Relationships between SARS-CoV-2 in Wastewater and COVID-19 Clinical Cases and Hospitalizations, with and without Normalization against Indicators of Human Waste

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.

Variability of Clinical Metrics in Small Population Communities Drive Perceived Wastewater and Environmental Surveillance Data Quality: Ontario, Canada-Wide Study

The emergence of COVID-19 in Canada has led to over 4.9 million cases and 59,000 deaths by May 2024. Traditional clinical surveillance metrics (hospital admissions and clinical laboratory-positive cases) were complemented with wastewater and environmental monitoring (WEM) to monitor SARS-CoV-2 incidence. However, challenges in public health integration of WEM persist due to perceived limitations of WEM data quality, potentially driving inconsistent correlations variability and lead times. This study investigates how factors like population size, WEM measurement magnitude, site isolation status, hospital admissions, and clinical laboratory-positive cases affect WEM data correlations and variability in Ontario. The analysis uncovers a direct relationship between clinical surveillance data and the population size of the surveyed sewersheds, while WEM measurement magnitude was not directly impacted by population size. Higher variability in clinical surveillance data was observed in smaller sewersheds, likely reducing correlation strength for inferring COVID-19 incidence. Population size significantly influenced correlation quality, with thresholds identified at ∼66,000 inhabitants for strong WEM-hospital admissions correlations and ∼68,000 inhabitants for WEM-laboratory-positive cases during waned vaccination periods in Ontario (the Omicron BA.1 wave). During significant vaccination immunization (the Omicron BA.2 wave), these thresholds increased to ∼187,000 and 238,000, respectively. These findings highlight the benefit of WEM for strategic public health monitoring and interventions, especially in smaller communities. This study provides insights for enhancing public health decision making and disease monitoring through WEM, applicable to COVID-19 and potentially other diseases.

Novel Workflows for Separate Isolation of Pathogen RNA or DNA from Wastewater: Detection by Innovative and Conventional qPCR

Wastewater-based surveillance (WBS) can provide a wealth of information regarding the health status of communities from measurements of nucleic acids found in wastewater. Processing workflows for WBS typically include sample collection, a primary concentration step, and lysis of the microbes to release nucleic acids, followed by nucleic acid purification and molecular-based quantification. This manuscript provides workflows from beginning to end with an emphasis on filtration-based concentration approaches coupled with specific lysis and nucleic acid extraction processes. Here, two WBS processing approaches are presented, one focusing on RNA-specific pathogens and the other focused on DNA-specific pathogens found within wastewater: 1) The RNA-specific approach, employed for analyzing RNA viruses like severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) couples electronegative filtration of wastewater with the placement of the filter within a lysis buffer followed by direct RNA extraction. 2) The DNA-specific approach, employed for analyzing DNA pathogens like Candida auris, uses size selection membranes during filtration, subsequently followed by a lysis buffer, bead-beating, and DNA extraction. Separate workflows for RNA versus DNA isolations have the advantage of improving the detection of the target pathogen. A novel aspect of the RNA-specific workflow is the direct extraction of nucleic acids from filter lysates, which shows enhanced recoveries, whereas the DNA-specific approach requires bead beating prior to extraction. Novelty is also provided in a new qPCR approach called Volcano 2nd Generation (V2G), which uses a polymerase capable of using RNA as a template, bypassing the reverse transcriptase step normally required for qPCR. Key features • Membrane filtration approaches for concentrating suspended solids from wastewater. After concentration, workflows are optimized for separate recovery of RNA and DNA. • Unique polymerase utilized to perform qPCR analysis, foregoing reverse transcription, for RNA. • Sample products for use with other molecular techniques (e.g., sequencing) as workflow approaches generate high-quality, concentrated nucleic acid extracts with minimal inhibitors. • Validated through COVID-19 surveillance where >1,000 samples of wastewater and >3,000 filter concentrates produced from these samples have been created and analyzed, with published results. This complete protocol was used in: J Biomol Tech (2023), DOI: 10.7171/3fc1f5fe.dfa8d906.

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.

Wastewater Based Measures of COVID-19 and Associations with Children's Absenteeism at Grade Schools

During the COVID-19 pandemic schools closed due to concerns over disease spread resulting in lost learning opportunities. One approach for documenting disease spread includes wastewater (WW) surveillance of the virus that causes COVID-19 (Severe Acute Respiratory Syndrome Coronavirus 2, SARS-CoV-2) and other infectious pathogens. The objective of this study was to evaluate whether wastewater can be used to track children's health at grade schools in an underserved community, which was vulnerable due to limited health-based data and difficulties in implementing mitigation measures. The 18-month study was initiated during January 2022 at 9 grade schools (3 high, 2 middle, and 4 elementary schools) characterized as low income. Children's health was evaluated through absenteeism due to difficulties in attaining representative clinical diagnoses through school-based clinics. Wastewater measurements of SARS-CoV-2 were available weekly through grab sample collection and RNA extraction followed by quantification using qPCR. The average absenteeism rate was 7.1%, ranging from 4.6% to 12.5% per school. Fraction of WW samples positive for SARS-CoV-2 was 38% with SARS-Cov-2 levels ranging from detection limits (100 gc/L) to a maximum of 10.2 million gc/L. When data were aggregated across all schools, a statistically significant association was observed between weekly absenteeism rates and WW SARS-CoV-2 with a one percent increase in the loge WW SARS-CoV-2 associated with a 1.4% increase in student absence (p < 0.05). When evaluating the data by individual school, this association was strongest at schools with enclosed architecture characterized by limited natural ventilation. For schools with limited resources for clinical diagnosis of illnesses, school absenteeism coupled with wastewater-based monitoring should be utilized for assessing overall health of student populations. Strategies to maintain schools open during pandemics should include consideration of school architecture along with appropriate messaging of WW monitoring results to inform administrators and families.

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.

Morbidity of SARS-CoV-2 in the evolution to endemicity and in comparison with influenza

BACKGROUND

There are three possible SARS-CoV-2 post-pandemic scenarios: (i) ongoing severity, (ii) influenza-like severity, and (iii) a transition to an endemic disease with lesser morbidity similar to that of other human coronaviruses.

METHODS

To assess a possible evolution of the pandemic under the three scenarios, we use data from the US National Covid Cohort Collaborative, CDC COVID-NET, and CDC Fluview and from the WastewaterSCAN Dashboard. We include influenza disease and treatment response as benchmark. The US National Covid Cohort Collaborative allows the quantification of viral-specific morbidity using electronic health records from 424,165 SARS-CoV-2 cases, 53,846 influenza cases, and 199,971 uninfected control subjects from 2021-2022. Evolution of hospitalization rates is estimated from the correlation between national SARS-CoV-2 and influenza hospitalization data and viral gene copies in wastewater.

RESULTS

Our findings reveal that medically attended SARS-CoV-2 infections exhibit similar morbidity to influenza [indicative of scenario (ii)], but SARS-CoV-2 hospitalization rates are one order of magnitude lower than influenza when considering virus concentration in wastewater [indicative of scenario (iii)]. Moreover, SARS-CoV-2 displays a more favorable response to antiviral therapy.

