Challenges in Measuring the Recovery of SARS-CoV-2 from Wastewater

Quantitative Reverse Transcription PCR Surveillance of SARS-CoV-2 Variants of Concern in Wastewater of Two Counties in Texas, United States

After its emergence in late November/December 2019, the severe acute respiratory syndrome coronavirus 2 virus (SARS-CoV-2) rapidly spread globally. Recognizing that this virus is shed in feces of individuals and that viral RNA is detectable in wastewater, testing for SARS-CoV-2 in sewage collections systems has allowed for the monitoring of a community's viral burden. Over a 9 month period, the influents of two regional wastewater treatment facilities were concurrently examined for wild-type SARS-CoV-2 along with variants B.1.1.7 and B.1.617.2 incorporated as they emerged. Epidemiological data including new confirmed COVID-19 cases and associated hospitalizations and fatalities were tabulated within each location. RNA from SARS-CoV-2 was detectable in 100% of the wastewater samples, while variant detection was more variable. Quantitative reverse transcription PCR (RT-qPCR) results align with clinical trends for COVID-19 cases, and increases in COVID-19 cases were positively related with increases in SARS-CoV-2 RNA load in wastewater, although the strength of this relationship was location specific. Our observations demonstrate that clinical and wastewater surveillance of SARS-CoV-2 wild type and constantly emerging variants of concern can be combined using RT-qPCR to characterize population infection dynamics. This may provide an early warning for at-risk communities and increases in COVID-19 related hospitalizations.

Simple Affinity-Based Method for Concentrating Viruses from Wastewater Using Engineered Curli Fibers

Wastewater surveillance is a proven method for tracking community spread and prevalence of some infectious viral diseases. A primary concentration step is often used to enrich viral particles from wastewater prior to subsequent viral quantification and/or sequencing. Here, we present a simple procedure for concentrating viruses from wastewater using bacterial biofilm protein nanofibers known as curli fibers. Through simple genetic engineering, we produced curli fibers functionalized with single-domain antibodies (also known as nanobodies) specific for the coat protein of the model virus bacteriophage MS2. Using these modified fibers in a simple spin-down protocol, we demonstrated efficient concentration of MS2 in both phosphate-buffered saline (PBS) and in the wastewater matrix. Additionally, we produced nanobody-functionalized curli fibers capable of binding the spike protein of SARS-CoV-2, showing the versatility of the system. Our concentration protocol is simple to implement, can be performed quickly under ambient conditions, and requires only components produced through bacterial culture. We believe this technology represents an attractive alternative to existing concentration methods and warrants further research and optimization for field-relevant applications.

Application of machine learning for multi-community COVID-19 outbreak predictions with wastewater surveillance

The potential of wastewater-based epidemiology (WBE) as a surveillance and early warning tool for the COVID-19 outbreak has been demonstrated. For areas with limited testing capacity, wastewater surveillance can provide information on the disease dynamic at a community level. A predictive model is a key to generating quantitative estimates of the infected population. Modeling longitudinal wastewater data can be challenging as biomarkers in wastewater are susceptible to variations caused by multiple factors associated with the wastewater matrix and the sewersheds characteristics. As WBE is an emerging trend, the model should be able to address the uncertainties of wastewater from different sewersheds. We proposed exploiting machine learning and deep learning techniques, which are supported by the growing WBE data. In this article, we reviewed the existing predictive models, among which the emerging machine learning/deep learning models showed great potential. However, most models are built for individual sewersheds with few features extracted from the wastewater. To fulfill the research gap, we compared different time-series and non-time-series models for their short-term predictive performance of COVID-19 cases in 9 diverse sewersheds. The time-series models, long short-term memory (LSTM) and Prophet, outcompeted the non-time-series models. Besides viral (SARS-CoV-2) loads and location identity, domain-specific features like biochemical parameters of wastewater, geographical parameters of the sewersheds, and some socioeconomic parameters of the communities can contribute to the models. With proper feature engineering and hyperparameter tuning, we believe machine learning models like LSTM can be a feasible solution for the COVID-19 trend prediction via WBE. Overall, this is a proof-of-concept study on the application of machine learning in COVID-19 WBE. Future studies are needed to deploy and maintain the model in more real-world applications.

The wave of the SARS-CoV-2 Omicron variant resulted in a rapid spike and decline as highlighted by municipal wastewater surveillance

This paper highlights the extraordinarily rapid spread of SARS-CoV-2 loads in wastewater that during the Omicron wave in December 2021-February 2022, compared with the profiles acquired in 2020-21 with 410 samples from two wastewater treatment plants (Trento+suburbs, 132,500 inhabitants). Monitoring of SARS-CoV-2 in wastewater focused on: (i) 3 samplings/week and analysis, (ii) normalization to calculate genomic units (GU) inh-1 d-1; (iii) calculation of a 7-day moving average to smooth daily fluctuations; (iv) comparison with the 'current active cases'/100,000 inh progressively affected by the mass vaccination. The time profiles of SARS-CoV-2 in wastewater matched the waves of active cases. In February-April 2021, a viral load of 1.0E＋07 GU inh-1 d- 1 corresponded to 700 active cases/100,000 inh. In July-September 2021, although the low current active cases, sewage revealed an appreciable SARS-CoV-2 circulation (in this period 2.2E＋07 GU inh-1 d-1 corresponded to 90 active cases/100,000 inh). Omicron was not detected in wastewater until mid-December 2021. The Omicron spread caused a 5-6 fold increase of the viral load in two weeks, reaching the highest peak (2.0-2.2E＋08 GU inh-1 d-1 and 4500 active cases/100,000 inh) during the pandemic. In this period, wastewater surveillance anticipated epidemiological data by about 6 days. In winter 2021-22, despite the 4-7 times higher viral loads in wastewater, hospitalizations were 4 times lower than in winter 2020-21 due to the vaccination coverage >80%. The Omicron wave demonstrated that SARS-CoV-2 monitoring of wastewater anticipated epidemiological data, confirming its importance in long-term surveillance.

Five-week warning of COVID-19 peaks prior to the Omicron surge in Detroit, Michigan using wastewater surveillance

Wastewater-based epidemiology (WBE) is useful in predicting temporal fluctuations of COVID-19 incidence in communities and providing early warnings of pending outbreaks. To investigate the relationship between SARS-CoV-2 concentrations in wastewater and COVID-19 incidence in communities, a 12-month study between September 1, 2020, and August 31, 2021, prior to the Omicron surge, was conducted. 407 untreated wastewater samples were collected from the Great Lakes Water Authority (GLWA) in southeastern Michigan. N1 and N2 genes of SARS-CoV-2 were quantified using RT-ddPCR. Daily confirmed COVID-19 cases for the City of Detroit, and Wayne, Macomb, Oakland counties between September 1, 2020, and October 4, 2021, were collected from a public data source. The total concentrations of N1 and N2 genes ranged from 714.85 to 7145.98 gc/L and 820.47 to 6219.05 gc/L, respectively, which were strongly correlated with the 7-day moving average of total daily COVID-19 cases in the associated areas, after 5 weeks of the viral measurement. The results indicate a potential 5-week lag time of wastewater surveillance preceding COVID-19 incidence for the Detroit metropolitan area. Four statistical models were established to analyze the relationship between SARS-CoV-2 concentrations in wastewater and COVID-19 incidence in the study areas. Under a 5-week lag time scenario with both N1 and N2 genes, the autoregression model with seasonal patterns and vector autoregression model were more effective in predicting COVID-19 cases during the study period. To investigate the impact of flow parameters on the correlation, the original N1 and N2 gene concentrations were normalized by wastewater flow parameters. The statistical results indicated the optimum models were consistent for both normalized and non-normalized data. In addition, we discussed parameters that explain the observed lag time. Furthermore, we evaluated the impact of the omicron surge that followed, and the impact of different sampling methods on the estimation of lag time.

Quantification of SARS-CoV-2 RNA in wastewater treatment plants mirrors the pandemic trend in Hong Kong

Wastewater-based epidemiology (WBE) for the SARS-CoV-2 virus in wastewater treatment plants (WWTPs) has emerged as a cost-effective and unbiased tool for population-level testing in the community. In the present study, we conducted a 6-month wastewater monitoring campaign from three WWTPs of different flow rates and catchment area characteristics, which serve 28 % (2.1 million people) of Hong Kong residents in total. Wastewater samples collected daily or every other day were concentrated using ultracentrifugation and the SARS-CoV-2 virus RNA in the supernatant was detected using the N1 and E primer sets. The results showed significant correlations between the virus concentration and the number of daily new cases in corresponding catchment areas of the three WWTPs when using 7-day moving average values (Kendall's tau-b value: 0.227-0.608, p < 0.001). SARS-CoV-2 virus concentration was normalized to a fecal indicator using PMMoV concentration and daily flow rates, but the normalization did not enhance the correlation. The key factors contributing to the correlation were also evaluated, including the sampling frequency, testing methods, and smoothing days. This study demonstrates the applicability of wastewater surveillance to monitor overall SARS-CoV-2 pandemic dynamics in a densely populated city like Hong Kong, and provides a large-scale longitudinal reference for the establishment of the long-term sentinel surveillance in WWTPs for WBE of pathogens which could be combined into a city-wide public health observatory.

