance, Sampling Methods, and Seasonal Infection Trends on a University Campus

Wastewater surveillance has emerged as a cost-effective and equitable approach for tracking the spread of SARS-CoV-2. In this study, we monitored the prevalence of SARS-CoV-2 on a university campus over three years (2021-2023) using wastewater-based epidemiology (WBE). Wastewater samples were collected from 11 manholes on campus, each draining wastewater from a corresponding dormitory building, and viral RNA concentrations were measured using reverse transcription-quantitative PCR (RT-qPCR). Weekly clinical case data were also obtained from the university health center. A strong positive and significant correlation was observed between Grab and Composite sampling methods, supporting their robustness as equally effective approaches for sample collection. Specifically, a strong correlation was observed between Aggie Village 4 Grab and Aggie Village 4 Composite samples (R2 = 0.84, p = 0.00) and between Barbee Grab and Barbee Composite samples (R2 = 0.80, p = 0.00). Additionally, higher viral RNA copies of SARS-CoV-2 (N1 gene) were detected during the Spring semester compared to the Fall and Summer semesters. Notably, elevations in raw N1 concentrations were observed shortly after the return of college students to campus, suggesting that these increases were predominantly associated with students returning at the beginning of the Fall and Spring semesters (January and August). To account for variations in fecal loading, SARS-CoV-2 RNA concentrations were normalized using Pepper Mild Mottle Virus (PMMoV), a widely used viral fecal biomarker. However, normalization using PMMoV did not improve correlations between SARS-CoV-2 RNA levels and clinical case data. Despite these findings, our study did not establish WBE as a consistently reliable complement to clinical testing in a university campus setting, contrary to many retrospective studies. One key limitation was that numerous off-campus students did not contribute to the campus wastewater system corresponding to the monitored dormitories. However, some off-campus students were still subjected to clinical testing at the university health center under mandated protocols. Moreover, the university health center discontinued reporting cases per dormitory after 2021, making direct comparisons more challenging. Nevertheless, this study highlights the continued value of WBE as a surveillance tool for monitoring infectious diseases and provides critical insights into its application in campus environments.

Tracking COVID-19 trends in communities with low population by wastewater-based surveillance

Wastewater-based surveillance (WBS) of SARS-CoV-2 is increasingly recognized as a valuable complement to clinical reporting for estimating COVID-19 infection rates. This acceptance stems from the strong correlation found between wastewater and clinical case data during the early stages of the pandemic. However, the cessation of COVID-19 restrictions, changes in clinical testing requirements by late 2021, and the widespread use of take-home antigen tests have diminished the reliability and volume of clinically reported case counts. This study explores the dynamics between clinical cases and wastewater-based results in a period of transition, focusing on student residential areas within a university campus. We analyzed wastewater from 13 sub-sewersheds, serving populations of 300 to 4000 individuals, three times weekly from December 2021 to June 2022. The analysis revealed two COVID-19 spikes in wastewater data during this time, whereas clinical reports indicated at most a single surge in infections across most communities. Further, in the first infection surge, clinical data plateaued sooner than wastewater trends and, in the second surge, either lagged or were completely absent. Correlations between wastewater SARS-CoV-2 concentrations and the 3-day rolling average of clinical cases were weak in smaller communities (≤1000 people) but improved with larger community sizes (>1000 people). Normalization with PMMoV did not enhance these correlations. Given the challenges in executing widespread and accurate mass clinical testing, our findings advocate for the efficacy of WBS data in reliably forecasting infection surges, even in less populous settings, thereby facilitating swift, informed public health interventions.

Hydrological and physicochemical parameters associated with SARS-CoV-2 and pepper mild mottle virus wastewater concentrations for a large-combined sewer system

During COVID-19, surveillance of SARS-CoV-2 in wastewater has been a promising tool for tracking viral infection at the community level. However, in addition to the shedding rates within the community, SARS-CoV-2 concentrations in raw wastewater are influenced by several environmental factors. This study investigated the effects of wastewater characteristics on the viral quantification of SARS-CoV-2 and pepper mild mottle virus (PMMoV) for a large wastewater system with combined sewers. Principal component analysis illustrated that water temperature negatively correlates with SARS-CoV-2 and PMMoV in wastewater, but flow rate and EC are highly correlated with SARS-CoV-2 in spring and winter. The normalization using EC enhanced the correlation with clinical data compared to normalization using pH, flow rate, and raw SARS-CoV-2. The normalization using PMMoV reduced the correlation with clinical data. Multiple linear and random forest (RF) applied to predict the concentrations of SARS-CoV-2 in wastewater, given the confirmed cases and physicochemical parameters. RF regression was the best model to predict SARS-CoV-2 in wastewater (R2=0.8), with the most important variables being the confirmed cases followed by water temperature. RF model is a potent predictor of the presence of SARS-CoV-2 in wastewater. This enhances the degree of reliability between community outbreaks and SARS-CoV-2 monitoring.

Evaluation of plasmid pBI143 for its optimal concentration methods, seasonal impact, and potential as a normalization parameter in wastewater-based epidemiology

Plasmid pBI143, abundant in the human gut, is a promising human-specific fecal marker. However, studies on its optimal concentration methods, seasonal variations, and potential as a normalization parameter for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the causative agent of coronavirus disease 2019 (COVID-19), remain limited. Among the three concentration methods compared, polyethylene glycol (PEG) precipitation and centrifugation demonstrated comparable efficiencies (9.3 ± 0.6 and 9.2 ± 0.6 log10 copies/L, respectively; n = 8 each), outperforming membrane filtration (8.0 ± 0.6 log10 copies/L; n = 8). PEG precipitation was further applied to quantify pBI143, together with other human-specific fecal markers (crAssphage and pepper mild mottle virus (PMMoV)), in 52 wastewater samples collected weekly over a one year from a wastewater treatment plant in Yamanashi Prefecture, Japan, by quantitative polymerase chain reaction. The higher pBI143 concentrations (9.6 ± 0.5 log10 copies/L) compared to PMMoV (8.2 ± 0.2 log10 copies/L) and crAssphage (8.0 ± 0.2 log10 copies/L) highlighted its potential as a robust marker for human fecal contamination. Unlike PMMoV and crAssphage that remained stable across seasons, pBI143 showed seasonal fluctuations, especially during summer and autumn, suggesting its greater sensitivity to environmental conditions. The study evaluated the suitability of pBI143, crAssphage, and PMMoV for normalizing SARS-CoV-2 concentrations in wastewater; however, non-normalized SARS-CoV-2 concentrations showed the highest correlation with COVID-19 cases (ρ = 0.74), whereas normalization reduced this correlation (PMMoV-normalized, ρ = 0.72; crAssphage-normalized, ρ = 0.70; and pBI143-normalized, ρ = 0.50), likely due to differences in the persistence and structural properties of the markers, indicating that these markers are less effective for SARS-CoV-2 normalization. This study underscores the promising utility of pBI143 in wastewater surveillance but highlights the need for further research across diverse regions to validate its applicability.

Spatiotemporal Variability of the Pepper Mild Mottle Virus Biomarker in Wastewater

Since the start of the coronavirus-19 pandemic, the use of wastewater-based epidemiology (WBE) for disease surveillance has increased throughout the world. Because wastewater measurements are affected by external factors, processing WBE data typically includes a normalization step in order to adjust wastewater measurements (e.g., viral ribonucleic acid (RNA) concentrations) to account for variation due to dynamic population changes, sewer travel effects, or laboratory methods. Pepper mild mottle virus (PMMoV), a plant RNA virus abundant in human feces and wastewater, has been used as a fecal contamination indicator and has been used to normalize wastewater measurements extensively. However, there has been little work to characterize the spatiotemporal variability of PMMoV in wastewater, which may influence the effectiveness of PMMoV for adjusting or normalizing WBE measurements. Here, we investigate its variability across space and time using data collected over a two-year period from sewage treatment plants across the United States. We find that most variation in PMMoV measurements can be attributed to longitude and latitude followed by site-specific variables. Further research into cross-geographical and -temporal comparability of PMMoV-normalized pathogen concentrations would strengthen the utility of PMMoV in WBE.

Environmental Dissemination of SARS-CoV-2: An Analysis Employing Crassphage and Next-Generation Sequencing Protocols

This study aimed to investigate the dissemination of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in water samples obtained during the coronavirus disease 2019 pandemic period, employing cross-assembly phage (crAssphage) as a fecal contamination biomarker and next-generation sequencing protocols to characterize SARS-CoV-2 variants. Raw wastewater and surface water (stream and sea) samples were collected for over a month in Rio de Janeiro, Brazil. Ultracentrifugation and negatively charged membrane filtration were employed for viral concentration of the wastewater and surface water samples, respectively. Viruses were detected and quantified by (RT-)qPCR applying TaqMan® system protocols. SARS-CoV-2 RNA signals were detected in 92.5% (37/40) of the wastewater samples and in 31.25% (10/32) of the stream water samples, but not in seawater samples. CrAssphage was detected in 100% of the wastewater samples, 93.75% (30/32) of the stream samples, and in 2/4 of the seawater samples. CrAssphage detection and high concentrations in stream surface waters (median 8.95 log10 gc/L) revealed diffuse contamination by domestic wastewater in a region with high sanitary coverage. The correlations detected between SARS-CoV-2 data and the moving averages of clinical cases per capita over the sampling period were moderate to strong when applying a 13-day offset, regardless of normalization by crAssphage data or not. Sequencing of the receptor-binding domain of the spike protein confirmed the detection of SARS-CoV-2, but did not characterize the circulating variant. On the other hand, the whole genome sequencing protocol identified circulation of the Gamma variant, corroborating the sampling period clinical data.