CONCLUSIONS

Our analysis confirms a rapid decline in SARS-CoV-2 morbidity as it transitions to an endemic state.

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.

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.

Piloting wastewater-based surveillance of norovirus in England

Wastewater-based epidemiology (WBE) gained widespread use as a tool for supporting clinical disease surveillance during the COVID-19 pandemic. There is now significant interest in the continued development of WBE for other pathogens of clinical significance. In this study, approximately 3,200 samples of wastewater from across England, previously collected for quantification of SARS-CoV-2, were re-analysed for the quantification of norovirus genogroup I (GI) and II (GII). Overall, GI and GII were detected in 93% and 98% of samples respectively, and at least one of the genogroups was detected in 99% of samples. GI was found at significantly lower concentrations than GII, but the proportion of each genogroup varied over time, with GI becoming more prevalent than GII in some areas towards the end of the study period (May 2021 - March 2022). Using relative strength indices (RSI), it was possible to study the trends of each genogroup, and total norovirus over time. Increases in norovirus levels appeared to coincide with the removal of COVID-19 related lockdown restrictions within England. Local Moran's I analyses indicated several localised outbreaks of both GI and GII across England, notably the possible GI outbreak in the north of England in early 2022. Comparisons of national average norovirus concentrations in wastewater against concomitant norovirus reported case numbers showed a significant linear relationship. This highlights the potential for wastewater-based monitoring of norovirus as a valuable approach to support surveillance of norovirus in communities.

Towards geospatially-resolved public-health surveillance via wastewater sequencing

Wastewater is a geospatially- and temporally-linked microbial fingerprint of a given population, making it a potentially valuable tool for tracking public health across locales and time. Here, we integrate targeted and bulk RNA sequencing (N = 2238 samples) to track the viral, bacterial, and functional content over geospatially distinct areas within Miami Dade County, USA, from 2020-2022. We used targeted amplicon sequencing to track diverse SARS-CoV-2 variants across space and time, and we found a tight correspondence with positive PCR tests from University students and Miami-Dade hospital patients. Additionally, in bulk metatranscriptomic data, we demonstrate that the bacterial content of different wastewater sampling locations serving small population sizes can be used to detect putative, host-derived microorganisms that themselves have known associations with human health and diet. We also detect multiple enteric pathogens (e.g., Norovirus) and characterize viral diversity across sites. Moreover, we observed an enrichment of antimicrobial resistance genes (ARGs) in hospital wastewater; antibiotic-specific ARGs correlated to total prescriptions of those same antibiotics (e.g Ampicillin, Gentamicin). Overall, this effort lays the groundwork for systematic characterization of wastewater that can potentially influence public health decision-making.

Evaluation of a field deployable, high-throughput RT-LAMP device as an early warning system for COVID-19 through SARS-CoV-2 measurements in wastewater

Quantification of SARS-CoV-2 RNA copies in wastewater can be used to estimate COVID-19 prevalence in communities. While such results are important for mitigating disease spread, SARS-CoV-2 measurements require sophisticated equipment and trained personnel, for which a centralized laboratory is necessary. This significantly impacts the time to result, defeating its purpose as an early warning detection tool. The objective of this study was to evaluate a field portable device (called MINI) for detecting SARS-CoV-2 viral loads in wastewater using real-time reverse transcriptase loop-mediated isothermal amplification (real-time RT-LAMP). The device was tested using wastewater samples collected from buildings (with 430 to 1430 inhabitants) that had known COVID-19-positive cases. Results show comparable performance of RT-LAMP against reverse transcriptase polymerase chain reaction (RT-qPCR) when detecting SARS-CoV-2 copies in wastewater. Both RT-LAMP and RT-qPCR detected SARS-CoV-2 in wastewater from buildings with at least three positive individuals within a 6-day time frame prior to diagnosis. The large 96-well throughput provided by MINI provided scalability to multi-building detection. The portability of the MINI device enabled decentralized on-site detection, significantly reducing the time to result. The overall findings support the use of RT-LAMP within the MINI configuration as an early detection system for COVID-19 infection using wastewater collected at the building scale.

Simultaneous biomass concentration and subsequent quantitation of multiple infectious disease agents and antimicrobial resistance genes from community wastewater

Wastewater-based surveillance (WBS) of infectious disease agents is increasingly seen as a reliable source of population health data. To date, wastewater-based surveillance efforts have largely focused on individual pathogens. However, given that wastewater contains a broad range of pathogens circulating in the population, a more comprehensive approach could enhance its usability. We focused on the simultaneous detection of SARS-CoV-2, sapovirus, Campylobacter jejuni, Campylobacter coli, Salmonella spp., pathogenic Escherichia coli, Cryptosporidium spp., Giardia spp. and antimicrobial resistance genes (ARGs) of clinical relevance. To achieve this goal, biomass concentration and nucleic acid extraction methods were optimized, and samples were analyzed by using a set of (RT)-qPCR and (HT)-qPCR methods. We determined the prevalence and the spatial and temporal trends of the targeted pathogens and collected novel information on ARGs in Finnish wastewater. In addition, the use of different wastewater concentrates, namely the ultrafiltered concentrate of the supernatant and the centrifuged pellet, and the effect of freezing and thawing wastewater prior to sample processing were investigated with the indicator microbe crAssphage. Freeze-thawing of wastewater decreased the gene copy count of crAssphage in comparison to analyzing fresh samples (p < 0.001). Campylobacters were most abundant in two of the four studied summer months (30 % detection rate) and in wastewaters from regions with intensive animal farming. Salmonella, however, was detected in 40 % of the samples without any clear seasonal trends, and the highest gene copy numbers were recorded from the largest wastewater treatment plants. Beta-lactamase resistance genes that have commonly been detected in bacteria isolated from humans in Finland, namely blaCTX-M, blaOXA48, blaNDM, and blaKPC, were also frequently detected in wastewaters (100, 98, 98, and 70 % detection rates, respectively). These results confirm the reliability of using wastewater in public health surveillance and demonstrate the possibility to simultaneously perform WBS of multiple pathogens.

SARS-CoV-2 RNA Detection in Wastewater and Its Effective Correlation with Clinical Data during the Outbreak of COVID-19 in Salamanca

Wastewater treatment plants (WWTPs) are the final stage of the anthropogenic water cycle where a wide range of chemical and biological markers of human activity can be found. In COVID-19 disease contexts, wastewater surveillance has been used to infer community trends based on viral abundance and SARS-CoV-2 RNA variant composition, which has served to anticipate and establish appropriate protocols to prevent potential viral outbreaks. Numerous studies worldwide have provided reliable and robust tools to detect and quantify SARS-CoV-2 RNA in wastewater, although due to the high dilution and degradation rate of the viral RNA in such samples, the detection limit of the pathogen has been a bottleneck for the proposed protocols so far. The current work provides a comprehensive and systematic study of the different parameters that may affect the detection of SARS-CoV-2 RNA in wastewater and hinder its quantification. The results obtained using synthetic viral RNA as a template allow us to consider that 10 genome copies per µL is the minimum RNA concentration that provides reliable and consistent values for the quantification of SARS-CoV-2 RNA. RT-qPCR analysis of wastewater samples collected at the WWTP in Salamanca (western Spain) and at six pumping stations in the city showed that below this threshold, positive results must be confirmed by sequencing to identify the specific viral sequence. This allowed us to find correlations between the SARS-CoV-2 RNA levels found in wastewater and the COVID-19 clinical data reported by health authorities. The close match between environmental and clinical data from the Salamanca case study has been confirmed by similar experimental approaches in four other cities in the same region. The present methodological approach reinforces the usefulness of wastewater-based epidemiology (WBE) studies in the face of future pandemic outbreaks.