Municipal and neighbourhood level wastewater surveillance and subtyping of an influenza virus outbreak

Recurrent influenza epidemics and pandemic potential are significant risks to global health. Public health authorities use clinical surveillance to locate and monitor influenza and influenza-like cases and outbreaks to mitigate hospitalizations and deaths. Currently, global integration of clinical surveillance is the only reliable method for reporting influenza types and subtypes to warn of emergent pandemic strains. The utility of wastewater surveillance (WWS) during the COVID-19 pandemic as a less resource intensive replacement or complement for clinical surveillance has been predicated on analyzing viral fragments in wastewater. We show here that influenza virus targets are stable in wastewater and partitions favorably to the solids fraction. By quantifying, typing, and subtyping the virus in municipal wastewater and primary sludge during a community outbreak, we forecasted a citywide flu outbreak with a 17-day lead time and provided population-level viral subtyping in near real-time to show the feasibility of influenza virus WWS at the municipal and neighbourhood levels in near real time using minimal resources and infrastructure.

Municipal and neighbourhood level wastewater surveillance and subtyping of an influenza virus outbreak

Recurrent influenza epidemics and pandemic potential are significant risks to global health. Public health authorities use clinical surveillance to locate and monitor influenza and influenza-like cases and outbreaks to mitigate hospitalizations and deaths. Currently, global integration of clinical surveillance is the only reliable method for reporting influenza types and subtypes to warn of emergent pandemic strains. The utility of wastewater surveillance (WWS) during the COVID-19 pandemic as a less resource intensive replacement or complement for clinical surveillance has been predicated on analyzing viral fragments in wastewater. We show here that influenza virus targets are stable in wastewater and partitions favorably to the solids fraction. By quantifying, typing, and subtyping the virus in municipal wastewater and primary sludge during a community outbreak, we forecasted a citywide flu outbreak with a 17-day lead time and provided population-level viral subtyping in near real-time to show the feasibility of influenza virus WWS at the municipal and neighbourhood levels in near real time using minimal resources and infrastructure.

SARS-CoV-2 Surveillance in Belgian Wastewaters

Wastewater-based surveillance was conducted by the national public health authority to monitor SARS-CoV-2 circulation in the Belgian population. Over 5 million inhabitants representing 45% of the Belgian population were monitored throughout 42 wastewater treatment plants for 15 months comprising three major virus waves. During the entire period, a high correlation was observed between the daily new COVID-19 cases and the SARS-CoV-2 concentration in wastewater corrected for rain impact and covered population size. Three alerting indicators were included in the weekly epidemiological assessment: High Circulation, Fast Increase, and Increasing Trend. These indicators were computed on normalized concentrations per individual treatment plant to allow for a comparison with a reference period as well as between analyses performed by distinct laboratories. When the indicators were not corrected for rain impact, rainy events caused an underestimation of the indicators. Despite this negative impact, the indicators permitted us to effectively monitor the evolution of the fourth virus wave and were considered complementary and valuable information to conventional epidemiological indicators in the weekly wastewater reports communicated to the National Risk Assessment Group.

Sewage surveillance for SARS-CoV-2: Molecular detection, quantification, and normalization factors

The presence of severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) in wastewater systems provides a primary indication of the coronavirus disease 2019 (COVID-19) spread throughout communities worldwide. Droplet digital polymerase chain reaction (dd-PCR) or reverse transcription-polymerase chain reaction (RT-PCR) administration of SARS-CoV-2 in wastewaters provides a reliable and efficient technology for gathering secondary local-level public health data. Often the accuracy of prevalence estimation is hampered by many methodological issues connected with wastewater surveillance. Still, more studies are needed to use and create efficient approaches for deciphering the actual SARS-CoV-2 indication from noise in the specimens/samples. Nearly 39-65% of positive patients and asymptomatic carriers expel the virus through their faeces however, only ∼6% of the infected hosts eject it through their urine. COVID-19 positive patients can shed the remnants of the SARS-CoV-2 RNA virus within the concentrations ∼10³-10⁸ copies/L. However, it can decrease up to 10² copies/L in wastewaters due to dilution. Environmental virology and microbiology laboratories play a significant role in the identification and analysis of SARS-CoV-2 ribonucleic acid (RNA) in waste and ambient waters worldwide. Virus extraction or recovery from the wastewater (However, due to lack of knowledge, established procedures, and integrated quality assurance/quality control (QA/QC) approaches, the novel coronavirus RNA investigation for estimating current illnesses and predicting future outbreaks is insufficient and/or conducted inadequately. The present manuscript is a technical review of the various methods and factors considered during the identification of SARS-CoV-2 genetic material in wastewaters and/or sludge, including tips and tricks to be taken care of during sampling, virus concentration, normalization, PCR inhibition, and trend line smoothening when compared with clinically active/positive cases.

Toward smart diagnosis of pandemic infectious diseases using wastewater-based epidemiology

COVID-19 outbreak revealed fundamental weaknesses of current diagnostic systems, particularly in prediction and subsequently prevention of pandemic infectious diseases (PIDs). Among PIDs detection methods, wastewater-based epidemiology (WBE) has been demonstrated to be a favorable mean for estimation of community-wide health. Besides, by going beyond purely sensing usages of WBE, it can be efficiently exploited in Healthcare 4.0/5.0 for surveillance, monitoring, control, and above all prediction and prevention, thereby, resulting in smart sensing and management of potential outbreaks/epidemics/pandemics. Herein, an overview of WBE sensors for PIDs is presented. The philosophy behind the smart diagnosis of PIDs using WBE with the help of digital technologies is then discussed, as well as their characteristics to be met. Analytical techniques that are pushing the frontiers of smart sensing and have a high potential to be used in the smart diagnosis of PIDs via WBE are surveyed. In this context, we underscore key challenges ahead and provide recommendations for implementing and moving faster toward smart diagnostics.

Multifaceted Assessment of Wastewater-Based Epidemiology for SARS-CoV-2 in Selected Urban Communities in Davao City, Philippines: A Pilot Study

Over 60 countries have integrated wastewater-based epidemiology (WBE) in their COVID-19 surveillance programs, focusing on wastewater treatment plants (WWTP). In this paper, we piloted the assessment of SARS-CoV-2 WBE as a complementary public health surveillance method in susceptible communities in a highly urbanized city without WWTP in the Philippines by exploring the extraction and detection methods, evaluating the contribution of physico-chemical-anthropogenic factors, and attempting whole-genome sequencing (WGS). Weekly wastewater samples were collected from sewer pipes or creeks in six communities with moderate-to-high risk of COVID-19 transmission, as categorized by the City Government of Davao from November to December 2020. Physico-chemical properties of the wastewater and anthropogenic conditions of the sites were noted. Samples were concentrated using a PEG-NaCl precipitation method and analyzed by RT-PCR to detect the SARS-CoV-2 N, RdRP, and E genes. A subset of nine samples were subjected to WGS using the Minion sequencing platform. SARS-CoV-2 RNA was detected in twenty-two samples (91.7%) regardless of the presence of new cases. Cycle threshold values correlated with RNA concentration and attack rate. The lack of a sewershed map in the sampled areas highlights the need to integrate this in the WBE planning. A combined analysis of wastewater physico-chemical parameters such as flow rate, surface water temperature, salinity, dissolved oxygen, and total dissolved solids provided insights on the ideal sampling location, time, and method for WBE, and their impact on RNA recovery. The contribution of fecal matter in the wastewater may also be assessed through the coliform count and in the context of anthropogenic conditions in the area. Finally, our attempt on WGS detected single-nucleotide polymorphisms (SNPs) in wastewater which included clinically reported and newly identified mutations in the Philippines. This exploratory report provides a contextualized framework for applying WBE surveillance in low-sanitation areas.