Wastewater-based epidemiology: deriving a SARS-CoV-2 data validation method to assess data quality and to improve trend recognition

Introduction

Accurate and consistent data play a critical role in enabling health officials to make informed decisions regarding emerging trends in SARS-CoV-2 infections. Alongside traditional indicators such as the 7-day-incidence rate, wastewater-based epidemiology can provide valuable insights into SARS-CoV-2 concentration changes. However, the wastewater compositions and wastewater systems are rather complex. Multiple effects such as precipitation events or industrial discharges might affect the quantification of SARS-CoV-2 concentrations. Hence, analysing data from more than 150 wastewater treatment plants (WWTP) in Germany necessitates an automated and reliable method to evaluate data validity, identify potential extreme events, and, if possible, improve overall data quality.

Methods

We developed a method that first categorises the data quality of WWTPs and corresponding laboratories based on the number of outliers in the reproduction rate as well as the number of implausible inflection points within the SARS-CoV-2 time series. Subsequently, we scrutinised statistical outliers in several standard quality control parameters (QCP) that are routinely collected during the analysis process such as the flow rate, the electrical conductivity, or surrogate viruses like the pepper mild mottle virus. Furthermore, we investigated outliers in the ratio of the analysed gene segments that might indicate laboratory errors. To evaluate the success of our method, we measure the degree of accordance between identified QCP outliers and outliers in the SARS-CoV-2 concentration curves.

Results and discussion

Our analysis reveals that the flow and gene segment ratios are typically best at identifying outliers in the SARS-CoV-2 concentration curve albeit variations across WWTPs and laboratories. The exclusion of datapoints based on QCP plausibility checks predominantly improves data quality. Our derived data quality categories are in good accordance with visual assessments.

Conclusion

Good data quality is crucial for trend recognition, both on the WWTP level and when aggregating data from several WWTPs to regional or national trends. Our model can help to improve data quality in the context of health-related monitoring and can be optimised for each individual WWTP to account for the large diversity among WWTPs.

The viral trends and genotype diversity of norovirus in the wastewater of Shenzhen, China

Norovirus (NoV) is the primary cause of acute gastroenteritis (AGE) on a global scale. Numerous studies have demonstrated the immense potential of wastewater surveillance in monitoring the prevalence and spread of NoV within communities. This study employed a one-step reverse transcription-quantitative PCR to quantify NoV GI/GII in wastewater samples (n = 2574), which were collected once or twice a week from 38 wastewater treatment plants from March 2023 to February 2024 in Shenzhen. The concentrations of NoV GI and GII ranged from 5.0 × 104 to 1.7 × 106 copies/L and 4.1 × 105 to 4.5 × 106 copies/L, respectively. The concentrations of NoV GII were higher than those of NoV GI. Spearman's correlation analysis revealed a moderate correlation between the concentration of NoV in wastewater and the detection rates of NoV infections in sentinel hospitals. Baseline values were established for NoV concentrations in Shenzhen's wastewater, providing a crucial reference point for implementing early warning systems and nonpharmaceutical interventions to mitigate the impact of potential outbreaks. A total of 24 NoV genotypes were identified in 100 wastewater samples by sequencing. Nine genotypes of NoV GI were detected, with the major genotypes being GI.4 (38.6 %) and GI.3 (21.8 %); Fifteen genotypes of NoV GII were identified, with GII.4 (53.6 %) and GII.17 (26.0 %) being dominant. The trends in the relative abundance of NoV GI/GII were significantly different, and the trends in the relative abundance of NoV GII.4 over time were similar across all districts, suggesting a potential risk of cross-regional spread. Our findings underscore the effectiveness of wastewater surveillance in reflecting population-level NoV infections, capturing the diverse array of NoV genotypes, and utilizing NoV RNA in wastewater as a specific indicator to supplement clinical surveillance data, ultimately enhancing our ability to predict the timing and intensity of NoV epidemics.

Effective method to mitigate impact of rain or snowmelt sewer flushing events on wastewater-based surveillance measurements

Wastewater-based surveillance (WBS) is increasingly used for monitoring disease targets in wastewaters around the world. This study, performed in Ottawa, Canada, identifies a decrease in SARS-CoV-2 wastewater measurements during snowmelt-induced sewer flushing events. Observations first revealed a correlation between suppressed viral measurements and periods of increased sewage flowrates, air temperatures above 0 °C during winter months, and solids mass flux increases. These correlations suggest that high sewage flowrates from snowmelt events or intense precipitation events lead to the scouring of previously settled solids in sewers and the subsequent entrainment of these solids into the transported wastewaters. Collection of WBS samples during flushing events hence contains a heterogeneous mixture of solids, including resuspended solids with varying degrees of decay. Therefore flushing events can present a challenge for accurately measuring disease target viral signals when using solids-based analytical methods. This study demonstrates that resuspended solids entrained in the wastewaters during flushing events retain PMMoV signal while the SARS-CoV-2 signal is significantly reduced due to the slower decay rate of pepper mild mottle virus (PMMoV) compared to SARS-CoV-2 within wastewaters. Hence current normalization methods using PMMoV are shown to be ineffective in correcting for flushing events and the associated resuspension of settled solids, as the PMMoV signal of settled solids within sewers does not account for the differential decay rates experiences by SARS-CoV-2 signal in settled solids. Instead, this study identifies RNA to PMMoV correction factor as an effective approach to correct for flushing events and to realign SARS-CoV-2 signal with COVID-19 hospital admission rates within communities. As such, the study highlights the key physicochemical parameters necessary to identify flushing events that affect SARS-CoV-2 WBS measurements and introduces a novel RNA to PMMoV correction factor approach for solids-based analysis of SARS-CoV-2 during flushing events, enhancing the accuracy of WBS data for public health decision-making.

Rapid and extensive SARS-CoV-2 Omicron variant infection wave revealed by wastewater surveillance in Shenzhen following the lifting of a strict COVID-19 strategy

Wastewater-based epidemiology (WBE) has emerged as a promising tool for monitoring the spread of COVID-19, as SARS-CoV-2 can be shed in the faeces of infected individuals, even in the absence of symptoms. This study aimed to optimize a prediction model for estimating COVID-19 infection rates based on SARS-CoV-2 RNA concentrations in wastewater, and reveal the infection trends and variant diversification in Shenzhen, China following the lifting of a strict COVID-19 strategy. Faecal samples (n = 4337) from 1204 SARS-CoV-2 infected individuals hospitalized in a designated hospital were analysed to obtain Omicron variant-specific faecal shedding dynamics. Wastewater samples from 6 wastewater treatment plants (WWTPs) and 9 pump stations, covering 3.55 million people, were monitored for SARS-CoV-2 RNA concentrations and variant abundance. We found that the viral load in wastewater increased rapidly in December 2022 in the two districts, demonstrating a sharp peak in COVID-19 infections in late-December 2022, mainly caused by Omicron subvariants BA.5.2.48 and BF.7.14. The prediction model, based on the mass balance between total viral load in wastewater and individual faecal viral shedding, revealed a surge in the cumulative infection rate from <0.1 % to over 70 % within three weeks after the strict COVID-19 strategy was lifted. Additionally, 39 cryptic SARS-CoV-2 variants were identified in wastewater, in addition to those detected through clinical surveillance. These findings demonstrate the effectiveness of WBE in providing comprehensive and efficient assessments of COVID-19 infection rates and identifying cryptic variants, highlighting its potential for monitoring emerging pathogens with faecal shedding.

Beyond COVID-19: Wastewater-based epidemiology for multipathogen surveillance and normalization strategies

Wastewater-based epidemiology (WBE) is a critical tool for monitoring community health. Although much attention has focused on severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), a causative agent of coronavirus disease 2019 (COVID-19), other pathogens also pose significant health risks. This study quantified the presence of SARS-CoV-2, influenza A virus (Inf-A), and noroviruses of genogroups I (NoV-GI) and II (NoV-GII) in wastewater samples collected weekly (n = 170) from July 2023 to February 2024 from five wastewater treatment plants (WWTPs) in Yamanashi Prefecture, Japan, by quantitative PCR. Inf-A RNA exhibited localized prevalence with positive ratios of 59 %-82 % in different WWTPs, suggesting regional outbreaks within specific areas. NoV-GI (94 %, 160/170) and NoV-GII (100 %, 170/170) RNA were highly prevalent, with NoV-GII (6.1 ± 0.8 log10 copies/L) consistently exceeding NoV-GI (5.4 ± 0.7 log10 copies/L) RNA concentrations. SARS-CoV-2 RNA was detected in 100 % of the samples, with mean concentrations of 5.3 ± 0.5 log10 copies/L in WWTP E and 5.8 ± 0.4 log10 copies/L each in other WWTPs. Seasonal variability was evident, with higher concentrations of all pathogenic viruses during winter. Non-normalized and normalized virus concentrations by fecal indicator bacteria (Escherichia coli and total coliforms), an indicator virus (pepper mild mottle virus (PMMoV)), and turbidity revealed significant positive associations with the reported disease cases. Inf-A and NoV-GI + GII RNA concentrations showed strong correlations with influenza and acute gastroenteritis cases, particularly when normalized to E. coli (Spearman's ρ = 0.70-0.81) and total coliforms (ρ = 0.70-0.81), respectively. For SARS-CoV-2, non-normalized concentrations showed a correlation of 0.61, decreasing to 0.31 when normalized to PMMoV, suggesting that PMMoV is unsuitable. Turbidity normalization also yielded suboptimal results. This study underscored the importance of selecting suitable normalization parameters tailored to specific pathogens for accurate disease trend monitoring using WBE, demonstrating its utility beyond COVID-19 surveillance.