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.

Simultaneous detection of influenza A, B and respiratory syncytial virus in wastewater samples by one-step multiplex RT-ddPCR assay

BACKGROUND

After the occurrence of the COVID-19 pandemic, detection of other disseminated respiratory viruses using highly sensitive molecular methods was declared essential for monitoring the spread of health-threatening viruses in communities. The development of multiplex molecular assays are essential for the simultaneous detection of such viruses even at low concentrations. In the present study, a highly sensitive and specific multiplex one-step droplet digital PCR (RT-ddPCR) assay was developed for the simultaneous detection and absolute quantification of influenza A (IAV), influenza B (IBV), respiratory syncytial virus (RSV), and beta-2-microglobulin transcript as an endogenous internal control (IC B2M).

RESULTS

The assay was first evaluated for analytical sensitivity and specificity, linearity, reproducibility, and recovery rates with excellent performance characteristics and then applied to 37 wastewater samples previously evaluated with commercially available and in-house quantitative real-time reverse transcription PCR (RT-qPCR) assays. IAV was detected in 16/37 (43%), IBV in 19/37 (51%), and RSV in 10/37 (27%) of the wastewater samples. Direct comparison of the developed assay with real-time RT-qPCR assays showed statistically significant high agreement in the detection of IAV (kappa Cohen's correlation coefficient: 0.834, p = 0.001) and RSV (kappa: 0.773, p = 0.001) viruses between the two assays, while the results for the detection of IBV (kappa: 0.355, p = 0.27) showed good agreement without statistical significance.

CONCLUSIONS

Overall, the developed one-step multiplex ddPCR assay is cost-effective, highly sensitive and specific, and can simultaneously detect three common respiratory viruses in the complex matrix of wastewater samples even at low concentrations. Due to its high sensitivity and resistance to PCR inhibitors, the developed assay could be further used as an early warning system for wastewater monitoring.

The role of catchment population size, data normalization, and chronology of public health interventions on wastewater-based COVID-19 viral trends

The severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) pandemic presented the most challenging global crisis in recent times. A pandemic caused by a novel pathogen such as SARS-CoV-2 necessitated the development of innovative techniques for the monitoring and surveillance of COVID-19 infections within communities. Wastewater surveillance (WWS) is recognized as a non-invasive, cost-effective, and valuable epidemiological tool to monitor the prevalence of COVID-19 infections in communities. Seven municipal wastewater sampling sites representing distinct sewershed communities were selected for the surveillance of the SARS-CoV-2 virus in Durham Region, Ontario, Canada over 8 months from March 2021 to October 2021. Viral RNA fragments of SARS-CoV-2 and the normalization target pepper mild mottle virus (PMMoV) were concentrated from wastewater influent using the PEG/NaCl superspeed centrifugation method and quantified using RT-qPCR. Strong significant correlations (Spearman's rs = 0.749 to 0.862, P < 0.001) were observed between SARS-CoV-2 gene copies/mL of wastewater and clinical cases reported in each delineated sewershed by onset date. Although raw wastewater offered higher correlation coefficients with clinical cases by onset date compared to PMMoV normalized data, only one site had a statistically significantly higher Spearman's correlation coefficient value for raw data than normalized data. Implementation of community stay-at-home orders and vaccinations over the course of the study period in 2021 were found to strongly correspond to decreasing SARS-CoV-2 wastewater trends in the wastewater treatment plants and upstream pumping stations.

Variations in the persistence of 5'-end genomic and subgenomic SARS-CoV-2 RNAs in wastewater from aircraft, airports and wastewater treatment plants

Wastewater sequencing has become a powerful supplement to clinical testing in monitoring SARS-CoV-2 infections in the post-COVID-19 pandemic era. While its applications in measuring the viral burden and main circulating lineages in the community have proved their efficacy, the variations in sequencing quality and coverage across the different regions of the SARS-CoV-2 genome are not well understood. Furthermore, it is unclear how different sample origins, viral extraction and concentration methods and environmental factors impact the reads sequenced from wastewater. Using high-coverage, amplicon-based, paired-end read sequencing of viral RNA extracted from wastewater collected directly from aircraft, pooled from different aircraft and airport buildings or from regular wastewater plants, we assessed the genome coverage across the sample groups with a focus on the 5'-end region covering the leader sequence and investigated whether it was possible to detect subgenomic RNA from viral material recovered from wastewater. We identified distinct patterns in the persistence of the different genomic regions across the different types of wastewaters and the existence of chimeric reads mapping to non-amplified regions. Our findings suggest that preservation of the 5'-end of the genome and the ability to detect subgenomic RNA reads, though highly susceptible to environment and sample processing conditions, may be indicative of the quality and amount of the viral RNA present in wastewater.

SARS-CoV-2 wastewater surveillance in the Czech Republic: Spatial and temporal differences in SARS-CoV-2 RNA concentrations and relationship to clinical data and wastewater parameters

This study presents the results of systematic wastewater monitoring of SARS-CoV-2 RNA and basic wastewater parameters from four different wastewater treatment plants (WWTPs) in the Czech Republic over the 2020-2022 epidemic. Two-step reverse-transcription quantitative PCR targeting genes encoding the N and Nsp12 proteins was employed to detect SARS-CoV-2 RNA loading in 420 wastewater samples. The results obtained were used to evaluate the potential of wastewater analysis for describing the epidemiological situation in cities of different sizes and determining temporal differences based on the prevailing SARS-CoV-2 variant. Strong correlations between the number of active and hospitalised COVID-19 cases in each WWTP catchment area and the concentration of SARS-CoV-2 RNA detected in the wastewater clearly demonstrated the suitability of this wastewater-based epidemiological approach for WWTPs of different sizes and characteristics, despite differences in SARS-CoV-2 variant waves, with some WWTPs showing high predictive potential. This study demonstrated on the data from the Czech Republic that targeted systematic monitoring of wastewater provides sufficiently robust data for surveillance of viral loads in sample populations, and thus contributes to preventing the spread of infection and subsequent introduction of appropriate measures.