Current Methods and Prospects for Analysis and Characterization of Nanomaterials in the Environment

Analysis and characterization of naturally occurring and engineered nanomaterials in the environment are critical for understanding their environmental behaviors and defining real exposure scenarios for environmental risk assessment. However, this is challenging primarily due to the low concentration, structural heterogeneity, and dynamic transformation of nanomaterials in complex environmental matrices. In this critical review, we first summarize sample pretreatment methods developed for separation and preconcentration of nanomaterials from environmental samples, including natural waters, wastewater, soils, sediments, and biological media. Then, we review the state-of-the-art microscopic, spectroscopic, mass spectrometric, electrochemical, and size-fractionation methods for determination of mass and number abundance, as well as the morphological, compositional, and structural properties of nanomaterials, with discussion on their advantages and limitations. Despite recent advances in detecting and characterizing nanomaterials in the environment, challenges remain to improve the analytical sensitivity and resolution and to expand the method applications. It is important to develop methods for simultaneous determination of multifaceted nanomaterial properties for in situ analysis and characterization of nanomaterials under dynamic environmental conditions and for detection of nanoscale contaminants of emerging concern (e.g., nanoplastics and biological nanoparticles), which will greatly facilitate the standardization of nanomaterial analysis and characterization methods for environmental samples.

Development of an efficient wastewater testing protocol for high-throughput country-wide SARS-CoV-2 monitoring

Wastewater-based surveillance has been widely used as a non-intrusive tool to monitor population-level transmission of COVID-19. Although various approaches are available to concentrate viruses from wastewater samples, scalable methods remain limited. Here, we sought to identify and evaluate SARS-CoV-2 virus concentration protocols for high-throughput wastewater testing. A total of twelve protocols for polyethylene glycol (PEG) precipitation and four protocols for ultrafiltration-based approaches were evaluated across two phases. The first phase entailed an initial evaluation using a small sample set, while the second phase further evaluated five protocols using wastewater samples of varying SARS-CoV-2 concentrations. Permutations in the pre-concentration, virus concentration and RNA extraction steps were evaluated. Among PEG-based methods, SARS-CoV-2 virus recovery was optimal with 1) the removal of debris prior to processing, 2) 2 h to 24 h incubation with 8% PEG at 4 °C, 3) 4000 xg or 14,000 xg centrifugation, and 4) a column-based RNA extraction method, yielding virus recovery of 42.4-52.5%. Similarly, the optimal protocol for ultrafiltration included 1) the removal of debris prior to processing, 2) ultrafiltration, and 3) a column-based RNA extraction method, yielding a recovery of 38.2%. This study also revealed that SARS-CoV-2 RNA recovery for samples with higher virus concentration were less sensitive to changes in the PEG method, but permutations in the PEG protocol could significantly impact virus yields when wastewater samples with lower SARS-CoV-2 RNA were used. Although both PEG precipitation and ultrafiltration methods resulted in similar SARS-CoV-2 RNA recoveries, the former method is more cost-effective while the latter method provided operational efficiency as it required a shorter turn-around-time (PEG precipitation, 9-23 h; Ultrafiltration, 5 h). The decision on which method to adopt will thus depend on the use-case for wastewater testing, and the need for cost-effectiveness, sensitivity, operational feasibility and scalability.

Evaluation of a Wastewater-Based Epidemiological Approach to Estimate the Prevalence of SARS-CoV-2 Infections and the Detection of Viral Variants in Disparate Oregon Communities at City and Neighborhood Scales

BACKGROUND

Positive correlations have been reported between wastewater SARS-CoV-2 concentrations and a community's burden of infection, disease or both. However, previous studies mostly compared wastewater to clinical case counts or nonrepresentative convenience samples, limiting their quantitative potential.

OBJECTIVES

This study examined whether wastewater SARS-CoV-2 concentrations could provide better estimations for SARS-CoV-2 community prevalence than reported cases of COVID-19. In addition, this study tested whether wastewater-based epidemiology methods could identify neighborhood-level COVID-19 hotspots and SARS-CoV-2 variants.

METHODS

Community SARS-CoV-2 prevalence was estimated from eight randomized door-to-door nasal swab sampling events in six Oregon communities of disparate size, location, and demography over a 10-month period. Simultaneously, wastewater SARS-CoV-2 concentrations were quantified at each community's wastewater treatment plant and from 22 Newport, Oregon, neighborhoods. SARS-CoV-2 RNA was sequenced from all positive wastewater and nasal swab samples. Clinically reported case counts were obtained from the Oregon Health Authority.

RESULTS

Estimated community SARS-CoV-2 prevalence ranged from 8 to 1,687/10,000 persons. Community wastewater SARS-CoV-2 concentrations ranged from 2.9 to 5.1     log 10 gene copies per liter. Wastewater SARS-CoV-2 concentrations were more highly correlated (Pearson's r = 0.96 ; R 2 = 0.91 ) with community prevalence than were clinically reported cases of COVID-19 (Pearson's r = 0.85 ; R 2 = 0.73 ). Monte Carlo simulations indicated that wastewater SARS-CoV-2 concentrations were significantly better than clinically reported cases at estimating prevalence (p < 0.05 ). In addition, wastewater analyses determined neighborhood-level COVID-19 hot spots and identified SARS-CoV-2 variants (B.1 and B.1.399) at the neighborhood and city scales.

DISCUSSION

The greater reliability of wastewater SARS-CoV-2 concentrations over clinically reported case counts was likely due to systematic biases that affect reported case counts, including variations in access to testing and underreporting of asymptomatic cases. With these advantages, combined with scalability and low costs, wastewater-based epidemiology can be a key component in public health surveillance of COVID-19 and other communicable infections. https://doi.org/10.1289/EHP10289.

Wastewater-Based Estimation of the Effective Reproductive Number of SARS-CoV-2

BACKGROUND

The effective reproductive number, R e , is a critical indicator to monitor disease dynamics, inform regional and national policies, and estimate the effectiveness of interventions. It describes the average number of new infections caused by a single infectious person through time. To date, R e estimates are based on clinical data such as observed cases, hospitalizations, and/or deaths. These estimates are temporarily biased when clinical testing or reporting strategies change.

OBJECTIVES

We show that the dynamics of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) RNA in wastewater can be used to estimate R e in near real time, independent of clinical data and without the associated biases.

METHODS

We collected longitudinal measurements of SARS-CoV-2 RNA in wastewater in Zurich, Switzerland, and San Jose, California, USA. We combined this data with information on the temporal dynamics of shedding (the shedding load distribution) to estimate a time series proportional to the daily COVID-19 infection incidence. We estimated a wastewater-based R e from this incidence.

RESULTS

The method to estimate R e from wastewater worked robustly on data from two different countries and two wastewater matrices. The resulting estimates were as similar to the R e estimates from case report data as R e estimates based on observed cases, hospitalizations, and deaths are among each other. We further provide details on the effect of sampling frequency and the shedding load distribution on the ability to infer R e .

DISCUSSION

To our knowledge, this is the first time R e has been estimated from wastewater. This method provides a low-cost, rapid, and independent way to inform SARS-CoV-2 monitoring during the ongoing pandemic and is applicable to future wastewater-based epidemiology targeting other pathogens. https://doi.org/10.1289/EHP10050.

Wastewater-Based Estimation of the Effective Reproductive Number of SARS-CoV-2

BACKGROUND

The effective reproductive number, Re, is a critical indicator to monitor disease dynamics, inform regional and national policies, and estimate the effectiveness of interventions. It describes the average number of new infections caused by a single infectious person through time. To date, Re estimates are based on clinical data such as observed cases, hospitalizations, and/or deaths. These estimates are temporarily biased when clinical testing or reporting strategies change.

OBJECTIVES

We show that the dynamics of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) RNA in wastewater can be used to estimate Re in near real time, independent of clinical data and without the associated biases.

METHODS

We collected longitudinal measurements of SARS-CoV-2 RNA in wastewater in Zurich, Switzerland, and San Jose, California, USA. We combined this data with information on the temporal dynamics of shedding (the shedding load distribution) to estimate a time series proportional to the daily COVID-19 infection incidence. We estimated a wastewater-based Re from this incidence.

RESULTS

The method to estimate Re from wastewater worked robustly on data from two different countries and two wastewater matrices. The resulting estimates were as similar to the Re estimates from case report data as Re estimates based on observed cases, hospitalizations, and deaths are among each other. We further provide details on the effect of sampling frequency and the shedding load distribution on the ability to infer Re.

DISCUSSION

To our knowledge, this is the first time Re has been estimated from wastewater. This method provides a low-cost, rapid, and independent way to inform SARS-CoV-2 monitoring during the ongoing pandemic and is applicable to future wastewater-based epidemiology targeting other pathogens. https://doi.org/10.1289/EHP10050.