Normalization of viral loads in Wastewater-Based Epidemiology using routine parameters: One year monitoring of SARS-CoV-2 in urban and tourist sewersheds

In wastewater-based epidemiology, normalization of experimental data is a crucial aspect, as emerged in the recent surveillance of COVID-19. Normalization facilitates the comparison between different areas or periods, and it helps in evaluating the differences due to the fluctuation of the population due to seasonal employment or tourism. Analysis of biomarkers in wastewater (i.e. drugs, beverage and food compounds, microorganisms such as PMMoV or crAssphage, etc.) is complex to perform, and it is not routinely monitored. This study compares the results of alternative normalization approaches applied to SARS-CoV-2 loads in wastewater using population size calculated with conventional hydraulic and/or chemical parameters (i.e. total suspended solids, chemical oxygen demand, nitrogen forms, etc.) commonly used in the routine monitoring of water quality. A total of 12 wastewater treatment plants were monitored, and 1068 samples of influent wastewater were collected in urban areas and in highly touristic areas (summer and/or winter). The results indicated that both census and population estimated with ammonium are effective and reliable parameters with which to normalize SARS-CoV-2 loads in wastewater from urban sewersheds with negligible fluctuating populations. However, this study reveals that, in the case of tourist locations, the population calculated using NH4-N loads can provide a better normalization of the specific viral load per inhabitant.

Longitudinal fecal shedding of SARS-CoV-2, pepper mild mottle virus, and human mitochondrial DNA in COVID-19 patients

Since the coronavirus disease 2019 (COVID-19) pandemic, wastewater-based epidemiology (WBE) has been widely applied in many countries and regions for monitoring COVID-19 transmission in the population through testing severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in wastewater. However, the amount of virus shed by individuals over time based on the stage of infection and accurate number of infections in the community creates challenges in predicting COVID-19 prevalence in the population and interpreting WBE results. In this study, we measured SARS-CoV-2, pepper mild mottle virus (PMMoV), and human mitochondrial DNA (mtDNA) in longitudinal fecal samples collected from 42 COVID-19 patients for up to 42 days after diagnosis. SARS-CoV-2 RNA was detected in 73.1% (19/26) of inpatient study participants in at least one of the collected fecal specimens during the sampling period. Most participants shed the virus within 3 weeks after diagnosis, but five inpatient participants still shed the virus between 20 and 60 days after diagnosis. The median concentration of SARS-CoV-2 in positive fecal samples was 1.08 × 105 genome copies (GC)/gram dry fecal material. PMMoV and mtDNA were detected in 99.4% (154/155) and 100% (155/155) of all fecal samples, respectively. The median concentrations of PMMoV RNA and mtDNA in fecal samples were 1.73 × 107 and 2.49 × 108 GC/dry gram, respectively. These results provide important information about the dynamics of fecal shedding of SARS-CoV-2 and two human fecal indicators in COVID-19 patients. mtDNA showed higher positive rates, higher concentrations, and less variability between and within individuals than PMMoV, suggesting that mtDNA could be a better normalization factor for WBE results than PMMoV.

Wastewater threshold as an indicator of COVID-19 cases in correctional facilities for public health response: A modeling study

Although prison facilities are not fully isolated from the communities in which they are located, most of the population is confined and requires high levels of health vigilance and protection. This study aimed to examine the dynamic relationship between facility-level wastewater viral concentrations and the probability of at least one positive COVID-19 case within the facility. The study period was from January 11, 2021 to May 8, 2023. Wastewater samples were collected and analyzed for SARS-CoV-2 (N1) and pepper mild mottle virus (PMMoV) three times weekly across 14 prison facilities in Kentucky (USA). Positive clinical case reports were also provided. A hierarchical Bayesian facility-level temporal model with a latent lagged process was developed. We modeled facility-specific SARS-CoV-2 (N1) normalized by the PMMoV wastewater concentration ratio threshold associated with at least one COVID-19 clinical case at an 80 % probability. The threshold differed among facilities. Across the 14 facilities, our model demonstrates a mean capture rate of 94.95 % via the N1/PMMoV ratio threshold with pts≥0.5. However, as the pts threshold was set higher, such as at ≥0.9, the mean capture rate of the model was reduced to 60 %. This robust performance underscores the effectiveness of the model for accurately detecting the presence of positive COVID-19 cases among incarcerated people. The findings of this study provide a facility-specific threshold model for public health response based on frequent wastewater surveillance.

Advancing Public Health Surveillance: Integrating Modeling and GIS in the Wastewater-Based Epidemiology of Viruses, a Narrative Review

This review article will present a comprehensive examination of the use of modeling, spatial analysis, and geographic information systems (GIS) in the surveillance of viruses in wastewater. With the advent of global health challenges like the COVID-19 pandemic, wastewater surveillance has emerged as a crucial tool for the early detection and management of viral outbreaks. This review will explore the application of various modeling techniques that enable the prediction and understanding of virus concentrations and spread patterns in wastewater systems. It highlights the role of spatial analysis in mapping the geographic distribution of viral loads, providing insights into the dynamics of virus transmission within communities. The integration of GIS in wastewater surveillance will be explored, emphasizing the utility of such systems in visualizing data, enhancing sampling site selection, and ensuring equitable monitoring across diverse populations. The review will also discuss the innovative combination of GIS with remote sensing data and predictive modeling, offering a multi-faceted approach to understand virus spread. Challenges such as data quality, privacy concerns, and the necessity for interdisciplinary collaboration will be addressed. This review concludes by underscoring the transformative potential of these analytical tools in public health, advocating for continued research and innovation to strengthen preparedness and response strategies for future viral threats. This article aims to provide a foundational understanding for researchers and public health officials, fostering advancements in the field of wastewater-based epidemiology.

Assessment of crAssphage as a biological variable for SARS-CoV-2 data normalization in wastewater surveillance

AIMS

This study aimed to assess the use of cross-assembled phage (crAssphage) as an endogenous control employing a multivariate normalization analysis and its application as a severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) data normalizer.

METHODS AND RESULTS

A total of 188 twelve-hour composite raw sewage samples were obtained from eight wastewater treatment plants (WWTP) during a 1-year monitoring period. Employing the N1 and N2 target regions, SARS-CoV-2 RNA was detected in 94% (177) and 90% (170) of the samples, respectively, with a global median of 5 log10 genomic copies per liter (GC l-1). CrAssphage was detected in 100% of the samples, ranging from 8.29 to 10.43 log10 GC l-1, with a median of 9.46 ± 0.40 log10 GC l-1, presenting both spatial and temporal variabilities.

CONCLUSIONS

Although SARS-CoV-2 data normalization employing crAssphage revealed a correlation with clinical cases occurring during the study period, crAssphage normalization by the flow per capita per day of each WWTP increased this correlation, corroborating the importance of normalizing wastewater surveillance data in disease trend monitoring.

Identification of environmental and methodological factors driving variability of Pepper Mild Mottle Virus (PMMoV) across three wastewater treatment plants in the City of Toronto

PMMoV has been widely used to normalize the concentration of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) RNA, influenza, and respiratory syncytial virus (RSV) to account for variations in the fecal content of wastewater. PMMoV is also used as an internal RNA recovery control for wastewater-based epidemiology (WBE) tests. While potentially useful for the interpretation of WBE data, previous studies have suggested that PMMoV concentration can be affected by various physico-chemical characteristics of wastewater. There is also the possibility that laboratory methods, particularly the variability in centrifugation steps to remove supernatant from pellets can cause PMMoV variability. The goal of this study is to improve our understanding of the main drivers of PMMoV variability by assessing the relationship between PMMoV concentration, the physico-chemical characteristics of wastewater, and the methodological approach for concentrating wastewater samples. We analyzed 24-hour composite wastewater samples collected from the influent stream of three wastewater treatment plants (WWTPs) located in the City of Toronto, Ontario, Canada. Samples were collected 3 to 5 times per week starting from the beginning of March 2021 to mid-July 2023. The influent flow rate was used to partition the data into wet and dry weather conditions. Physico-chemical characteristics (e.g., total suspended solids (TSS), biological oxygen demand (BOD), alkalinity, electrical conductivity (EC), and ammonia (NH3)) of the raw wastewater were measured, and PMMoV was quantified. Spatial and temporal variability of PMMoV was observed throughout the study period. PMMoV concentration was significantly higher during dry weather conditions. Multiple linear regression analysis demonstrates that the number and type of physico-chemical parameters that drive PMMoV variability are site-specific, but overall BOD and alkalinity were the most important predictors. Differences in PMMoV concentration for a single WWTP between two different laboratory methods, along with a weak correlation between pellet mass and TSS using one method may indicate that differences in sample concentration and subjective subsampling bias could alter viral recovery and introduce variability to the data.