Testing methods to estimate population size for wastewater treatment plants using census data: Implications for wastewater-based epidemiology

In wastewater-based epidemiology (WBE), wastewater loads are commonly reported as a per capita value. Census population counts are often used to obtain a population size to normalise wastewater loads. However, the methods used to calculate the population size of wastewater treatment plants (WWTPs) from census data are rarely reported in the WBE literature. This is problematic because the geographical extents of wastewater catchments and census area units rarely align perfectly with each other and exist at different spatial scales. This complicates efforts to estimate the number of people serviced by WWTPs in these census area units. This study compared four geospatial methods to combine wastewater catchment areas and census area units to calculate the census population size of wastewater treatment plants. These methods were applied nationally to WWTPs across New Zealand. Population estimates varied by up to 73 % between the methods, which could skew comparisons of per capita wastewater loads between sites. Variability in population estimates (relative standard deviation, RSD) was significantly higher in smaller catchments (rs = -0.727, P < .001), highlighting the importance of method selection in smaller sites. Census population estimates were broadly similar to those provided by wastewater operators, but significant variation was observed for some sites (ranging from 42 % lower to 78 % higher, RSD = 262 %). We present a widely applicable method to calculate population size from census, which involves disaggregating census area units by individual properties. The results reinforce the need for transparent reporting to maintain confidence in the comparison of WBE across sites and studies.

Detection of SARS-CoV-2 and Omicron variant RNA in wastewater samples from Manila, Philippines

Manila, a highly urbanized city, is listed as one of the top cities with the highest recorded number of coronavirus disease 2019 (COVID-19) cases in the Philippines. This study aimed to detect and quantify the RNA of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and the Omicron variant in 51 wastewater samples collected from three locations in Manila, namely Estero de Santa Clara, Estero de Pandacan, which are open drainages, and a sewage treatment plant (STP) at De La Salle University-Manila, between July 2022 and February 2023. Using one-step reverse transcription-quantitative polymerase chain reaction, SARS-CoV-2 and Omicron variant RNA were detected in 78 % (40/51; 4.9 ± 0.5 log10 copies/L) and 60 % (24/40; 4.4 ± 0.3 log10 copies/L) of wastewater samples collected from all sampling sites, respectively. SARS-CoV-2 RNA was detected frequently at Estero de Santa Clara (88 %, 15/17); its highest concentration was at the STP (6.3 log10 copies/L). The Omicron variant RNA was present in the samples collected (4.4 ± 0.3 log10 copies/L) from all sampling sites, with the highest concentration at the STP (4.9 log10 copies/L). Regardless of normalization, using concentrations of pepper mild mottle virus RNA, SARS-CoV-2 RNA concentrations exhibited the highest positive correlation with COVID-19 reported cases in Manila 5 days after the clinical report. These findings revealed that wastewater-based epidemiology may aid in identifying and monitoring of the presence of pathogens in open drainages and STPs in the Philippines. This paper provides the first documentation on SARS-CoV-2 and the Omicron variant in wastewater from Manila.

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 can be used to reduce clinical testing intensity on a university campus

Clinical testing has been a vital part of the response to and suppression of the COVID-19 pandemic; however, testing imposes significant burdens on a population. College students had to contend with clinical testing while simultaneously dealing with health risks and the academic pressures brought on by quarantines, changes to virtual platforms, and other disruptions to daily life. The objective of this study was to analyze whether wastewater surveillance can be used to decrease the intensity of clinical testing while maintaining reliable measurements of diseases incidence on campus. Twelve months of human health and wastewater surveillance data for eight residential buildings on a university campus were analyzed to establish how SARS-CoV-2 levels in the wastewater can be used to minimize clinical testing burden on students. Wastewater SARS-CoV-2 levels were used to create multiple scenarios, each with differing levels of testing intensity, which were compared to the actual testing volumes implemented by the university. We found that scenarios in which testing intensity fluctuations matched rise and falls in SARS-CoV-2 wastewater levels had stronger correlations between SARS-CoV-2 levels and recorded clinical positives. In addition to stronger correlations, most scenarios resulted in overall fewer weekly clinical tests performed. We suggest the use of wastewater surveillance to guide COVID-19 testing as it can significantly increase the efficacy of COVID-19 surveillance while reducing the burden placed on college students during a pandemic. Future efforts should be made to integrate wastewater surveillance into clinical testing strategies implemented on college campuses.

Critical Needs for Integrated Surveillance: Wastewater-Based and Clinical Epidemiology in Evolving Scenarios with Lessons Learned from SARS-CoV-2

During the COVID-19 pandemic, wastewater-based epidemiology (WBE) and clinical surveillance have been used as tools for analyzing the circulation of SARS-CoV-2 in the community, but both approaches can be strongly influenced by some sources of variability. From the challenging perspective of integrating environmental and clinical data, we performed a correlation analysis between SARS-CoV-2 concentrations in raw sewage and incident COVID-19 cases in areas served by medium-size wastewater treatment plants (WWTPs) from 2021 to 2023. To this aim, both datasets were adjusted for several sources of variability: WBE data were adjusted for factors including the analytical protocol, sewage flow, and population size, while clinical data adjustments considered the demographic composition of the served population. Then, we addressed the impact on the correlation of differences among sewerage networks and variations in the frequency and type of swab tests due to changes in political and regulatory scenarios. Wastewater and clinical data were significantly correlated when restrictive containment measures and limited movements were in effect (ρ = 0.50) and when COVID-19 cases were confirmed exclusively through molecular testing (ρ = 0.49). Moreover, a positive (although weak) correlation arose for WWTPs located in densely populated areas (ρ = 0.37) and with shorter sewerage lengths (ρ = 0.28). This study provides methodological approaches for interpreting WBE and clinical surveillance data, which could also be useful for other infections. Data adjustments and evaluation of possible sources of bias need to be carefully considered from the perspective of integrated environmental and clinical surveillance of infections.

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.

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.

Wastewater-based Epidemiology and SARS-CoV-2: Variant Trends in the Apulia Region (Southern Italy) and Effect of Some Environmental Parameters

During the COVID-19 pandemic, wastewater monitoring has been used to monitor the levels of SARS-CoV-2 RNA entering the sewerage system. In Italy, the Istituto Superiore di Sanità coordinated the SARI project (Sorveglianza Ambientale Reflue in Italia) to detect SARS-CoV-2 and its variants. In this study, the concentration of SARS-CoV-2 and its variants in raw wastewater against COVID-19 cases was evaluated together with the effect of temperature and precipitation on virus spread. We validated a predictive model, proposed by De Giglio et al., 2021, to establish the number of COVID-19 cases/100,000 inhabitants. A receiver operating characteristic curve model was applied to predict the number of COVID-19 cases and Poisson regression was applied to study the effect of temperature and rainfall on viral load. In Apulia, from October 2021 to December 2022, we analyzed 1041 samples, of which 985 (94.6%) tested positive for SARS-CoV-2. Median atmospheric temperature was inversely proportional to viral load in wastewater; no correlation was found with precipitation. The predictive model confirmed that at least 11 cases/100,000 inhabitants would occur in the 15 days following the detection of the virus in wastewater. Environmental surveillance of SARS-CoV-2 can be used to map the virus and its variants.