Effect of Time and Temperature on SARS-CoV-2 in Municipal Wastewater Conveyance Systems

Wastewater surveillance for SARS-CoV-2 is becoming a widespread public health metric, but little is known about pre-analytical influences on these measurements. We examined SARS-CoV-2 loads from two sewer service areas with different travel times that were within the same metropolitan area. Throughout the one-year study, case rates were nearly identical between the two service areas allowing us to compare differences in empirical concentrations relative to conveyance system characteristics and wastewater treatment plant parameters. We found time did not have a significant effect on degradation of SARS-CoV-2 when using average transit times (22 vs. 7.5 h) (p = 0.08), or under low flow conditions when transit times are greater (p = 0.14). Flow increased rather than decreased SARS-CoV-2 case-adjusted concentrations, but this increase was only significant in one service area. Warmer temperatures (16.8–19.8 °C) compared with colder (8.4–12.3 °C) reduced SARS-CoV-2 case-adjusted loads by ~50% in both plants (p < 0.05). Decreased concentrations in warmer temperatures may be an important factor to consider when comparing seasonal dynamics. Oxygen demand and suspended solids had no significant effect on SARS-CoV-2 case-adjusted loads overall. Understanding wastewater conveyance system influences prior to sample collection will improve comparisons of regional or national data for SARS-CoV-2 community infections.

Evaluation of process limit of detection and quantification variation of SARS-CoV-2 RT-qPCR and RT-dPCR assays for wastewater surveillance

Effective wastewater surveillance of SARS-CoV-2 RNA requires the rigorous characterization of the limit of detection resulting from the entire sampling process - the process limit of detection (PLOD). Yet to date, no studies have gone beyond quantifying the assay limit of detection (ALOD) for RT-qPCR or RT-dPCR assays. While the ALOD is the lowest number of gene copies (GC) associated with a 95% probability of detection in a single PCR reaction, the PLOD represents the sensitivity of the method after considering the efficiency of all processing steps (e.g., sample handling, concentration, nucleic acid extraction, and PCR assays) to determine the number of GC in the wastewater sample matrix with a specific probability of detection. The primary objective of this study was to estimate the PLOD resulting from the combination of primary concentration and extraction with six SARS-CoV-2 assays: five RT-qPCR assays (US CDC N1 and N2, China CDC N and ORF1ab (CCDC N and CCDC ORF1ab), and E_Sarbeco RT-qPCR, and one RT-dPCR assay (US CDC N1 RT-dPCR) using two models (exponential survival and cumulative Gaussian). An adsorption extraction (AE) concentration method (i.e., virus adsorption on membrane and the RNA extraction from the membrane) was used to concentrate gamma-irradiated SARS-CoV-2 seeded into 36 wastewater samples. Overall, the US CDC N1 RT-dPCR and RT-qPCR assays had the lowest ALODs (< 10 GC/reaction) and PLODs (<3,954 GC/50 mL; 95% probability of detection) regardless of the seeding level and model used. Nevertheless, consistent amplification and detection rates decreased when seeding levels were < 2.32 × 103 GC/50 mL even for US CDC N1 RT-qPCR and RT-dPCR assays. Consequently, when SARS-CoV-2 RNA concentrations are expected to be low, it may be necessary to improve the positive detection rates of wastewater surveillance by analyzing additional field and RT-PCR replicates. To the best of our knowledge, this is the first study to assess the SARS-CoV-2 PLOD for wastewater and provides important insights on the analytical limitations for trace detection of SARS-CoV-2 RNA in wastewater.

SARS-CoV-2 monitoring at three sewersheds of different scales and complexity demonstrates distinctive relationships between wastewater measurements and COVID-19 case data

Wastewater monitoring of SARS-CoV-2 presents a means of tracking COVID-19 community infection dynamics on a broader geographic scale. However, accounting for environmental and sample-processing losses may be necessary for wastewater measurements to readily inform our understanding of infection prevalence. Here, we present measurements of the SARS-CoV-2 N1 and N2 gene targets from weekly wastewater samples at three sites in Hamilton County, Ohio, during an increase and subsequent decline of COVID-19 infections. The concentration of N1 or N2 RNA in wastewater, measured over the course of six months, ranged from below the detection limit to over 104 gene copies/l, and correlated with case data at two wastewater treatment plants, but not at a sub-sewershed-level sampling site. We also evaluated the utility of a broader range of variables than has been reported consistently in previous work, in improving correlations of SARS-CoV-2 concentrations with case data. These include a spiked matrix recovery control (OC43), flow-normalization, and assessment of fecal loading using endogenous fecal markers (HF183, PMMoV, crAssphage). We found that adjusting for recovery, flow, and fecal indicators increased these correlations for samples from a larger sewershed (serving ~488,000 people) with greater industrial and stormwater inputs, but raw N1/N2 concentrations corresponded better with case data at a smaller, residential-oriented sewershed. Our results indicate that the optimal adjustment factors for correlating wastewater and clinical case data moving forward may not be generalizable to all sewersheds.

SARS-CoV-2 RNA is enriched by orders of magnitude in primary settled solids relative to liquid wastewater at publicly owned treatment works

Wastewater-based epidemiology has gained attention throughout the world for detection of SARS-CoV-2 RNA in wastewater to supplement clinical testing. Raw wastewater consists of small particles, or solids, suspended in liquid. Methods have been developed to measure SARS-CoV-2 RNA in the liquid and the solid fraction of wastewater, with some studies reporting higher concentrations in the solid fraction. To investigate this relationship further, six laboratories collaborated to conduct a study across five publicly owned treatment works (POTWs) where both primary settled solids obtained from primary clarifiers and raw wastewater influent samples were collected and quantified for SARS-CoV-2 RNA. Settled solids and influent samples were processed by participating laboratories using their respective methods and retrospectively paired based on date of collection. SARS-CoV-2 RNA concentrations, on a mass equivalent basis, were higher in settled solids than in influent by approximately three orders of magnitude. Concentrations in matched settled solids and influent were positively and significantly correlated at all five POTWs. RNA concentrations in both settled solids and influent were correlated to COVID-19 incidence rates in the sewersheds and thus representative of disease occurrence; the settled solids methods appeared to produce a comparable relationship between SARS-CoV-2 RNA concentration measurements and incidence rates across all POTWs. Settled solids and influent methods showed comparable sensitivity, N gene detection frequency, and calculated empirical incidence rate lower limits. Analysis of settled solids for SARS-CoV-2 RNA has the advantage of using less sample volume to achieve similar sensitivity to influent methods.

SARS-CoV-2 RNA is enriched by orders of magnitude in primary settled solids relative to liquid wastewater at publicly owned treatment works

Wastewater-based epidemiology has gained attention throughout the world for detection of SARS-CoV-2 RNA in wastewater to supplement clinical testing. Raw wastewater consists of small particles, or solids, suspended in liquid. Methods have been developed to measure SARS-CoV-2 RNA in the liquid and the solid fraction of wastewater, with some studies reporting higher concentrations in the solid fraction. To investigate this relationship further, six laboratories collaborated to conduct a study across five publicly owned treatment works (POTWs) where both primary settled solids obtained from primary clarifiers and raw wastewater influent samples were collected and quantified for SARS-CoV-2 RNA. Settled solids and influent samples were processed by participating laboratories using their respective methods and retrospectively paired based on date of collection. SARS-CoV-2 RNA concentrations, on a mass equivalent basis, were higher in settled solids than in influent by approximately three orders of magnitude. Concentrations in matched settled solids and influent were positively and significantly correlated at all five POTWs. RNA concentrations in both settled solids and influent were correlated to COVID-19 incidence rates in the sewersheds and thus representative of disease occurrence; the settled solids methods appeared to produce a comparable relationship between SARS-CoV-2 RNA concentration measurements and incidence rates across all POTWs. Settled solids and influent methods showed comparable sensitivity, N gene detection frequency, and calculated empirical incidence rate lower limits. Analysis of settled solids for SARS-CoV-2 RNA has the advantage of using less sample volume to achieve similar sensitivity to influent methods.

Validating and optimizing the method for molecular detection and quantification of SARS-CoV-2 in wastewater

Wastewater surveillance of SARS-CoV-2 has become a promising tool to estimate population-level changes in community infections and the prevalence of COVID-19 disease. Although many studies have reported the detection and quantification of SARS-CoV-2 in wastewater, remarkable variation remains in the methodology. In this study, we validated a molecular testing method by concentrating viruses from wastewater using ultrafiltration and detecting SARS-CoV-2 using one-step RT-qPCR assay. The following parameters were optimized including sample storage condition, wastewater pH, RNA extraction and RT-qPCR assay by quantification of SARS-CoV-2 or spiked human coronavirus strain 229E (hCoV-229E). Wastewater samples stored at 4 °C after collection showed significantly enhanced detection of SARS-CoV-2 with approximately 2-3 PCR-cycle threshold (Ct) values less when compared to samples stored at -20 °C. Pre-adjustment of the wastewater pH to 9.6 to aid virus desorption followed by pH readjustment to neutral after solid removal significantly increased the recovery of spiked hCoV-229E. Of the five commercially available RNA isolation kits evaluated, the MagMAX-96 viral RNA isolation kit showed the best recovery of hCoV-229E (50.1 ± 20.1%). Compared with two-step RT-qPCR, one-step RT-qPCR improved sensitivity for SARS-CoV-2 detection. Salmon DNA was included for monitoring PCR inhibition and pepper mild mottle virus (PMMoV), a fecal indicator indigenous to wastewater, was used to normalize SARS-CoV-2 levels in wastewater. Our method for molecular detection of SARS-CoV-2 in wastewater provides a useful tool for public health surveillance of COVID-19.