Small-scale wastewater-based epidemiology (WBE) for infectious diseases and antibiotic resistance: A scoping review

Wastewater analysis can serve as a source of public health information. In recent years, wastewater-based epidemiology (WBE) has emerged and proven useful for the detection of infectious diseases. However, insights from the wastewater treatment plant do not allow for the small-scale differentiation within the sewer system that is needed to analyze the target population under study in more detail. Small-scale WBE offers several advantages, but there has been no systematic overview of its application. The aim of this scoping review is to provide a comprehensive overview of the current state of knowledge on small-scale WBE for infectious diseases, including methodological considerations for its application. A systematic database search was conducted, considering only peer-reviewed articles. Data analyses included quantitative summary and qualitative narrative synthesis. Of 2130 articles, we included 278, most of which were published since 2020. The studies analyzed wastewater at the building level (n = 203), especially healthcare (n = 110) and educational facilities (n = 80), and at the neighborhood scale (n = 86). The main analytical parameters were viruses (n = 178), notably SARS-CoV-2 (n = 161), and antibiotic resistance (ABR) biomarkers (n = 99), often analyzed by polymerase chain reaction (PCR), with DNA sequencing techniques being less common. In terms of sampling techniques, active sampling dominated. The frequent lack of detailed information on the specification of selection criteria and the characterization of the small-scale sampling sites was identified as a concern. In conclusion, based on the large number of studies, we identified several methodological considerations and overarching strategic aspects for small-scale WBE. An enabling environment for small-scale WBE requires inter- and transdisciplinary knowledge sharing across countries. Promoting the adoption of small-scale WBE will benefit from a common international conceptualization of the approach, including standardized and internationally accepted terminology. In particular, the development of good WBE practices for different aspects of small-scale WBE is warranted. This includes the establishment of guidelines for a comprehensive characterization of the local sewer system and its sub-sewersheds, and transparent reporting to ensure comparability of small-scale WBE results.

Tracking diarrhea viruses and mpox virus using the wastewater surveillance network in Hong Kong

The wastewater surveillance network successfully established for COVID-19 showed great potential to monitor other infectious viruses, such as norovirus, rotavirus and mpox virus. In this study, we established and validated detection methods for these viruses in wastewater. We developed a supernatant-based method to detect RNA viruses from wastewater samples and applied it to the monthly diarrhea viruses (norovirus genogroup I & II, and rotavirus) surveillance in wastewater treatment plants (WWTPs) at a city-wide level for 16 months. Significant correlations were observed between the diarrhea viruses concentrations in wastewater and detection rates in faecal specimens by clinical surveillance. The highest norovirus concentration in wastewater was obtained in winter, consistent with the seasonal pattern of norovirus outbreak in Hong Kong. Additionally, we established a pellet-based method to monitor DNA viruses in wastewater and detected weak signals for mpox virus in wastewater from a WWTP serving approximately 16,700 people, when the first mpox patient in Hong Kong was admitted to the hospital within the catchment area. Genomic sequencing provided confirmatory evidence for the validity of the results. Our findings emphasized the efficacy of the wastewater surveillance network in WWTPs as a cost-effective tool to track the transmission trend of diarrhea viruses and to provide sensitive detection of novel emerging viruses such as mpox virus in low-prevalence areas. The developed methods and surveillance results provide confidence for establishing robust wastewater surveillance programs to control infectious diseases in the post-pandemic era.

COVID-19 trends at the University of Tennessee: predictive insights from raw sewage SARS-CoV-2 detection and evaluation and PMMoV as an indicator for human waste

Wastewater-based epidemiology (WBE) has become a valuable tool for monitoring the prevalence of SARS-CoV-2 on university campuses. However, concerns about effectiveness of raw sewage as a COVID-19 early warning system still exist, and it's not clear how useful normalization by simultaneous comparison of Pepper Mild Mottle Virus (PMMoV) is in addressing variations resulting from fecal discharge dilution. This study aims to contribute insights into these aspects by conducting an academic-year field trial at the student residences on the University of Tennessee, Knoxville campus, raw sewage. This was done to investigate the correlations between SARS-CoV-2 RNA load, both with and without PMMoV normalization, and various parameters, including active COVID-19 cases, self-isolations, and their combination among all student residents. Significant positive correlations between SARS-CoV-2 RNA load a week prior, during the monitoring week, and the subsequent week with active cases. Despite these correlations, normalization by PMMoV does not enhance these associations. These findings suggest the potential utility of SARS-CoV-2 RNA load as an early warning indicator and provide valuable insights into the application and limitations of WBE for COVID-19 surveillance specifically within the context of raw sewage on university campuses.

Wastewater-based surveillance as a tool for public health action: SARS-CoV-2 and beyond

Wastewater-based surveillance (WBS) has undergone dramatic advancement in the context of the coronavirus disease 2019 (COVID-19) pandemic. The power and potential of this platform technology were rapidly realized when it became evident that not only did WBS-measured SARS-CoV-2 RNA correlate strongly with COVID-19 clinical disease within monitored populations but also, in fact, it functioned as a leading indicator. Teams from across the globe rapidly innovated novel approaches by which wastewater could be collected from diverse sewersheds ranging from wastewater treatment plants (enabling community-level surveillance) to more granular locations including individual neighborhoods and high-risk buildings such as long-term care facilities (LTCF). Efficient processes enabled SARS-CoV-2 RNA extraction and concentration from the highly dilute wastewater matrix. Molecular and genomic tools to identify, quantify, and characterize SARS-CoV-2 and its various variants were adapted from clinical programs and applied to these mixed environmental systems. Novel data-sharing tools allowed this information to be mobilized and made immediately available to public health and government decision-makers and even the public, enabling evidence-informed decision-making based on local disease dynamics. WBS has since been recognized as a tool of transformative potential, providing near-real-time cost-effective, objective, comprehensive, and inclusive data on the changing prevalence of measured analytes across space and time in populations. However, as a consequence of rapid innovation from hundreds of teams simultaneously, tremendous heterogeneity currently exists in the SARS-CoV-2 WBS literature. This manuscript provides a state-of-the-art review of WBS as established with SARS-CoV-2 and details the current work underway expanding its scope to other infectious disease targets.

Identifying spatiotemporal trends of SARS-CoV-2 RNA in wastewater: from the perspective of upstream and downstream wastewater-based epidemiology (WBE)

Recently, many efforts have been made to address the rapid spread of newly identified COVID-19 virus variants. Wastewater-based epidemiology (WBE) is considered a potential early warning tool for identifying the rapid spread of this virus. This study investigated the occurrence of SARS-CoV-2 in eight wastewater treatment plants (WWTPs) and their sewerage systems which serve most of the population in Taoyuan City, Taiwan. Across the entire study period, the wastewater viral concentrations were correlated with the number of COVID-19 cases in each WWTP (Spearman's r = 0.23-0.76). In addition, it is confirmed that several treatment technologies could effectively eliminate the virus RNA from WWTP influent (> 90%). On the other hand, further results revealed that an inverse distance weighted (IDW) interpolation and hotspot model combined with the geographic information system (GIS) method could be applied to analyze the spatiotemporal variations of SARS-CoV-2 in wastewater from the sewer system. In addition, socio-economic factors, namely, population density, land use, and income tax were successfully identified as the potential drivers which substantially affected the onset of the COVID-19 outbreak in Taiwan. Finally, the data obtained from this study can provide a powerful tool in public health decision-making not only in response to the current epidemic situation but also to other epidemic issues in the future.

COVID-19 hospitalizations and deaths predicted by SARS-CoV-2 levels in Boise, Idaho wastewater

The viral load of COVID-19 in untreated wastewater from Idaho's capital city Boise, ID (Ada County) has been used to predict changes in hospital admissions (statewide in Idaho) and deaths (Ada County) using distributed fixed lag modeling and artificial neural networks (ANN). The wastewater viral counts were used to determine the lag time between peaks in wastewater viral counts and COVID-19 hospitalizations as well as deaths (14 and 23 days, respectively). Quantitative measurement of SARS-CoV-2 viral RNA counts in the untreated wastewater was determined three times a week using RT-qPCR over a span of 13 months. To mitigate the effects of PCR inhibitors in wastewater, a series of dilution tests were conducted, and the 1/4 dilution was used to generate the most successful model. Wastewater SARS-CoV-2 viral RNA counts and hospitalization from June 7, 2021 to December 29, 2021 were used as training data to predict hospitalizations; and wastewater SARS-CoV-2 viral RNA counts and deaths from June 7, 2021 to December 20, 2021 were used as training data to predict deaths. These training data were used to make predictive ANN models for future hospitalizations and deaths. To the best of our knowledge, this is the first report of prediction of deaths from COVID-19 based on wastewater SARS-CoV-2 viral RNA counts using machine learning-based multilayered ANN. The applied modeling demonstrates that wastewater surveillance data can be combined with hospitalizations and death data to generate machine learning-based ANN models that predict future COVID-19 hospital admissions and deaths, providing an early warning for medical response teams and healthcare policymakers.