Expanding a Wastewater-Based Surveillance Methodology for DNA Isolation from a Workflow Optimized for SARS-CoV-2 RNA Quantification

Wastewater-based surveillance (WBS) is a noninvasive, epidemiological strategy for assessing the spread of COVID-19 in communities. This strategy was based upon wastewater RNA measurements of the viral target, severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2). The utility of WBS for assessing the spread of COVID-19 has motivated research to measure targets beyond SARS-CoV-2, including pathogens containing DNA. The objective of this study was to establish the necessary steps for isolating DNA from wastewater by modifying a long-standing RNA-specific extraction workflow optimized for SARS-CoV-2 detection. Modifications were made to the sample concentration process and included an evaluation of bead bashing prior to the extraction of either DNA or RNA. Results showed that bead bashing reduced detection of RNA from wastewater but improved recovery of DNA as assessed by quantitative polymerase chain reaction (qPCR). Bead bashing is therefore not recommended for the quantification of RNA viruses using qPCR. Whereas for Mycobacterium bacterial DNA isolation, bead bashing was necessary for improving qPCR quantification. Overall, we recommend 2 separate workflows, one for RNA viruses that does not include bead bashing and one for other microbes that use bead bashing for DNA isolation. The experimentation done here shows that current-standing WBS program methodologies optimized for SARS-CoV-2 need to be modified and reoptimized to allow for alternative pathogens to be readily detected and monitored, expanding its utility as a tool for public health assessment.

Evaluating various composite sampling modes for detecting pathogenic SARS-CoV-2 virus in raw sewage

Inadequate sampling approaches to wastewater analyses can introduce biases, leading to inaccurate results such as false negatives and significant over- or underestimation of average daily viral concentrations, due to the sporadic nature of viral input. To address this challenge, we conducted a field trial within the University of Tennessee residence halls, employing different composite sampling modes that encompassed different time intervals (1 h, 2 h, 4 h, 6 h, and 24 h) across various time windows (morning, afternoon, evening, and late-night). Our primary objective was to identify the optimal approach for generating representative composite samples of SARS-CoV-2 from raw wastewater. Utilizing reverse transcription-quantitative polymerase chain reaction, we quantified the levels of SARS-CoV-2 RNA and pepper mild mottle virus (PMMoV) RNA in raw sewage. Our findings consistently demonstrated that PMMoV RNA, an indicator virus of human fecal contamination in water environment, exhibited higher abundance and lower variability compared to pathogenic SARS-CoV-2 RNA. Significantly, both SARS-CoV-2 and PMMoV RNA exhibited greater variability in 1 h individual composite samples throughout the entire sampling period, contrasting with the stability observed in other time-based composite samples. Through a comprehensive analysis of various composite sampling modes using the Quade Nonparametric ANCOVA test with date, PMMoV concentration and site as covariates, we concluded that employing a composite sampler during a focused 6 h morning window for pathogenic SARS-CoV-2 RNA is a pragmatic and cost-effective strategy for achieving representative composite samples within a single day in wastewater-based epidemiology applications. This method has the potential to significantly enhance the accuracy and reliability of data collected at the community level, thereby contributing to more informed public health decision-making during a pandemic.

Wastewater-based surveillance can be used to model COVID-19-associated workforce absenteeism

Wastewater-based surveillance (WBS) of infectious diseases is a powerful tool for understanding community COVID-19 disease burden and informing public health policy. The potential of WBS for understanding COVID-19's impact in non-healthcare settings has not been explored to the same degree. Here we examined how SARS-CoV-2 measured from municipal wastewater treatment plants (WWTPs) correlates with workforce absenteeism. SARS-CoV-2 RNA N1 and N2 were quantified three times per week by RT-qPCR in samples collected at three WWTPs servicing Calgary and surrounding areas, Canada (1.4 million residents) between June 2020 and March 2022. Wastewater trends were compared to workforce absenteeism using data from the largest employer in the city (>15,000 staff). Absences were classified as being COVID-19-related, COVID-19-confirmed, and unrelated to COVID-19. Poisson regression was performed to generate a prediction model for COVID-19 absenteeism based on wastewater data. SARS-CoV-2 RNA was detected in 95.5 % (85/89) of weeks assessed. During this period 6592 COVID-19-related absences (1896 confirmed) and 4524 unrelated absences COVID-19 cases were recorded. A generalized linear regression using a Poisson distribution was performed to predict COVID-19-confirmed absences out of the total number of absent employees using wastewater data as a leading indicator (P < 0.0001). The Poisson regression with wastewater as a one-week leading signal has an Akaike information criterion (AIC) of 858, compared to a null model (excluding wastewater predictor) with an AIC of 1895. The likelihood-ratio test comparing the model with wastewater signal with the null model shows statistical significance (P < 0.0001). We also assessed the variation of predictions when the regression model was applied to new data, with the predicted values and corresponding confidence intervals closely tracking actual absenteeism data. Wastewater-based surveillance has the potential to be used by employers to anticipate workforce requirements and optimize human resource allocation in response to trackable respiratory illnesses like COVID-19.

Detection of the clinically persistent, pathogenic yeast spp. Candida auris from hospital and municipal wastewater in Miami-Dade County, Florida

The use of wastewater-based surveillance (WBS) for detecting pathogens within communities has been growing since the beginning of the COVID-19 pandemic with early efforts investigating severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) RNA in wastewater. Recent efforts have shed light on the utilization of WBS for alternative targets, such as fungal pathogens, like Candida auris, in efforts to expand the technology to assess non-viral targets. The objective of this study was to extend workflows developed for SARS-CoV-2 quantification to evaluate whether C. auris can be recovered from wastewater, inclusive of effluent from a wastewater treatment plant (WWTP) and from a hospital with known numbers of patients colonized with C. auris. Measurements of C. auris in wastewater focused on culture-based methods and quantitative PCR (qPCR). Results showed that C. auris can be cultured from wastewater and that levels detected by qPCR were higher in the hospital wastewater compared to the wastewater from the WWTP, suggesting either dilution or degradation of this pathogenic yeast at downstream collection points. The results from this study illustrate that WBS can extend beyond SARS-CoV-2 monitoring to evaluate additional non-viral pathogenic targets and demonstrates that C. auris isolated from wastewater is competent to replicate in vitro using fungal-specific culture media.

Campus node-based wastewater surveillance enables COVID-19 case localization and confirms lower SARS-CoV-2 burden relative to the surrounding community

Wastewater-based surveillance (WBS) has been established as a powerful tool that can guide health policy at multiple levels of government. However, this approach has not been well assessed at more granular scales, including large work sites such as University campuses. Between August 2021 and April 2022, we explored the occurrence of SARS-CoV-2 RNA in wastewater using qPCR assays from multiple complimentary sewer catchments and residential buildings spanning the University of Calgary's campus and how this compared to levels from the municipal wastewater treatment plant servicing the campus. Real-time contact tracing data was used to evaluate an association between wastewater SARS-CoV-2 burden and clinically confirmed cases and to assess the potential of WBS as a tool for disease monitoring across worksites. Concentrations of wastewater SARS-CoV-2 N1 and N2 RNA varied significantly across six sampling sites - regardless of several normalization strategies - with certain catchments consistently demonstrating values 1-2 orders higher than the others. Relative to clinical cases identified in specific sewersheds, WBS provided one-week leading indicator. Additionally, our comprehensive monitoring strategy enabled an estimation of the total burden of SARS-CoV-2 for the campus per capita, which was significantly lower than the surrounding community (p≤0.001). Allele-specific qPCR assays confirmed that variants across campus were representative of the community at large, and at no time did emerging variants first debut on campus. This study demonstrates how WBS can be efficiently applied to locate hotspots of disease activity at a very granular scale, and predict disease burden across large, complex worksites.