Association of SARS-CoV-2 presence in sewage with public adherence to precautionary measures and reported COVID-19 prevalence in Tehran

Compared to the growing body of literature on severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) detection and quantification in sewage, there are limited studies reporting on correlations between the viral loads in sewage and the prevalence of infected patients. The present work is a part of the regular monitoring effort for SARS-CoV-2 in wastewater influents from seven wastewater treatment plants (WWTPs) in Tehran, Iran, starting from late September 2020 until early April 2021. These facilities cover ~64% of the metropolis serving >5000,000 M individuals. The study set out to track the trends in the prevalence of COVID-19 in the community using wastewater based epidemiology (WBE) and to investigate whether these measurements correlate with officially reported infections in the population. Composite sewage samples collected over 16 h were enriched by polyethylene glycol precipitation and the corresponding threshold cycle (Ct) profiles for CDC 'N' and 'ORF1ab' assays were derived through real time RT-qPCR. Monte Carlo simulation model was employed to provide estimates of the disease prevalence in the study area. RNA from SARS-CoV-2 was detectable in 100% ('N' assay) and 81% ('ORF1ab' assay) of totally 91 sewage samples, with viral loads ranging from 40 to 45,000 gene copies/L. The outbreak of COVID-19 positively correlated (R2 = 0.80) with the measured viral load in sewage samples. Furthermore, sewage SARS-CoV-2 RNA loads preceded infections in the population by 1 to 2 days, which were in line with public adherence with and support for government instructions to contain the pandemic. Given the transient presence of human host-restricted infections such as SARS-CoV-2, these results provide evidence for assessment of the effectiveness of coordinated efforts that specifically address public health responses based on wastewater-based disease surveillance against not only COVID-19 but also for future infectious outbreaks.

Sensitivity of wastewater-based epidemiology for detection of SARS-CoV-2 RNA in a low prevalence setting

To assist public health responses to COVID-19, wastewater-based epidemiology (WBE) is being utilised internationally to monitor SARS-CoV-2 infections at the community level. However, questions remain regarding the sensitivity of WBE and its use in low prevalence settings. In this study, we estimated the total number of COVID-19 cases required for detection of SARS-CoV-2 RNA in wastewater. To do this, we leveraged a unique situation where, over a 4-month period, all symptomatic and asymptomatic cases, in a population of approximately 120,000, were precisely known and mainly located in a single managed isolation and quarantine facility (MIQF) building. From 9 July to 6 November 2020, 24-hr composite wastewater samples (n = 113) were collected daily from the sewer outside the MIQF, and from the municipal wastewater treatment plant (WWTP) located 5 km downstream. New daily COVID-19 cases at the MIQF ranged from 0 to 17, and for most of the study period there were no cases outside the MIQF identified. SARS-CoV-2 RNA was detected in 54.0% (61/113) at the WWTP, compared to 95.6% (108/113) at the MIQF. We used logistic regression to estimate the shedding of SARS-CoV-2 RNA into wastewater based on four infectious shedding models. With a total of 5 and 10 COVID-19 infectious cases per 100,000 population (0.005% and 0.01% prevalence) the predicated probability of SARS-CoV-2 RNA detection at the WWTP was estimated to be 28 and 41%, respectively. When a proportional shedding model was used, this increased to 58% and 87% for 5 and 10 cases, respectively. In other words, when 10 individuals were actively shedding SARS-CoV-2 RNA in a catchment of 100,000 individuals, there was a high likelihood of detecting viral RNA in wastewater. SARS-CoV-2 RNA detections at the WWTP were associated with increasing COVID-19 cases. Our results show that WBE provides a reliable and sensitive platform for detecting infections at the community scale, even when case prevalence is low, and can be of use as an early warning system for community outbreaks.

SARS-CoV-2 RNA surveillance in large to small centralized wastewater treatment plants preceding the third COVID-19 resurgence in Bangkok, Thailand

Wastewater surveillance for SARS-CoV-2 RNA has been a successful indicator of COVID-19 outbreaks in populations prior to clinical testing. However, this has been mostly conducted in high-income countries, which means there is a dearth of performance investigations in low- and middle-income countries with different socio-economic settings. This study evaluated the applicability of SARS-CoV-2 RNA monitoring in wastewater (n = 132) to inform COVID-19 infection in the city of Bangkok, Thailand using CDC N1 and N2 RT-qPCR assays. Wastewater influents (n = 112) and effluents (n = 20) were collected from 19 centralized wastewater treatment plants (WWTPs) comprising four large, four medium, and 11 small WWTPs during seven sampling events from January to April 2021 prior to the third COVID-19 resurgence that was officially declared in April 2021. The CDC N1 assay showed higher detection rates and mostly lower Ct values than the CDC N2. SARS-CoV-2 RNA was first detected at the first event when new reported cases were low. Increased positive detection rates preceded an increase in the number of newly reported cases and increased over time with the reported infection incidence. Wastewater surveillance (both positive rates and viral loads) showed strongest correlation with daily new COVID-19 cases at 22-24 days lag (Spearman's Rho = 0.85-1.00). Large WWTPs (serving 432,000-580,000 of the population) exhibited similar trends of viral loads and new cases to those from all 19 WWTPs, emphasizing that routine monitoring of the four large WWTPs could provide sufficient information for the city-scale dynamics. Higher sampling frequency at fewer sites, i.e., at the four representative WWTPs, is therefore suggested especially during the subsiding period of the outbreak to indicate the prevalence of COVID-19 infection, acting as an early warning of COVID-19 resurgence.

Understanding and managing uncertainty and variability for wastewater monitoring beyond the pandemic: Lessons learned from the United Kingdom national COVID-19 surveillance programmes

The COVID-19 pandemic has put unprecedented pressure on public health resources around the world. From adversity, opportunities have arisen to measure the state and dynamics of human disease at a scale not seen before. In the United Kingdom, the evidence that wastewater could be used to monitor the SARS-CoV-2 virus prompted the development of National wastewater surveillance programmes. The scale and pace of this work has proven to be unique in monitoring of virus dynamics at a national level, demonstrating the importance of wastewater-based epidemiology (WBE) for public health protection. Beyond COVID-19, it can provide additional value for monitoring and informing on a range of biological and chemical markers of human health. A discussion of measurement uncertainty associated with surveillance of wastewater, focusing on lessons-learned from the UK programmes monitoring COVID-19 is presented, showing that sources of uncertainty impacting measurement quality and interpretation of data for public health decision-making, are varied and complex. While some factors remain poorly understood, we present approaches taken by the UK programmes to manage and mitigate the more tractable sources of uncertainty. This work provides a platform to integrate uncertainty management into WBE activities as part of global One Health initiatives beyond the pandemic.

Comparison of five polyethylene glycol precipitation procedures for the RT-qPCR based recovery of murine hepatitis virus, bacteriophage phi6, and pepper mild mottle virus as a surrogate for SARS-CoV-2 from wastewater

Polyethylene glycol (PEG) precipitation is one of the conventional methods for virus concentration. This technique has been used to detect severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) RNA in wastewater. The procedures and seeded surrogate viruses were different among implementers; thus, the reported whole process recovery efficiencies considerably varied among studies. The present study compared five PEG precipitation procedures, with different operational parameters, for the RT-qPCR-based whole process recovery efficiency of murine hepatitis virus (MHV), bacteriophage phi6, and pepper mild mottle virus (PMMoV), and molecular process recovery efficiency of murine norovirus using 34 raw wastewater samples collected in Japan. The five procedures yielded significantly different whole process recovery efficiency of MHV (0.070%-2.6%) and phi6 (0.071%-0.51%). The observed concentration of indigenous PMMoV ranged from 8.9 to 9.7 log (8.2 × 108 to 5.6 × 109) copies/L. Interestingly, PEG precipitation with 2-h incubation outperformed that with overnight incubation partially due to the difference in molecular process recovery efficiency. The recovery load of MHV exhibited a positive correlation (r = 0.70) with that of PMMoV, suggesting that PMMoV is the potential indicator of the recovery efficiency of SARS-CoV-2. In addition, we reviewed 13 published studies and found considerable variability between different studies in the whole process recovery efficiency of enveloped viruses by PEG precipitation. This was due to the differences in operational parameters and surrogate viruses as well as the differences in wastewater quality and bias in the measurement of the seeded load of surrogate viruses, resulting from the use of different analytes and RNA extraction methods. Overall, the operational parameters (e.g., incubation time and pretreatment) should be optimized for PEG precipitation. Co-quantification of PMMoV may allow for the normalization of SARS-CoV-2 RNA concentration by correcting for the differences in whole process recovery efficiency and fecal load among samples.