Predictive power of wastewater for nowcasting infectious disease transmission: A retrospective case study of five sewershed areas in Louisville, Kentucky

BACKGROUND

Epidemiological nowcasting traditionally relies on count surveillance data. The availability and quality of such count data may vary over time, limiting representation of true infections. Wastewater data correlates with traditional surveillance data and may provide additional value for nowcasting disease trends.

METHODS

We obtained SARS-CoV-2 case, death, wastewater, and serosurvey data for Jefferson County, Kentucky (USA), between August 2020 and March 2021, and parameterized an existing nowcasting model using combinations of these data. We assessed the predictive performance and variability at the sewershed level and compared the effects of adding or replacing wastewater data to case and death reports.

FINDINGS

Adding wastewater data minimally improved the predictive performance of nowcasts compared to a model fitted to case and death data (Weighted Interval Score (WIS) 0.208 versus 0.223), and reduced the predictive performance compared to a model fitted to deaths data (WIS 0.517 versus 0.500). Adding wastewater data to deaths data improved the nowcasts agreement to estimates from models using cases and deaths data. These findings were consistent across individual sewersheds as well as for models fit to the aggregated total data of 5 sewersheds. Retrospective reconstructions of epidemiological dynamics created using different combinations of data were in general agreement (coverage >75%).

INTERPRETATION

These findings show wastewater data may be valuable for infectious disease nowcasting when clinical surveillance data are absent, such as early in a pandemic or in low-resource settings where systematic collection of epidemiologic data is difficult.

Effectiveness of environmental surveillance of SARS-CoV-2 as an early-warning system: Update of a systematic review during the second year of the pandemic

The aim of this updated systematic review was to offer an overview of the effectiveness of environmental surveillance (ES) of SARS-CoV-2 as a potential early-warning system (EWS) for COVID-19 and new variants of concerns (VOCs) during the second year of the pandemic. An updated literature search was conducted to evaluate the added value of ES of SARS-CoV-2 for public health decisions. The search for studies published between June 2021 and July 2022 resulted in 1,588 publications, identifying 331 articles for full-text screening. A total of 151 publications met our inclusion criteria for the assessment of the effectiveness of ES as an EWS and early detection of SARS-CoV-2 variants. We identified a further 30 publications among the grey literature. ES confirms its usefulness as an EWS for detecting new waves of SARS-CoV-2 infection with an average lead time of 1-2 weeks for most of the publication. ES could function as an EWS for new VOCs in areas with no registered cases or limited clinical capacity. Challenges in data harmonization and variant detection require standardized approaches and innovations for improved public health decision-making. ES confirms its potential to support public health decision-making and resource allocation in future outbreaks.

Evaluation of four human-associated fecal biomarkers in wastewater in Southern Ontario

Human fecal biomarkers (HFBs) have a longstanding history in the field of microbial source tracking (MST) serving as indicators of human fecal contamination in drinking and recreational water. Further, HFBs have aided in recent efforts to monitor human pathogen transmission within communities. The dilution of wastewater from various sources throughout the sewershed cannot be controlled and human fecal biomarkers (HFBs) can be used to normalize target human pathogen concentrations so that fluctuations in fecal matter in wastewater can be accounted for. In the current study, we monitored the prevalence of four HFBs - including two viruses, Pepper mild mottle virus (PMMoV), cross-assembly phage (crAssphage), as well as two human-associated Bacteroides markers, HF183 and BacHuman - in wastewater samples from ten Southern Ontario wastewater treatment plants and evaluated their temporal and spatial variation in context of environmental factors that may impact the ability of HFB to normalize pathogen concentrations in wastewater. Environmental variables including precipitation, wastewater flow rate, temperature, and concentrated mass were also analyzed for their potential correlation with HFB variation in wastewater. The four HFBs were detected at high concentrations across all 10 sampling locations. The median concentrations across all sampling sites were: PMMoV 3.6 Log gene copies (GC)/mL; crAssphage 5.0 Log GC/mL; HF183 6.8 Log GC/mL and BacHuman 6.9 Log GC/mL. All HFBs were found to be similarly stratified across all 10 sites, and the bacterial markers were consistently found at higher concentration compared to the viral HFBs at all sites. The coefficient of variation (CV) for each HFB was used to characterize the variability of each biomarker at each sewershed. BacHuman and crAssphage were found to have lower CV than PMMoV and HF183, indicating that BacHuman and crAssphage may perform better in reflecting the variations in abundance of human feces in wastewater or MST applications.

Characterizing Spatial Information Loss for Wastewater Surveillance Using crAssphage: Effect of Decay, Temperature, and Population Mobility

Populations contribute information about their health status to wastewater. Characterizing how that information degrades in transit to wastewater sampling locations (e.g., wastewater treatment plants and pumping stations) is critical to interpret wastewater responses. In this work, we statistically estimate the loss of information about fecal contributions to wastewater from spatially distributed populations at the census block group resolution. This was accomplished with a hydrologically and hydraulically influenced spatial statistical approach applied to crAssphage (Carjivirus communis) load measured from the influent of four wastewater treatment plants in Hamilton County, Ohio. We find that we would expect to observe a 90% loss of information about fecal contributions from a given census block group over a travel time of 10.3 h. This work demonstrates that a challenge to interpreting wastewater responses (e.g., during wastewater surveillance) is distinguishing between a distal but large cluster of contributions and a near but small contribution. This work demonstrates new modeling approaches to improve measurement interpretation depending on sewer network and wastewater characteristics (e.g., geospatial layout, temperature variability, population distribution, and mobility). This modeling can be integrated into standard wastewater surveillance methods and help to optimize sewer sampling locations to ensure that different populations (e.g., vulnerable and susceptible) are appropriately represented.

Simultaneous detection and quantification of multiple pathogen targets in wastewater

Wastewater-based epidemiology has emerged as a critical tool for public health surveillance, building on decades of environmental surveillance work for pathogens such as poliovirus. Work to date has been limited to monitoring a single pathogen or small numbers of pathogens in targeted studies; however, few studies consider simultaneous quantitative analysis of a wide variety of pathogens, which could greatly increase the utility of wastewater surveillance. We developed a novel quantitative multi-pathogen surveillance approach (35 pathogen targets including bacteria, viruses, protozoa, and helminths) using TaqMan Array Cards (TAC) and applied the method on concentrated wastewater samples collected at four wastewater treatment plants in Atlanta, GA from February to October of 2020. From sewersheds serving approximately 2 million people, we detected a wide range of targets including many we expected to find in wastewater (e.g., enterotoxigenic E. coli and Giardia in 97% of 29 samples at stable concentrations) as well as unexpected targets including Strongyloides stercoralis (a human threadworm rarely observed in the USA). Other notable detections included SARS-CoV-2, but also several pathogen targets that are not commonly included in wastewater surveillance like Acanthamoeba spp., Balantidium coli, Entamoeba histolytica, astrovirus, norovirus, and sapovirus. Our data suggest broad utility in expanding the scope of enteric pathogen surveillance in wastewaters, with potential for application in a variety of settings where pathogen quantification in fecal waste streams can inform public health surveillance and selection of control measures to limit infections.

Sewage network operational risks based on InfoWorks ICM with nodal flow diurnal patterns under NPIs for COVID-19

Non-Pharmaceutical Interventions (NPIs) have been widely employed globally over the past three years to control the rapid spread of coronavirus disease 2019 (COVID-19). These measures have imposed restrictions on urban residents' activities and significantly influenced sewage discharge characteristics within sewage network, particularly in densely populated cities in China. This study focused on the nodal flow diurnal patterns and sewage network operational risks before and after epidemic lockdown in Beijing from March to May in 2022. Nodal flow diurnal patterns on weekdays and weekends before and after NPIs were analyzed using measured data through statistical and mathematical methods. A sewage network model was established to simulate and analyze the operational risks based on InfoWorks ICM before and after epidemic lockdown. The main conclusions were as follows: (1) In predominantly residential areas, the total wastewater volume increased by approximately 28.76 % to 33.52 % after the implementation of strict NPIs. The morning and midday "M" peaks on normalized weekdays transformed into "N" peaks, and the morning peak time was delayed by 0.5 to 1 hour after the lockdown; (2) Following NPIs, More than 90 % of manholes' average water levels rose to varying degrees, approximately 50 % of pipe lengths exhibited a full flow state; (3) When the lockdown was in place during a hot summer day, sewage overflow phenomena were observed in 4.6 % and 9.6 % of manholes, respectively, with per capita daily drainage equivalent reaching 40-50 %. These findings hold significant implications for the proactive planning and operational management of water industry infrastructure during major emergencies.