Monitoring influenza and respiratory syncytial virus in wastewater. Beyond COVID-19

Wastewater-based surveillance can be a valuable tool to monitor viral circulation and serve as an early warning system. For respiratory viruses that share similar clinical symptoms, namely SARS-CoV-2, influenza, and respiratory syncytial virus (RSV), identification in wastewater may allow differentiation between seasonal outbreaks and COVID-19 peaks. In this study, to monitor these viruses as well as standard indicators of fecal contamination, a weekly sampling campaign was carried out for 15 months (from September 2021 to November 2022) in two wastewater treatment plants that serve the entire population of Barcelona (Spain). Samples were concentrated by the aluminum hydroxide adsorption-precipitation method and then analyzed by RNA extraction and RT-qPCR. All samples were positive for SARS-CoV-2, while the positivity rates for influenza virus and RSV were significantly lower (10.65 % for influenza A (IAV), 0.82 % for influenza B (IBV), 37.70 % for RSV-A and 34.43 % for RSV-B). Gene copy concentrations of SARS-CoV-2 were often approximately 1 to 2 logarithmic units higher compared to the other respiratory viruses. Clear peaks of IAV H3:N2 in February and March 2022 and RSV in winter 2021 were observed, which matched the chronological incidence of infections recorded in the Catalan Government clinical database. In conclusion, the data obtained from wastewater surveillance provided new information on the abundance of respiratory viruses in the Barcelona area and correlated favorably with clinical data.

Monkeypox viral nucleic acids detected using both DNA and RNA extraction workflows

Molecular methods have been used to detect human pathogens in wastewater with sampling typically performed at wastewater treatment plants (WWTP) and upstream locations within the sewer system. A wastewater-based surveillance (WBS) program was established at the University of Miami (UM) in 2020, which included measurements of SARS-CoV-2 levels in wastewater from its hospital and within the regional WWTP. In addition to the development of a SARS-CoV-2 quantitative PCR (qPCR) assay, qPCR assays to detect other human pathogens of interest were also developed at UM. Here we report on the use of a modified set of reagents published by the CDC to detect nucleic acids of Monkeypox virus (MPXV) which emerged during May of 2022 to become a concern worldwide. Samples collected from the University hospital and from the regional WWTP were processed through DNA and RNA workflows and analyzed by qPCR to detect a segment of the MPXV CrmB gene. Results show positive detections of MPXV nucleic acids in the hospital and wastewater treatment plant wastewater which coincided with clinical cases in the community and mirrored the overall trend of nationwide MPXV cases reported to the CDC. We recommend the expansion of current WBS programs' methods to detect a broader range of pathogens of concern in wastewater and present evidence that viral RNA in human cells infected by a DNA virus can be detected in wastewater.

Correlative analysis of wastewater trends with clinical cases and hospitalizations through five dominant variant waves of COVID-19

Wastewater-based epidemiology (WBE) has been utilized to track community infections of Coronavirus Disease 2019 (COVID-19) by detecting RNA of the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), within samples collected from wastewater. The correlations between community infections and wastewater measurements of the RNA can potentially change as SARS-CoV-2 evolves into new variations by mutating. This study analyzed SARS-CoV-2 RNA, and indicators of human waste in wastewater from two sewersheds of different scales (University of Miami (UM) campus and Miami-Dade County Central District wastewater treatment plant (CDWWTP)) during five internally defined COVID-19 variant dominant periods (Initial, Pre-Delta, Delta, Omicron and Post-Omicron wave). SARS-CoV-2 RNA quantities were compared against COVID-19 clinical cases and hospitalizations to evaluate correlations with wastewater SARS-CoV-2 RNA. Although correlations between documented clinical cases and hospitalizations were high, prevalence for a given wastewater SARS-CoV-2 level varied depending upon the variant analyzed. The correlative relationship was significantly steeper (more cases per level found in wastewater) for the Omicron-dominated period. For hospitalization, the relationships were steepest for the Initial wave, followed by the Delta wave with flatter slopes during all other waves. Overall results were interpreted in the context of SARS-CoV-2 virulence and vaccination rates among the community.

Wastewater-based epidemiology predicts COVID-19-induced weekly new hospital admissions in over 150 USA counties

Although the coronavirus disease (COVID-19) emergency status is easing, the COVID-19 pandemic continues to affect healthcare systems globally. It is crucial to have a reliable and population-wide prediction tool for estimating COVID-19-induced hospital admissions. We evaluated the feasibility of using wastewater-based epidemiology (WBE) to predict COVID-19-induced weekly new hospitalizations in 159 counties across 45 states in the United States of America (USA), covering a population of nearly 100 million. Using county-level weekly wastewater surveillance data (over 20 months), WBE-based models were established through the random forest algorithm. WBE-based models accurately predicted the county-level weekly new admissions, allowing a preparation window of 1-4 weeks. In real applications, periodically updated WBE-based models showed good accuracy and transferability, with mean absolute error within 4-6 patients/100k population for upcoming weekly new hospitalization numbers. Our study demonstrated the potential of using WBE as an effective method to provide early warnings for healthcare systems.

COVID-19 Case and Mortality Surveillance using Daily SARS-CoV-2 in Wastewater Samples adjusting for Meteorological Conditions and Sample pH

Background

Wastewater monitoring is increasingly used for community surveillance of infectious diseases, especially after the COVID-19 pandemic as the genomic footprints of pathogens shed by infected individuals can be traced in the environment. However, detection and concentration of pathogens in the environmental samples and their efficacy in predicting infectious diseases can be influenced by meteorological conditions and quality of samples.

Objectives

This research examines whether meteorological conditions and sample pH affect SARS-CoV-2 concentrations in wastewater samples, and whether the association of SARS-CoV-2 with COVID-19 cases and mortality improves when adjusted for meteorological conditions and sample pH value in Miami-Dade County, FL.

Methods

Daily wastewater samples were collected from Miami-Dade Wastewater Treatment Plant in Key Biscayne, Florida from August 2021 to August 2022. The samples were analyzed for pH and spiked with OC43. RNA was extracted from the concentrated wastewater sample and SARS-CoV-2 was quantified using qPCR. COVID-19 and mortality data were acquired from the Centers for Disease Control and Prevention (CDC) and meteorological data from the National Climatic Data Center. COVID-19 case and mortality rates were modelled with respect to time-lagged wastewater SARS-CoV-2 adjusting for meteorological conditions, and sample pH value and OC43 recovery.