Minimizing errors in RT-PCR detection and quantification of SARS-CoV-2 RNA for wastewater surveillance

Wastewater surveillance for pathogens using reverse transcription-polymerase chain reaction (RT-PCR) is an effective and resource-efficient tool for gathering community-level public health information, including the incidence of coronavirus disease-19 (COVID-19). Surveillance of Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2) in wastewater can potentially provide an early warning signal of COVID-19 infections in a community. The capacity of the world's environmental microbiology and virology laboratories for SARS-CoV-2 RNA characterization in wastewater is increasing rapidly. However, there are no standardized protocols or harmonized quality assurance and quality control (QA/QC) procedures for SARS-CoV-2 wastewater surveillance. This paper is a technical review of factors that can cause false-positive and false-negative errors in the surveillance of SARS-CoV-2 RNA in wastewater, culminating in recommended strategies that can be implemented to identify and mitigate some of these errors. Recommendations include stringent QA/QC measures, representative sampling approaches, effective virus concentration and efficient RNA extraction, PCR inhibition assessment, inclusion of sample processing controls, and considerations for RT-PCR assay selection and data interpretation. Clear data interpretation guidelines (e.g., determination of positive and negative samples) are critical, particularly when the incidence of SARS-CoV-2 in wastewater is low. Corrective and confirmatory actions must be in place for inconclusive results or results diverging from current trends (e.g., initial onset or reemergence of COVID-19 in a community). It is also prudent to perform interlaboratory comparisons to ensure results' reliability and interpretability for prospective and retrospective analyses. The strategies that are recommended in this review aim to improve SARS-CoV-2 characterization and detection for wastewater surveillance applications. A silver lining of the COVID-19 pandemic is that the efficacy of wastewater surveillance continues to be demonstrated during this global crisis. In the future, wastewater should also play an important role in the surveillance of a range of other communicable diseases.

A nationwide indicator to smooth and normalize heterogeneous SARS-CoV-2 RNA data in wastewater

Since many infected people experience no or few symptoms, the SARS-CoV-2 epidemic is frequently monitored through massive virus testing of the population, an approach that may be biased and may be difficult to sustain in low-income countries. Since SARS-CoV-2 RNA can be detected in stool samples, quantifying SARS-CoV-2 genome by RT-qPCR in wastewater treatment plants (WWTPs) has been carried out as a complementary tool to monitor virus circulation among human populations. However, measuring SARS-CoV-2 viral load in WWTPs can be affected by many experimental and environmental factors. To circumvent these limits, we propose here a novel indicator, the wastewater indicator (WWI), that partly reduces and corrects the noise associated with the SARS-CoV-2 genome quantification in wastewater (average noise reduction of 19%). All data processing results in an average correlation gain of 18% with the incidence rate. The WWI can take into account the censorship linked to the limit of quantification (LOQ), allows the automatic detection of outliers to be integrated into the smoothing algorithm, estimates the average measurement error committed on the samples and proposes a solution for inter-laboratory normalization in the absence of inter-laboratory assays (ILA). This method has been successfully applied in the context of Obépine, a French national network that has been quantifying SARS-CoV-2 genome in a representative sample of French WWTPs since March 5th 2020. By August 26th, 2021, 168 WWTPs were monitored in the French metropolitan and overseas territories of France. We detail the process of elaboration of this indicator, show that it is strongly correlated to the incidence rate and that the optimal time lag between these two signals is only a few days, making our indicator an efficient complement to the incidence rate. This alternative approach may be especially important to evaluate SARS-CoV-2 dynamics in human populations when the testing rate is low.

Comparative analysis of rapid concentration methods for the recovery of SARS-CoV-2 and quantification of human enteric viruses and a sewage-associated marker gene in untreated wastewater

To support public-health-related disease surveillance and monitoring, it is crucial to concentrate both enveloped and non-enveloped viruses from domestic wastewater. To date, most concentration methods were developed for non-enveloped viruses, and limited studies have directly compared the recovery efficiency of both types of viruses. In this study, the effectiveness of two different concentration methods (Concentrating pipette (CP) method and an adsorption-extraction (AE) method amended with MgCl₂) were evaluated for untreated wastewater matrices using three different viruses (SARS-CoV-2 (seeded), human adenovirus 40/41 (HAdV 40/41), and enterovirus (EV)) and a wastewater-associated bacterial marker gene targeting Lachnospiraceae (Lachno3). For SARS-CoV-2, the estimated mean recovery efficiencies were significantly greater by as much as 5.46 times, using the CP method than the AE method amended with MgCl₂. SARS-CoV-2 RNA recovery was greater for samples with higher titer seeds regardless of the method, and the estimated mean recovery efficiencies using the CP method were 25.1 ± 11% across ten WWTPs when wastewater samples were seeded with 5 × 10⁴ gene copies (GC) of SARS-CoV-2. Meanwhile, the AE method yielded significantly greater concentrations of indigenous HAdV 40/41 and Lachno3 from wastewater compared to the CP method. Finally, no significant differences in indigenous EV concentrations were identified in comparing the AE and CP methods. These data indicate that the most effective concentration method varies by microbial analyte and that the priorities of the surveillance or monitoring program should be considered when choosing the concentration method.

Droplet digital RT-PCR to detect SARS-CoV-2 signature mutations of variants of concern in wastewater

Wastewater surveillance has shown to be a valuable and efficient tool to obtain information about the trends of COVID-19 in the community. Since the recent emergence of new variants, associated with increased transmissibility and/or antibody escape (variants of concern), there is an urgent need for methods that enable specific and timely detection and quantification of the occurrence of these variants in the community. In this study, we demonstrate the use of RT-ddPCR on wastewater samples for specific detection of mutation N501Y. This assay enabled simultaneous enumeration of lineage B.1.351 (containing the 501Y mutation) and Wild Type (WT, containing 501N) SARS-CoV-2 RNA. Detection of N501Y was possible in samples with mixtures of WT with low proportions of B.1.351 (0.5%) and could accurately determine the proportion of N501Y and WT in mixtures of SARS-CoV-2 RNA. The application to raw sewage samples from the cities of Amsterdam and Utrecht demonstrated that this method can be applied to wastewater samples. The emergence of N501Y in Amsterdam and Utrecht wastewater aligned with the emergence of B.1.1.7 as causative agent of COVID-19 in the Netherlands, indicating that RT-ddPCR of wastewater samples can be used to monitor the emergence of the N501Y mutation in the community. It also indicates that RT-ddPCR could be used for sensitive and accurate monitoring of current (like K417N, K417T, E484K, L452R) or future mutations present in SARS-CoV-2 variants of concern. Monitoring these mutations can be used to obtain insight in the introduction and spread of VOC and support public health decision-making regarding measures to limit viral spread or allocation of testing or vaccination.

On the Critical Role of Human Feces and Public Toilets in the Transmission of COVID-19: Evidence from China

The surprising spread speed of the COVID-19 pandemic creates an urgent need for investigating the transmission chain or transmission pattern of COVID-19 beyond the traditional respiratory channels. This study therefore examines whether human feces and public toilets play a critical role in the transmission of COVID-19. First, it develops a theoretical model that simulates the transmission chain of COVID-19 through public restrooms. Second, it uses stabilized epidemic data from China to empirically examine this theory, conducting an empirical estimation using a two-stage least squares (2SLS) model with appropriate instrumental variables (IVs). This study confirms that the wastewater directly promotes the transmission of COVID-19 within a city. However, the role of garbage in this transmission chain is more indirect in the sense that garbage has a complex relationship with public toilets, and it promotes the transmission of COVID-19 within a city through interaction with public toilets and, hence, human feces. These findings have very strong policy implications in the sense that if we can somehow use the ratio of public toilets as a policy instrument, then we can find a way to minimize the total number of infections in a region. As shown in this study, pushing the ratio of public toilets (against open defecation) to the local population in a city to its optimal level would help to reduce the total infection in a region.