Unveiling indicator, enteric, and respiratory viruses in aircraft lavatory wastewater using adsorption-extraction and Nanotrap® Microbiome A Particles workflows

The effective detection of viruses in aircraft wastewater is crucial to establish surveillance programs for monitoring virus spread via aircraft passengers. This study aimed to compare the performance of two virus concentration workflows, adsorption-extraction (AE) and Nanotrap® Microbiome A Particles (NMAP), in detecting the prevalence and concentrations of 15 endogenous viruses including ssDNA, dsDNA, ssRNA in 24 aircraft lavatory wastewater samples. The viruses tested included two indicator viruses, four enteric viruses, and nine respiratory viruses. The results showed that cross-assembly phage (crAssphage), human polyomavirus (HPyV), rhinovirus A (RhV A), and rhinovirus B (RhV B) were detected in all wastewater samples using both workflows. However, enterovirus (EV), human norovirus GII (HNoV GII), human adenovirus (HAdV), bocavirus (BoV), parechovirus (PeV), epstein-barr virus (EBV). Influenza A virus (IAV), and respiratory syncytial virus B (RsV B) were infrequently detected by both workflows, and hepatitis A virus (HAV), influenza B virus (IBV), and respiratory syncytial virus B (RsV A) were not detected in any samples. The NMAP workflow had greater detection rates of RNA viruses (EV, PeV, and RsV B) than the AE workflow, while the AE workflow had greater detection rates of DNA viruses (HAdV, BoV, and EBV) than the NMAP workflow. The concentration of each virus was also analyzed, and the results showed that crAssphage had the highest mean concentration (6.76 log10 GC/12.5 mL) followed by HPyV (5.46 log10 GC/12.5 mL using the AE workflow, while the mean concentrations of enteric and respiratory viruses ranged from 2.48 to 3.63 log10 GC/12.5 mL. Using the NMAP workflow, the mean concentration of crAssphage was 5.18 log10 GC/12.5 mL and the mean concentration of HPyV was 4.20 log10 GC/12.5 mL, while mean concentrations of enteric and respiratory viruses ranged from 2.55 to 3.74 log10 GC/12.5 mL. Significantly higher (p < 0.05) mean concentrations of crAssphage and HPyV were observed when employing the AE workflow in comparison to the NMAP workflow. Conversely, the NMAP workflow yielded significantly greater (p < 0.05) concentrations of RhV A, and RhV B compared to the AE workflow. The findings of this study can aid in the selection of an appropriate concentration workflow for virus surveillance studies and contribute to the development of efficient virus detection methods.

Evaluation of wastewater-based epidemiology of COVID-19 approaches in Singapore's 'closed-system' scenario: A long-term country-wide assessment

With the COVID-19 pandemic the use of WBE to track diseases spread has rapidly evolved into a widely applied strategy worldwide. However, many of the current studies lack the necessary systematic approach and supporting quality of epidemiological data to fully evaluate the effectiveness and usefulness of such methods. Use of WBE in a very low disease prevalence setting and for long-term monitoring has yet to be validated and it is critical for its intended use as an early warning system. In this study we seek to evaluate the sensitivity of WBE approaches under low prevalence of disease and ability to provide early warning. Two monitoring scenarios were used: (i) city wide monitoring (population 5,700,000) and (ii) community/localized monitoring (population 24,000 to 240,000). Prediction of active cases by WBE using multiple linear regression shows that a multiplexed qPCR approach with three gene targets has a significant advantage over single-gene monitoring approaches, with R2 = 0.832 (RMSE 0.053) for an analysis using N, ORF1ab and S genes (R2 = 0.677 to 0.793 for single gene strategies). A predicted disease prevalence of 0.001% (1 in 100,000) for a city-wide monitoring was estimated by the multiplexed RT-qPCR approach and was corroborated by epidemiological data evidence in three 'waves'. Localized monitoring setting shows an estimated detectable disease prevalence of ∼0.002% (1 in 56,000) and is supported by the geospatial distribution of active cases and local population dynamics data. Data analysis also shows that this approach has a limitation in sensitivity, or hit rate, of 62.5 % and an associated high miss rate (false negative rate) of 37.5 % when compared to available epidemiological data. Nevertheless, our study shows that, with enough sampling resolution, WBE at a community level can achieve high precision and accuracies for case detection (96 % and 95 %, respectively) with low false omission rate (4.5 %) even at low disease prevalence levels.

Correlation between wastewater and COVID-19 case incidence rates in major California sewersheds across three variant periods

Monitoring for COVID-19 through wastewater has been used for adjunctive public health surveillance, with SARS-CoV-2 viral concentrations in wastewater correlating with incident cases in the same sewershed. However, the generalizability of these findings across sewersheds, laboratory methods, and time periods with changing variants and underlying population immunity has not been well described. The California Department of Public Health partnered with six wastewater treatment plants starting in January 2021 to monitor wastewater for SARS-CoV-2, with analyses performed at four laboratories. Using reported PCR-confirmed COVID-19 cases within each sewershed, the relationship between case incidence rates and wastewater concentrations collected over 14 months was evaluated using Spearman's correlation and linear regression. Strong correlations were observed when wastewater concentrations and incidence rates were averaged (10- and 7-day moving window for wastewater and cases, respectively, ρ = 0.73-0.98 for N1 gene target). Correlations remained strong across three time periods with distinct circulating variants and vaccination rates (winter 2020-2021/Alpha, summer 2021/Delta, and winter 2021-2022/Omicron). Linear regression revealed that slopes of associations varied by the dominant variant of concern, sewershed, and laboratory (β = 0.45-1.94). These findings support wastewater surveillance as an adjunctive public health tool to monitor SARS-CoV-2 community trends.

Assessment of seasonality and normalization techniques for wastewater-based surveillance in Ontario, Canada

Introduction

Wastewater-based surveillance is at the forefront of monitoring for community prevalence of COVID-19, however, continued uncertainty exists regarding the use of fecal indicators for normalization of the SARS-CoV-2 virus in wastewater. Using three communities in Ontario, sampled from 2021-2023, the seasonality of a viral fecal indicator (pepper mild mottle virus, PMMoV) and the utility of normalization of data to improve correlations with clinical cases was examined.

Methods

Wastewater samples from Warden, the Humber Air Management Facility (AMF), and Kitchener were analyzed for SARS-CoV-2, PMMoV, and crAssphage. The seasonality of PMMoV and flow rates were examined and compared by Season-Trend-Loess decomposition analysis. The effects of normalization using PMMoV, crAssphage, and flow rates were analyzed by comparing the correlations to clinical cases by episode date (CBED) during 2021.

Results

Seasonal analysis demonstrated that PMMoV had similar trends at Humber AMF and Kitchener with peaks in January and April 2022 and low concentrations (troughs) in the summer months. Warden had similar trends but was more sporadic between the peaks and troughs for PMMoV concentrations. Flow demonstrated similar trends but was not correlated to PMMoV concentrations at Humber AMF and was very weak at Kitchener (r = 0.12). Despite the differences among the sewersheds, unnormalized SARS-CoV-2 (raw N1-N2) concentration in wastewater (n = 99-191) was strongly correlated to the CBED in the communities (r = 0.620-0.854) during 2021. Additionally, normalization with PMMoV did not improve the correlations at Warden and significantly reduced the correlations at Humber AMF and Kitchener. Flow normalization (n = 99-191) at Humber AMF and Kitchener and crAssphage normalization (n = 29-57) correlations at all three sites were not significantly different from raw N1-N2 correlations with CBED.

Discussion

Differences in seasonal trends in viral biomarkers caused by differences in sewershed characteristics (flow, input, etc.) may play a role in determining how effective normalization may be for improving correlations (or not). This study highlights the importance of assessing the influence of viral fecal indicators on normalized SARS-CoV-2 or other viruses of concern. Fecal indicators used to normalize the target of interest may help or hinder establishing trends with clinical outcomes of interest in wastewater-based surveillance and needs to be considered carefully across seasons and sites.

Near full-automation of COPMAN using a LabDroid enables high-throughput and sensitive detection of SARS-CoV-2 RNA in wastewater as a leading indicator

Wastewater-based epidemiology (WBE) is a promising tool to efficiently monitor COVID-19 prevalence in a community. For WBE community surveillance, automation of the viral RNA detection process is ideal. In the present study, we achieved near full-automation of a previously established method, COPMAN (COagulation and Proteolysis method using MAgnetic beads for detection of Nucleic acids in wastewater), which was then applied to detect SARS-CoV-2 in wastewater for half a year. The automation line employed the Maholo LabDroid and an automated-pipetting device to achieve a high-throughput sample-processing capability of 576 samples per week. SARS-CoV-2 RNA was quantified with the automated COPMAN using samples collected from two wastewater treatment plants in the Sagami River basin in Japan between 1 November 2021 and 24 May 2022, when the numbers of daily reported COVID-19 cases ranged from 0 to 130.3 per 100,000 inhabitants. The automated COPMAN detected SARS-CoV-2 RNA from 81 out of 132 samples at concentrations of up to 2.8 × 10⁵ copies/L. These concentrations showed direct correlations with subsequently reported clinical cases (5-13 days later), as determined by Pearson's and Spearman's cross-correlation analyses. To compare the results, we also conducted testing with the EPISENS-S (Efficient and Practical virus Identification System with ENhanced Sensitivity for Solids, Ando et al., 2022), a previously reported detection method. SARS-CoV-2 RNA detected with EPISENS-S correlated with clinical cases only when using Spearman's method. Our automated COPMAN was shown to be an efficient method for timely and large-scale monitoring of viral RNA, making WBE more feasible for community surveillance.