Results

Temperature, dew point, pH values and OC43 recovery showed significant associations with wastewater SARS-CoV-2. Time-lagged wastewater SARS-CoV-2 showed significant associations with COVID-19 case and mortality incidence rates. This association improved when wastewater SARS-CoV-2 levels were adjusted for (or instrumented on) meteorological conditions, OC43 recovery, and sample pH. A 0.47% change in COVID-19 case incidence rate was associated with 1% change in wastewater SARS-CoV-2 (β ~ 0.47; 95% CI = 0.29 - 0.64; p < 0.001). A 0.12 % change in COVID-19 mortality rate was associated with 1 % change in SARS-CoV-2 in wastewater 44 days prior. A 0.07% decline in COVID-19 mortality rate was associated with a unit increase in ambient temperature 28 days prior.

Discussion

Time lagged wastewater SARS-CoV-2 (and its adjustment for sample pH and RNA recovery) and meteorological conditions can be used for the surveillance of COVID-19 case and mortality. These findings can be extrapolated to improve the surveillance of other infectious diseases by proactive measurements of infectious agent(s) in the wastewater samples, adjusting for meteorological conditions and sample pH value.

Comparison of Nanotrap® Microbiome A Particles, membrane filtration, and skim milk workflows for SARS-CoV-2 concentration in wastewater

Introduction

Severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) RNA monitoring in wastewater has become an important tool for Coronavirus Disease 2019 (COVID-19) surveillance. Grab (quantitative) and passive samples (qualitative) are two distinct wastewater sampling methods. Although many viral concentration methods such as the usage of membrane filtration and skim milk are reported, these methods generally require large volumes of wastewater, expensive lab equipment, and laborious processes.

Methods

The objectives of this study were to compare two workflows (Nanotrap® Microbiome A Particles coupled with MagMax kit and membrane filtration workflows coupled with RNeasy kit) for SARS-CoV-2 recovery in grab samples and two workflows (Nanotrap® Microbiome A Particles and skim milk workflows coupled with MagMax kit) for SARS-CoV-2 recovery in Moore swab samples. The Nanotrap particle workflow was initially evaluated with and without the addition of the enhancement reagent 1 (ER1) in 10 mL wastewater. RT-qPCR targeting the nucleocapsid protein was used for detecting SARS-CoV-2 RNA.

Results

Adding ER1 to wastewater prior to viral concentration significantly improved viral concentration results (P < 0.0001) in 10 mL grab and swab samples processed by automated or manual Nanotrap workflows. SARS-CoV-2 concentrations in 10 mL grab and Moore swab samples with ER1 processed by the automated workflow as a whole showed significantly higher (P < 0.001) results than 150 mL grab samples using the membrane filtration workflow and 250 mL swab samples using the skim milk workflow, respectively. Spiking known genome copies (GC) of inactivated SARS-CoV-2 into 10 mL wastewater indicated that the limit of detection of the automated Nanotrap workflow was ~11.5 GC/mL using the RT-qPCR and 115 GC/mL using the digital PCR methods.

Discussion

These results suggest that Nanotrap workflows could substitute the traditional membrane filtration and skim milk workflows for viral concentration without compromising the assay sensitivity. The manual workflow can be used in resource-limited areas, and the automated workflow is appropriate for large-scale COVID-19 wastewater-based surveillance.

Wastewater-Based Epidemiology of SARS-CoV-2: Assessing Prevalence and Correlation with Clinical Cases

Wastewater-based epidemiology has been recognized as a tool to monitor the progress of COVID-19 pandemic worldwide. The study presented herein aimed at quantitating the SARS-CoV-2 RNA in the wastewaters, predicting the number of infected individuals in the catchment areas, and correlating it with the clinically reported COVID-19 cases. Wastewater samples (n = 162) from different treatment stages were collected from three wastewater treatment plants (WWTPs) from Mumbai city during the 2nd surge of COVID-19 (April 2021 to June 2021). SARS-CoV-2 causing COVID-19, was detected in 76.2% and 4.8% of raw and secondary treated (n = 63 each) wastewater samples respectively while all tertiary treated samples (n = 36) were negative. The quantity of SARS-CoV-2 RNA determined as gene copies/100 mL varied among all the three WWTPs under study. The gene copy numbers thus obtained were further used to estimate the number of infected individuals within the population served by these WWTPs using two published methods. A positive correlation (p < 0.05) was observed between the estimated number of infected individuals and clinically confirmed COVID-19 cases reported during the sampling period in two WWTPs. Predicted infected individuals calculated in this study were 100 times higher than the reported COVID-19 cases in all the WWTPs assessed. The study findings demonstrated that the present wastewater treatment technologies at the three WWTPs studied were adequate to remove the virus. However, SARS-CoV-2 genome surveillance with emphasis on monitoring its variants should be implemented as a routine practice to prepare for any future surge in infections.

Geospatially-resolved public-health surveillance via wastewater sequencing

Wastewater, which contains everything from pathogens to pollutants, is a geospatially-and temporally-linked microbial fingerprint of a given population. As a result, it can be leveraged for monitoring multiple dimensions of public health across locales and time. Here, we integrate targeted and bulk RNA sequencing (n=1,419 samples) to track the viral, bacterial, and functional content over geospatially distinct areas within Miami Dade County from 2020-2022. First, we used targeted amplicon sequencing (n=966) to track diverse SARS-CoV-2 variants across space and time, and we found a tight correspondence with clinical caseloads from University students (N = 1,503) and Miami-Dade County hospital patients (N = 3,939 patients), as well as an 8-day earlier detection of the Delta variant in wastewater vs. in patients. Additionally, in 453 metatranscriptomic samples, we demonstrate that different wastewater sampling locations have clinically and public-health-relevant microbiota that vary as a function of the size of the human population they represent. Through assembly, alignment-based, and phylogenetic approaches, we also detect multiple clinically important viruses (e.g., norovirus ) and describe geospatial and temporal variation in microbial functional genes that indicate the presence of pollutants. Moreover, we found distinct profiles of antimicrobial resistance (AMR) genes and virulence factors across campus buildings, dorms, and hospitals, with hospital wastewater containing a significant increase in AMR abundance. Overall, this effort lays the groundwork for systematic characterization of wastewater to improve public health decision making and a broad platform to detect emerging pathogens.

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.