Monitoring of SARS-CoV-2 RNA in wastewater as an epidemiological surveillance tool in Mendoza, Argentina

Wastewater-based epidemiology (WBE) is an emerging tool that gives temporal and spatial information on a population's health status. Here, we report the epidemiological dynamics of a population of ~1.2 million residents in the metropolitan region of Mendoza province, Argentina, within the period July 2020 to January 2021. We combined the use of WBE of two wastewater treatment plants with epidemiological surveillance of the corresponding populations. We applied two viral concentration methods (polyethylene glycol precipitation and aluminum-based adsorption-flocculation) and RNA isolation methods in each wastewater sample to increase the possibility of detection and quantification of nucleocapsid markers (N1 and N2) of SARS-CoV-2 by RT-qPCR. Overall, our results allowed us to trace the rise, exponential growth, plateau, and fall of SARS-CoV-2 infections for 26 weeks. Individual analysis for each wastewater treatment plant showed a positive correlation between the viral load of SARS-CoV-2 genetic markers and COVID-19 cases that were diagnosed per week. Our findings indicate that WBE is a useful epidemiological indicator to anticipate the increase in COVID-19 cases and monitor the advance of the pandemic and different waves of infections.

Wastewater-Based Epidemiology for Community Monitoring of SARS-CoV-2: Progress and Challenges

Wastewater-based epidemiology (WBE) is useful for the surveillance of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in communities, complementing clinical diagnostic testing of individuals. In this Review, we summarize recent progress and highlight remaining challenges in monitoring SARS-CoV-2 RNA in wastewater systems for community and environmental surveillance. Very low concentrations of viral particles and RNA present in the complicated wastewater and sewage sample matrix require efficient sample processing and sensitive detection. We discuss advantages and limitations of available methods for wastewater sample processing, including collection, separation, enrichment, RNA extraction, and purification. Efficient extraction of the viral RNA and removal of interfering sample matrices are critical to the subsequent reverse transcription-quantitative polymerase chain reaction (RT-qPCR) for sensitive detection of SARS-CoV-2 in wastewater. We emphasize the importance of implementing appropriate controls and method validation, which include the use of surrogate viruses for assessing extraction efficiency and normalization against measurable chemical and biological components in wastewater. Critical analysis of the published studies reveals imperative research needs for the development, validation, and standardization of robust and sensitive methods for quantitative detection of viral RNA and proteins in wastewater for WBE.

Removal of SARS-CoV-2 viral markers through a water reclamation facility

There have been multiple reports of COVID-19 virus, SARS-CoV-2 RNA presence in influent wastewater of water reclamation facilities (WRFs) across the world. In this study, the removal of SARS-CoV-2 RNA was investigated in a WRF by collecting samples from various stages relayed to hydraulic retention time (HRT) and analyzed for viral RNA (N1 and N2) gene markers and wastewater characteristics. SARS-CoV-2 RNA was detected in 28 out of 28 influent wastewater and primary effluent samples. Secondary effluent showed 4 out of 9 positive samples, and all tertiary and final effluent samples were below the detection limit for the viral markers. The reduction was significant (p value < 0.005, one-way analysis of variance [ANOVA] test) in secondary treatment, ranging from 1.4 to 2.0 log10 removal. Adjusted N1 viral marker had a positive correlation with total suspended solids, total Kjeldahl nitrogen, and ammonia concentrations (Spearman's ρ = 0.61, 0.67, and 0.53, respectively, p value < 0.05), while demonstrating a strongly negative correlation with HRT (Spearman's ρ = -0.58, p value < 0.01). PRACTITIONER POINTS: Viral RNA was present in all samples taken from influent and primary effluent of a WRF. SARS-CoV-2 gene marker was detected in secondary effluent in 4 out of 9 samples. Tertiary and final effluent samples tested nondetect for SARS-CoV-2 gene markers. Up to 0.5 and 2.0 log10 virus removal values were achieved by primary and secondary treatment, respectively.

Assessing Multiplex Tiling PCR Sequencing Approaches for Detecting Genomic Variants of SARS-CoV-2 in Municipal Wastewater

Wastewater-based genomic surveillance of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus shows promise to complement genomic epidemiology efforts. Multiplex tiling PCR is a desirable approach for targeted genome sequencing of SARS-CoV-2 in wastewater due to its low cost and rapid turnaround time. However, it is not clear how different multiplex tiling PCR primer schemes or wastewater sample matrices impact the resulting SARS-CoV-2 genome coverage. The objective of this work was to assess the performance of three different multiplex primer schemes, consisting of 150-bp, 400-bp, and 1,200-bp amplicons, as well as two wastewater sample matrices, influent wastewater and primary sludge, for targeted genome sequencing of SARS-CoV-2. Wastewater samples were collected weekly from five municipal wastewater treatment plants (WWTPs) in the Metro Vancouver region of British Columbia, Canada during a period of increased coronavirus disease 19 (COVID-19) case counts from February to April 2021. RNA extracted from clarified influent wastewater provided significantly higher genome coverage (breadth and median depth) than primary sludge samples across all primer schemes. Shorter amplicons appeared to be more resilient to sample RNA degradation but were hindered by greater primer pool complexity in the 150-bp scheme. The identified optimal primer scheme (400 bp) and sample matrix (influent) were capable of detecting the emergence of mutations associated with genomic variants of concern, for which the daily wastewater load significantly correlated with clinical case counts. Taken together, these results provide guidance on best practices for implementing wastewater-based genomic surveillance and demonstrate its ability to inform epidemiology efforts by detecting genomic variants of concern circulating within a geographic region. IMPORTANCE Monitoring the genomic characteristics of the SARS-CoV-2 virus circulating in a population can shed important insights into epidemiological aspects of the COVID-19 outbreak. Sequencing every clinical patient sample in a highly populous area is a difficult feat, and thus sequencing SARS-CoV-2 RNA in municipal wastewater offers great promise to augment genomic surveillance by characterizing a pooled population sample matrix, particularly during an escalating outbreak. Here, we assess different approaches and sample matrices for rapid targeted genome sequencing of SARS-CoV-2 in municipal wastewater. We demonstrate that the optimal approach is capable of detecting the emergence of SARS-CoV-2 genomic variants of concern, with strong correlations to clinical case data in the province of British Columbia. These results provide guidance on best practices on, as well as further support for, the application of wastewater genomic surveillance as a tool to augment current genomic epidemiology efforts.

Assessing Multiplex Tiling PCR Sequencing Approaches for Detecting Genomic Variants of SARS-CoV-2 in Municipal Wastewater

Wastewater-based genomic surveillance of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus shows promise to complement genomic epidemiology efforts. Multiplex tiling PCR is a desirable approach for targeted genome sequencing of SARS-CoV-2 in wastewater due to its low cost and rapid turnaround time. However, it is not clear how different multiplex tiling PCR primer schemes or wastewater sample matrices impact the resulting SARS-CoV-2 genome coverage. The objective of this work was to assess the performance of three different multiplex primer schemes, consisting of 150-bp, 400-bp, and 1,200-bp amplicons, as well as two wastewater sample matrices, influent wastewater and primary sludge, for targeted genome sequencing of SARS-CoV-2. Wastewater samples were collected weekly from five municipal wastewater treatment plants (WWTPs) in the Metro Vancouver region of British Columbia, Canada during a period of increased coronavirus disease 19 (COVID-19) case counts from February to April 2021. RNA extracted from clarified influent wastewater provided significantly higher genome coverage (breadth and median depth) than primary sludge samples across all primer schemes. Shorter amplicons appeared to be more resilient to sample RNA degradation but were hindered by greater primer pool complexity in the 150-bp scheme. The identified optimal primer scheme (400 bp) and sample matrix (influent) were capable of detecting the emergence of mutations associated with genomic variants of concern, for which the daily wastewater load significantly correlated with clinical case counts. Taken together, these results provide guidance on best practices for implementing wastewater-based genomic surveillance and demonstrate its ability to inform epidemiology efforts by detecting genomic variants of concern circulating within a geographic region. IMPORTANCE Monitoring the genomic characteristics of the SARS-CoV-2 virus circulating in a population can shed important insights into epidemiological aspects of the COVID-19 outbreak. Sequencing every clinical patient sample in a highly populous area is a difficult feat, and thus sequencing SARS-CoV-2 RNA in municipal wastewater offers great promise to augment genomic surveillance by characterizing a pooled population sample matrix, particularly during an escalating outbreak. Here, we assess different approaches and sample matrices for rapid targeted genome sequencing of SARS-CoV-2 in municipal wastewater. We demonstrate that the optimal approach is capable of detecting the emergence of SARS-CoV-2 genomic variants of concern, with strong correlations to clinical case data in the province of British Columbia. These results provide guidance on best practices on, as well as further support for, the application of wastewater genomic surveillance as a tool to augment current genomic epidemiology efforts.