Prediction of hospitalisations based on wastewater-based SARS-CoV-2 epidemiology

Wastewater-based epidemiology is widely applied in Austria since April 2020 to monitor the SARS-CoV-2 pandemic. With a steadily increasing number of monitored wastewater facilities, 123 plants covering roughly 70 % of the 9 million population were monitored as of August 2022. In this study, the SARS-CoV-2 viral concentrations in raw sewage were analysed to infer short-term hospitalisation occupancy. The temporal lead of wastewater-based epidemiological time series over hospitalisation occupancy levels facilitates the construction of forecast models. Data pre-processing techniques are presented, including the approach of comparing multiple decentralised wastewater signals with aggregated and centralised clinical data. Time‑lead quantification was performed using cross-correlation analysis and coefficient of determination optimisation approaches. Multivariate regression models were successfully applied to infer hospitalisation bed occupancy. The results show a predictive potential of viral loads in sewage towards Covid-19 hospitalisation occupancy, with an average lead time towards ICU and non-ICU bed occupancy between 14.8-17.7 days and 8.6-11.6 days, respectively. The presented procedure provides access to the trend and tipping point behaviour of pandemic dynamics and allows the prediction of short-term demand for public health services. The results showed an increase in forecast accuracy with an increase in the number of monitored wastewater treatment plants. Trained models are sensitive to changing variant types and require recalibration of model parameters, likely caused by immunity by vaccination and/or infection. The utilised approach displays a practical and rapidly implementable application of wastewater-based epidemiology to infer hospitalisation occupancy.

The dynamic relationship between COVID-19 cases and SARS-CoV-2 wastewater concentrations across time and space: Considerations for model training data sets

During the COVID-19 pandemic, wastewater-based surveillance has been used alongside diagnostic testing to monitor infection rates. With the decline in cases reported to public health departments due to at-home testing, wastewater data may serve as the primary input for epidemiological models, but training these models is not straightforward. We explored factors affecting noise and bias in the ratio between wastewater and case data collected in 26 sewersheds in California from October 2020 to March 2022. The strength of the relationship between wastewater and case data appeared dependent on sampling frequency and population size, but was not increased by wastewater normalization to flow rate or case count normalization to testing rates. Additionally, the lead and lag times between wastewater and case data varied over time and space, and the ratio of log-transformed individual cases to wastewater concentrations changed over time. This ratio decreased between the Epsilon/Alpha and Delta variant surges of COVID-19 and increased during the Omicron BA.1 variant surge, and was also related to the diagnostic testing rate. Based on this analysis, we present a framework of scenarios describing the dynamics of the case to wastewater ratio to aid in data handling decisions for ongoing modeling efforts.

The Implementation and Utilization of Wastewater-Based Epidemiology: Experiences From a Local Health Department

Wastewater-based epidemiology has increasingly demonstrated its importance in addressing public health threats. The COVID-19 pandemic brought forth funding for public health agencies to conduct wastewater-based epidemiology. Using a team with diverse skills, a local health department utilized this funding to regularly monitor SARS-CoV-2 in wastewater on university campuses, a K-12 campus, an inpatient psychiatric facility, and a long-term care facility. Between September 2021 and May 2022, more than 760 wastewater samples were collected of which 102 (13.4%) were above a predetermined threshold. When sites exceeded that threshold, local health department staff provided testing resources. Wastewater-based epidemiology is a useful surveillance program that can be effectively conducted by local health departments when provided with funding and a skilled workforce.

A multistate assessment of population normalization factors for wastewater-based epidemiology of COVID-19

Wastewater-based epidemiology (WBE) has become a valuable tool for monitoring SARS-CoV-2 infection trends throughout the COVID-19 pandemic. Population biomarkers that measure the relative human fecal contribution to normalize SARS-CoV-2 wastewater concentrations are needed for improved analysis and interpretation of community infection trends. The Centers for Disease Control and Prevention National Wastewater Surveillance System (CDC NWSS) recommends using the wastewater flow rate or human fecal indicators as population normalization factors. However, there is no consensus on which normalization factor performs best. In this study, we provided the first multistate assessment of the effects of flow rate and human fecal indicators (crAssphage, F+ Coliphage, and PMMoV) on the correlation of SARS-CoV-2 wastewater concentrations and COVID-19 cases using the CDC NWSS dataset of 182 communities across six U.S. states. Flow normalized SARS-CoV-2 wastewater concentrations produced the strongest correlation with COVID-19 cases. The correlation from the three human fecal indicators were significantly lower than flow rate. Additionally, using reverse transcription droplet digital polymerase chain reaction (RT-ddPCR) significantly improved correlation values over samples that were analyzed with real-time reverse transcription quantitative polymerase chain reaction (rRT-qPCR). Our assessment shows that utilizing flow normalization with RT-ddPCR generate the strongest correlation between SARS-CoV-2 wastewater concentrations and COVID-19 cases.

Wastewater surveillance of SARS-CoV-2 at intra-city level demonstrated high resolution in tracking COVID-19 and calibration using chemical indicators

Wastewater-based epidemiology has proven to be a supportive tool to better comprehend the dynamics of the COVID-19 pandemic. As the disease moves into endemic stage, the surveillance at wastewater sub-catchments such as pump station and manholes is providing a novel mechanism to examine the reemergence and to take measures that can prevent the spread. However, there is still a lack of understanding when it comes to wastewater-based epidemiology implementation at the smaller intra-city level for better granularity in data, and dilution effect of rain precipitation at pump stations. For this study, grab samples were collected from six areas of Seattle between March-October 2021. These sampling sites comprised five manholes and one pump station with population ranging from 2580 to 39,502 per manhole/pump station. The wastewater samples were analyzed for SARS-CoV-2 RNA concentrations, and we also obtained the daily COVID-19 cases (from individual clinical testing) for each corresponding sewershed, which ranged from 1 to 12 and the daily incidence varied between 3 and 64 per 100,000 of population. Rain precipitation lowered viral RNA levels and sensitivity of viral detection but wastewater total ammonia (NH4+-N) and phosphate (PO43--P) were shown as potential chemical indicators to calibrate/level out the dilution effect. These chemicals showed the potential in improving the wastewater surveillance capacity of COVID-19.

Wastewater monitoring of SARS-CoV-2 RNA at K-12 schools: comparison to pooled clinical testing data

Background

Wastewater measurements of SARS-CoV-2 RNA have been extensively used to supplement clinical data on COVID-19. Most examples in the literature that describe wastewater monitoring for SARS-CoV-2 RNA use samples from wastewater treatment plants and individual buildings that serve as the primary residence of community members. However, wastewater surveillance can be an attractive supplement to clinical testing in K-12 schools where individuals only spend a portion of their time but interact with others in close proximity, increasing risk of potential transmission of disease.

Methods

Wastewater samples were collected from two K-12 schools in California and divided into solid and liquid fractions to be processed for detection of SARS-CoV-2. The resulting detection rate in each wastewater fraction was compared to each other and the detection rate in pooled clinical specimens.

Results

Most wastewater samples were positive for SARS-CoV-2 RNA when clinical testing was positive (75% for solid samples and 100% for liquid samples). Wastewater samples continued to test positive for SARS-CoV-2 RNA when clinical testing was negative or in absence of clinical testing (83% for both solid and liquid samples), indicating presence of infected individuals in the schools. Wastewater solids had a higher concentration of SARS-CoV-2 than wastewater liquids on an equivalent mass basis by three orders of magnitude.

Wastewater monitoring in tourist cities as potential sentinel sites for near real-time dynamics of imported SARS-CoV-2 variants

Wastewater-based epidemiology (WBE) complements the clinical surveillance of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and its variants' distribution in populations. Many developed nations have established national and regional WBE systems; however, governance and budget constraints could be obstacles for low- and middle-income countries. An urgent need thus exists to identify hotspots to serve as sentinel sites for WBE. We hypothesized that representative wastewater treatment plants (WWTPs) in two international gateway cities, Bangkok and Phuket, Thailand, could be sentineled for SARS-CoV-2 and its variants to reflect the clinical distribution patterns at city level and serve as early indicators of new variants entering the country. Municipal wastewater samples (n = 132) were collected from eight representative municipal WWTPs in Bangkok and Phuket during 19 sampling events from October 2021 to March 2022, which were tested by reverse transcription-quantitative polymerase chain reaction (RT-qPCR) using the US CDC N1 and N2 multiplex and variant (Alpha, Delta, and Omicron BA.1 and BA.2) singleplex assays. The variant detection ratios from Bangkok and Phuket followed similar trends to the national clinical testing data, and each variant's viral loads agreed with the daily new cases (3-d moving average). Omicron BA.1 was detected in Phuket wastewater prior to Bangkok, possibly due to Phuket's WWTPs serving tourist communities. We found that the Omicron BA.1 and BA.2 viral loads predominantly drove the SARS-CoV-2 resurgence. We also noted a shifting pattern in the Bangkok WBE from a 22-d early warning in early 2021 to a near real-time pattern in late 2021. The potential application of tourist hotspots for WBE to indicate the arrival of new variants and re-emerging or unprecedented infectious agents could support tourism-dependent economies by complementing the reduced clinical regulations while maintaining public health protection via wastewater surveillance.