Degradation rates influence the ability of composite samples to represent 24-hourly means of SARS-CoV-2 and other microbiological target measures in wastewater

The utility of using severe-acute respiratory syndrome coronavirus-2 (SARS-CoV-2) RNA for assessing the prevalence of COVID-19 within communities begins with the design of the sample collection program. The objective of this study was to assess the utility of 24-hour composites as representative samples for measuring multiple microbiological targets in wastewater, and whether normalization of SARS-CoV-2 by endogenous targets can be used to decrease hour to hour variability at different watershed scales. Two sets of experiments were conducted, in tandem with the same wastewater, with samples collected at the building, cluster, and community sewershed scales. The first set of experiments focused on evaluating degradation of microbiological targets: SARS-CoV-2, Simian Immunodeficiency Virus (SIV) - a surrogate spiked into the wastewater, plus human waste indicators of Pepper Mild Mottle Virus (PMMoV), Beta-2 microglobulin (B2M), and fecal coliform bacteria (FC). The second focused on the variability of these targets from samples, collected each hour on the hour. Results show that SARS-CoV-2, PMMoV, and B2M were relatively stable, with minimal degradation over 24-h. SIV, which was spiked-in prior to analysis, degraded significantly and FC increased significantly over the course of 24 h, emphasizing the possibility for decay and growth within wastewater. Hour-to-hour variability of the source wastewater was large between each hour of sampling relative to the variability of the SARS-CoV-2 levels calculated between sewershed scales; thus, differences in SARS-CoV-2 hourly variability were not statistically significant between sewershed scales. Results further provided that the quantified representativeness of 24-h composite samples (i.e., statistical equivalency compared against hourly collected grabs) was dependent upon the molecular target measured. Overall, improvements made by normalization were minimal within this study. Degradation and multiplication for other targets should be evaluated when deciding upon whether to collect composite or grab samples in future studies.

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 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.

Wastewater-based epidemiology approach: The learning lessons from COVID-19 pandemic and the development of novel guidelines for future pandemics

Wastewater-based epidemiology (WBE) provides a comprehensive real-time framework of population attitude and health status. This approach is attracting the interest of medical community and health authorities to monitor the prevalence of a virus (such as the severe acute respiratory syndrome coronavirus 2, SARS-CoV-2) among a community. Indeed, WBE is currently fine-tuning as environmental surveillance tool for coronavirus disease 2019 (COVID-19) pandemic. After a bibliometric analysis conducted to discover the research trends in WBE field, this work aimed to side-by-side compare the conventional method based on clinical testing with WBE approach. Furthermore, novel guidelines were developed to apply the WBE approach to a pandemic. The growing interest on WBE approach for COVID-19 pandemic is demonstrated by looking at the sharp increase in scientific papers published in the last years and at the ongoing studies on viral quantification methods and analytical procedures. The side-by-side comparison highlighted the ability of WBE to identify the hot-spot areas faster than the conventional approach, reducing the costs (e.g., rational use of available resources) and the gatherings at medical centers. Contrary to clinical testing, WBE has the surveillance capacity for preventing the virus resurgence, including asymptomatic contribution, and ensuring the preservation of medical staff health by avoiding the exposure to the virus infection during clinical testing. As extensively reported, the time in collecting epidemiological data is crucial for establishing the prevention and mitigation measures that are essential for curbing a pandemic. The developed guidelines can help to build a WBE system useful to control any future pandemic.

Predicting COVID-19 cases using SARS-CoV-2 RNA in air, surface swab and wastewater samples

Genomic footprints of pathogens shed by infected individuals can be traced in environmental samples, which can serve as a noninvasive method of infectious disease surveillance. The research evaluates the efficacy of environmental monitoring of SARS-CoV-2 RNA in air, surface swabs and wastewater to predict COVID-19 cases. Using a prospective experimental design, air, surface swabs, and wastewater samples were collected from a college dormitory housing roughly 500 students from March to May 2021 at the University of Miami, Coral Gables, FL. Students were randomly screened for COVID-19 during the study period. SARS-CoV-2 concentration in environmental samples was quantified using Volcano 2nd Generation-qPCR. Descriptive analyses were conducted to examine the associations between time-lagged SARS-CoV-2 in environmental samples and COVID-19 cases. SARS-CoV-2 was detected in air, surface swab and wastewater samples on 52 (63.4 %), 40 (50.0 %) and 57 (68.6 %) days, respectively. On 19 (24 %) of 78 days SARS-CoV-2 was detected in all three sample types. COVID-19 cases were reported on 11 days during the study period and SARS-CoV-2 was also detected two days before the case diagnosis on all 11 (100 %), 9 (81.8 %) and 8 (72.7 %) days in air, surface swab and wastewater samples, respectively. SARS-CoV-2 detection in environmental samples was an indicator of the presence of local COVID-19 cases and a 3-day lead indicator for a potential outbreak at the dormitory building scale. Proactive environmental surveillance of SARS-CoV-2 or other pathogens in multiple environmental media has potential to guide targeted measures to contain and/or mitigate infectious disease outbreaks within communities.

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.

Contextualizing Wastewater-Based surveillance in the COVID-19 vaccination era

SARS-CoV-2 wastewater-based surveillance (WBS) offers a tool for cost-effective oversight of a population's infections. In the past two years, WBS has proven to be crucial for managing the pandemic across different geographical regions. However, the changing context of the pandemic due to high levels of COVID-19 vaccination warrants a closer examination of its implication towards SARS-CoV-2 WBS. Two main questions were raised: 1) Does vaccination cause shedding of viral signatures without infection? 2) Does vaccination affect the relationship between wastewater and clinical data? To answer, we review historical reports of shedding from viral vaccines in use prior to the COVID-19 pandemic including for polio, rotavirus, influenza and measles infection and provide a perspective on the implications of different COVID-19 vaccination strategies with regard to the potential shedding of viral signatures into the sewershed. Additionally, we reviewed studies that looked into the relationship between wastewater and clinical data and how vaccination campaigns could have affected the relationship. Finally, analyzing wastewater and clinical data from the Netherlands, we observed changes in the relationship concomitant with increasing vaccination coverage and switches in dominant variants of concern. First, that no vaccine-derived shedding is expected from the current commercial pipeline of COVID-19 vaccines that may confound interpretation of WBS data. Secondly, that breakthrough infections from vaccinated individuals contribute significantly to wastewater signals and must be interpreted in light of the changing dynamics of shedding from new variants of concern.

Application of neighborhood-scale wastewater-based epidemiology in low COVID-19 incidence situations

Wastewater-based epidemiology (WBE), an emerging approach for community-wide COVID-19 surveillance, was primarily characterized at large sewersheds such as wastewater treatment plants serving a large population. Although informed public health measures can be better implemented for a small population, WBE for neighborhood-scale sewersheds is less studied and not fully understood. This study applied WBE to seven neighborhood-scale sewersheds (average population of 1471) from January to November 2021. Community testing data showed an average of 0.004 % incidence rate in these sewersheds (97 % of monitoring periods reported two or fewer daily infections). In 92 % of sewage samples, SARS-CoV-2 N gene fragments were below the limit of quantification. We statistically determined 10-2.6 as the threshold of the SARS-CoV-2 N gene concentration normalized to pepper mild mottle virus (N/PMMOV) to alert high COVID-19 incidence rate in the studied sewershed. This threshold of N/PMMOV identified neighborhood-scale outbreaks (COVID-19 incidence rate higher than 0.2 %) with 82 % sensitivity and 51 % specificity. Importantly, neighborhood-scale WBE can discern local outbreaks that would not otherwise be identified by city-scale WBE. Our findings suggest that neighborhood-scale WBE is an effective community-wide disease surveillance tool when COVID-19 incidence is maintained at a low level.

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.