Development of quantitative wastewater surveillance models facilitated the precise epidemic management of COVID-19

Wastewater surveillance serves as a promising approach to elucidate the silent transmission of SARS-CoV-2 in communities. To understand the decay of the coronavirus in sewage pipes, the decay of the coronavirus traveling over 20 km distance of pipeline was analyzed. Based on the decay model, a WWTP and a community model were then proposed for predicting COVID-19 cases in Xi'an and Nanchang city during the COVID-19 outbreak in 2021 and 2022. The results suggested that Monte Carol simulations estimated 23.3, 50.1, 127.3 and 524.2 infected persons in the Yanta district of Xi'an city on December 14th, 18th, 22nd and 26th of 2021, respectively, which is largely consistent with the clinical reports. Next, we further conducted wastewater surveillance in two WWTPs that covered the whole metropolitan region in Nanchang to validate the robustness of the WWTP model from December 2021 to April 2022. SARS-CoV-2 signals were detected in two WWTPs from March 15th to April 5th. Predicted infection numbers were in agreement with the actual infection cases, which promoted precise epidemic control. Finally, community wastewater surveillance was conducted for 40 communities that were not 100 % covered by massive nucleic acid testing in Nanchang city, which accurately identified the SARS-CoV-2 carriers not detected by massive nucleic acid testing. In conclusion, accurate prediction of COVID-19 cases based on WWTP and community models promoted precise epidemic control. This work highlights the viability of wastewater surveillance for outbreak evaluation and identification of hidden cases, which provides an extraordinary example for implementing precise epidemic control of COVID-19.

Does normalization of SARS-CoV-2 concentrations by Pepper Mild Mottle Virus improve correlations and lead time between wastewater surveillance and clinical data in Alberta (Canada): comparing twelve SARS-CoV-2 normalization approaches

Wastewater-based surveillance (WBS) data normalization is an analyte measurement correction that addresses variations resulting from dilution of fecal discharge by non-sanitary sewage, stormwater or groundwater infiltration. No consensus exists on what WBS normalization parameters result in the strongest correlations and lead time between SARS-CoV-2 WBS data and COVID-19 cases. This study compared flow, population size and biomarker normalization impacts on the correlations and lead times for ten communities in twelve sewersheds in Alberta (Canada) between September 2020 and October 2021 (n = 1024) to determine if normalization by Pepper Mild Mottle Virus (PMMoV) provides any advantages compared to other normalization parameters (e.g., flow, reported and dynamic population sizes, BOD, TSS, NH3, TP). PMMoV concentrations (GC/mL) corresponded with plant influent flows and were highest in the urban centres. SARS-CoV-2 target genes E, N1 and N2 were all negatively associated with wastewater influent pH, while PMMoV was positively associated with temperature. Pooled data analysis showed that normalization increased ρ-values by almost 0.1 and was highest for ammonia, TKN and TP followed by PMMoV. Normalization by other parameters weakened associations. None of the differences were statistically significant. Site-specific correlations showed that normalization of SARS-CoV-2 data by PMMoV only improved correlations significantly in two of the twelve systems; neither were large sewersheds or combined sewer systems. In five systems, normalization by traditional wastewater strength parameters and dynamic population estimates improved correlations. Lead time ranged between 1 and 4 days in both pooled and site-specific comparisons. We recommend that WBS researchers and health departments: a) Investigate WWTP influent properties (e.g., pH) in the WBS planning phase and use at least two parallel approaches for normalization only if shown to provide value; b) Explore normalization by wastewater strength parameters and dynamic population size estimates further; and c) Evaluate purchasing an influent flow meter in small communities to support long-term WBS efforts and WWTP management.

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.

Temporal Variation of SARS-CoV-2 Levels in Wastewater from a Meat Processing Plant

Wastewater-based surveillance (WBS) on SARS-CoV-2 has been proved to be an effective approach to estimate the prevalence of COVID-19 in communities and cities. However, its application was overlooked at smaller scale, such as a single facility. Meat processing plants are hotspots for COVID-19 outbreaks due to their unique environment that are favorable for the survival and persistence of SARS-CoV-2. This is the first known WBS study in meat processing plants. The goal was to understand the temporal variation of the SARS-CoV-2 levels in wastewater from a meat processing plant in Canada during a three-month campaign and to find any correlation with clinically confirmed cases in the surrounding city area. Higher SARS-CoV-2 concentrations and detection frequencies were observed in the solid fraction compared to the liquid fraction of the wastewater. The viruses can be preserved in the solid fraction of wastewater for up to 12 days. The wastewater virus level did not correlate to the city-wide COVID-19 cases due to the unmatching scales. WBS on SARS-CoV-2 in meat processing plants can be useful for identifying COVID-19 outbreaks in the facility and serve as an effective alternative when resources for routine individual testing are not available.

Microbial Water Quality through a Full-Scale Advanced Wastewater Treatment Demonstration Facility

The fates of viruses, bacteria, and antibiotic resistance genes during advanced wastewater treatment are important to assess for implementation of potable reuse systems. Here, a full-scale advanced wastewater treatment demonstration facility (ozone, biological activated carbon filtration, micro/ultrafiltration, reverse osmosis, and advanced oxidation) was sampled over three months. Atypically, no disinfectant residual was applied before the microfiltration step. Microbial cell concentrations and viability were assessed via flow cytometry and adenosine triphosphate (ATP). Concentrations of bacteria (16S rRNA gene), viruses (human adenovirus and JC polyomavirus), and antibiotic resistance genes (sul1 and bla _TEM ) were assessed via quantitative PCR following the concentration of large sample volumes by dead-end ultrafiltration. In all membrane filtration permeates, microbial concentrations were higher than previously reported for chloraminated membranes, and log₁₀ reduction values were lower than expected. Concentrations of 16S rRNA and sul1 genes were reduced by treatment but remained quantifiable in reverse osmosis permeate. It is unclear whether sul1 in the RO permeate was from the passage of resistance genes or new growth of microorganisms, but the concentrations were on the low end of those reported for conventional drinking water distribution systems. Adenovirus, JC polyomavirus, and bla _TEM genes were reduced below the limit of detection (∼10^-2 gene copies per mL) by microfiltration. The results provide insights into how treatment train design and operation choices affect microbial water quality as well as the use of flow cytometry and ATP for online monitoring and process control.

Wastewater-Based Epidemiology for SARS-CoV-2 Biomarkers: Evaluation of Normalization Methods in Small and Large Communities in Southern Germany

In the context of the COVID-19 pandemic, wastewater-based epidemiology (WBE) emerged as a useful tool to account for the prevalence of SARS-CoV-2 infections on a population scale. In this study, we analyzed wastewater samples from three large (>300,000 people served) and four small (<25,000 people served) communities throughout southern Germany from August to December 2021, capturing the fourth infection wave in Germany dominated by the Delta variant (B.1.617.2). As dilution can skew the SARS-CoV-2 biomarker concentrations in wastewater, normalization to wastewater parameters can improve the relationship between SARS-CoV-2 biomarker data and clinical prevalence data. In this study, we investigated the suitability and performance of various normalization parameters. Influent flow data showed strong relationships to precipitation data; accordingly, flow-normalization reacted distinctly to precipitation events. Normalization by surrogate viruses CrAssphage and pepper mild mottle virus showed varying performance for different sampling sites. The best normalization performance was achieved with a mixed fecal indicator calculated from both surrogate viruses. Analyzing the temporal and spatial variation of normalization parameters proved to be useful to explain normalization performance. Overall, our findings indicate that the performance of surrogate viruses, flow, and hydro-chemical data is site-specific. We recommend testing the suitability of normalization parameters individually for specific sewage systems.

Looking Forward: The Role of Academic Researchers in Building Sustainable Wastewater Surveillance Programs

BACKGROUND

In just over 2 years, tracking the COVID-19 pandemic through wastewater surveillance advanced from early reports of successful SARS-CoV-2 RNA detection in untreated wastewater to implementation of programs in at least 60 countries. Early wastewater monitoring efforts primarily originated in research laboratories and are now transitioning into more formal surveillance programs run in commercial and public health laboratories. A major challenge in this progression has been to simultaneously optimize methods and build scientific consensus while implementing surveillance programs, particularly during the rapidly changing landscape of the pandemic. Translating wastewater surveillance results for effective use by public health agencies also remains a key objective for the field.

OBJECTIVES

We examined the evolution of wastewater surveillance to identify model collaborations and effective partnerships that have created rapid and sustained success. We propose needed areas of research and key roles academic researchers can play in the framework of wastewater surveillance to aid in the transition from early monitoring efforts to more formalized programs within the public health system.

DISCUSSION

Although wastewater surveillance has rapidly developed as a useful public health tool for tracking COVID-19, there remain technical challenges and open scientific questions that academic researchers are equipped to address. This includes validating methodology and backfilling important knowledge gaps, such as fate and transport of surveillance targets and epidemiological links to wastewater concentrations. Our experience in initiating and implementing wastewater surveillance programs in the United States has allowed us to reflect on key barriers and draw useful lessons on how to promote synergy between different areas of expertise. As wastewater surveillance programs are formalized, the working relationships developed between academic researchers, commercial and public health laboratories, and data users should promote knowledge co-development. We believe active involvement of academic researchers will contribute to building robust surveillance programs that will ultimately provide new insights into population health. https://doi.org/10.1289/EHP11519.