Wastewater surveillance of SARS-CoV-2 corroborates heightened community infection during the initial peak of COVID-19 in Bexar County, Texas

The purpose of this study was to conduct a preliminary assessment of the levels of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) RNA in wastewater at the Salitrillo Wastewater Treatment Plant in Texas during the initial peak of coronavirus disease 2019 (COVID-19) outbreak. Raw wastewater influent (24 h composite, time-based 1 L samples, n = 13) was collected weekly during June-August 2020. We measured SARS-CoV-2 RNA in wastewater by reverse transcription droplet digital PCR using the same N1 and N2 primer sets as employed in COVID-19 clinical testing. Virus RNA copies for positive samples (77%) ranged from 1.4 × 102 to 4.1 × 104 copies per liter of wastewater, and exhibited both increasing and decreasing trends, which corresponded well with the COVID-19 weekly infection rate (N1: ρ = 0.558, P = 0.048; N2: ρ = 0.487, P = 0.092). A sharp increase in virus RNA concentrations was observed during July sampling dates, consistent with the highest number of COVID-19 cases reported. This could be attributed to an increase in the spread of COVID-19 infection due to the Fourth of July holiday week gatherings (outdoor gatherings were limited to 100 people during that time). Our data show that wastewater surveillance is an effective tool to determine trends in infectious disease prevalence, and provide complementary information to clinical testing.

Wastewater Surveillance during Mass COVID-19 Vaccination on a College Campus

The suitability of wastewater monitoring following widespread vaccination against COVID-19 remains uncertain. SARS-CoV-2 RNA levels were monitored in wastewater solids during a university mass vaccination campaign in which >90% of the 12280 students were fully vaccinated (Pfizer-BioNTech, BNT162b2). SARS-CoV-2 RNA levels in wastewater solids correlated with the 7-day average of COVID-19 cases when lagged by 1-3 days (ρ = 0.51-0.55; p = 0.023-0.039). During and after the second vaccine dose, SARS-CoV-2 RNA was not detected in wastewater solids on 19 of 21 days (12 consecutive days of nondetection at the end of the semester), a significant decrease (p = 0.027) in positivity rate. A large influx of outside visitors (move out and commencement) led to an immediate increase in wastewater SARS-CoV-2 RNA positivity (seven detections over seven days). Wastewater solids offer a sensitive matrix for environmental surveillance of COVID-19 at the subsewershed level (50% probability of RNA detection with two cases) both during and after mass vaccination. Mass vaccination was coincident with decreased shedding of SARS-CoV-2 RNA into wastewater. This suggests the absence of a large population of shedding infections, symptomatic or not, following mass vaccination among a university campus population.

A rapid and simple protocol for concentration of SARS-CoV-2 from sewage

The aim of this study was to set up a simple protocol to concentrate SARS-CoV-2 from sewage, which can be implemented in laboratories with minimal equipment resources. The method avoids the need for extensive purification steps and reduces the concentration of potential inhibitors of RT-qPCR contained in sewage. The concentration method consists of a single step, in which a small volume (40 mL) of sewage sample is incubated with polyaluminum chloride (PAC)(0.00045 N Al³⁺ final concentration). Virus particles adsorbed to the precipitate are collected by low-speed centrifugation, after which the recovered pellet is resuspended with a saline buffer. PAC-concentrated samples are stable for at least one week at 4 °C. Therefore, they may be sent refrigerated to a diagnosis center for RNA extraction and RT-qPCR for SARS-CoV-2 RNA detection if the lab does not have such capabilities. The PAC concentration method produced an average shift of 4.5-units in quantification cycle (Cq) values compared to non-concentrated samples, indicating a 25-fold increase in detection sensitivity. The lower detection limit corresponded approximately to 100 viral copies per ml. Kappa index indicated substantial agreement between PAC and polyethylene glycol (PEG) precipitation protocols (k = 0.688, CI 0.457-0.919). This low-cost concentration protocol could be useful to aid in the monitoring of community circulation of SARS-CoV-2, especially in low- and middle-income countries, which do not have massive access to support from specialized labs for sewage surveillance.

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

Wastewater surveillance for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) RNA can be integrated with COVID-19 case data to inform timely pandemic response. However, more research is needed to apply and develop systematic methods to interpret the true SARS-CoV-2 signal from noise introduced in wastewater samples (e.g., from sewer conditions, sampling and extraction methods, etc.). In this study, raw wastewater was collected weekly from five sewersheds and one residential facility. The concentrations of SARS-CoV-2 in wastewater samples were compared to geocoded COVID-19 clinical testing data. SARS-CoV-2 was reliably detected (95% positivity) in frozen wastewater samples when reported daily new COVID-19 cases were 2.4 or more per 100,000 people. To adjust for variation in sample fecal content, four normalization biomarkers were evaluated: crAssphage, pepper mild mottle virus, Bacteroides ribosomal RNA (rRNA), and human 18S rRNA. Of these, crAssphage displayed the least spatial and temporal variability. Both unnormalized SARS-CoV-2 RNA signal and signal normalized to crAssphage had positive and significant correlation with clinical testing data (Kendall's Tau-b (τ)=0.43 and 0.38, respectively), but no normalization biomarker strengthened the correlation with clinical testing data. Locational dependencies and the date associated with testing data impacted the lead time of wastewater for clinical trends, and no lead time was observed when the sample collection date (versus the result date) was used for both wastewater and clinical testing data. This study supports that trends in wastewater surveillance data reflect trends in COVID-19 disease occurrence and presents tools that could be applied to make wastewater signal more interpretable and comparable across studies.

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

Wastewater surveillance for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) RNA can be integrated with COVID-19 case data to inform timely pandemic response. However, more research is needed to apply and develop systematic methods to interpret the true SARS-CoV-2 signal from noise introduced in wastewater samples (e.g., from sewer conditions, sampling and extraction methods, etc.). In this study, raw wastewater was collected weekly from five sewersheds and one residential facility. The concentrations of SARS-CoV-2 in wastewater samples were compared to geocoded COVID-19 clinical testing data. SARS-CoV-2 was reliably detected (95% positivity) in frozen wastewater samples when reported daily new COVID-19 cases were 2.4 or more per 100,000 people. To adjust for variation in sample fecal content, four normalization biomarkers were evaluated: crAssphage, pepper mild mottle virus, Bacteroides ribosomal RNA (rRNA), and human 18S rRNA. Of these, crAssphage displayed the least spatial and temporal variability. Both unnormalized SARS-CoV-2 RNA signal and signal normalized to crAssphage had positive and significant correlation with clinical testing data (Kendall's Tau-b (τ)=0.43 and 0.38, respectively), but no normalization biomarker strengthened the correlation with clinical testing data. Locational dependencies and the date associated with testing data impacted the lead time of wastewater for clinical trends, and no lead time was observed when the sample collection date (versus the result date) was used for both wastewater and clinical testing data. This study supports that trends in wastewater surveillance data reflect trends in COVID-19 disease occurrence and presents tools that could be applied to make wastewater signal more interpretable and comparable across studies.

Novel Multiplexed Amplicon-Based Sequencing to Quantify SARS-CoV-2 RNA from Wastewater

The application of wastewater-based epidemiology (WBE) to support the global response to the COVID-19 pandemic has shown encouraging outcomes. The accurate, sensitive, and high-throughput detection of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in municipal wastewater is critical for WBE. Here, we present a novel approach based on multiplexed amplicon-based sequencing, namely the ATOPlex platform, for detecting SARS-CoV-2. The ATOPlex platform is capable of quantifying SARS-CoV-2 RNA at concentrations that are at least 1 order of magnitude lower than the detection limit of reverse transcription quantitative polymerase chain reaction (RT-qPCR). Robust and accurate phylogenetic placement can be done at viral concentrations 4 times lower than the detection limit of RT-qPCR. We further found that the solid fraction in wastewater harbors a considerable amount of viral RNA, highlighting the need to extract viral RNA from the solid and liquid fractions of wastewater. This study delivers a highly sensitive, phylogenetically informative, and high-throughput analytical workflow that facilitates the application of WBE.

Standardizing data reporting in the research community to enhance the utility of open data for SARS-CoV-2 wastewater surveillance

SARS-CoV-2 RNA detection in wastewater is being rapidly developed and adopted as a public health monitoring tool worldwide. With wastewater surveillance programs being implemented across many different scales and by many different stakeholders, it is critical that data collected and shared are accompanied by an appropriate minimal amount of metainformation to enable meaningful interpretation and use of this new information source and intercomparison across datasets. While some databases are being developed for specific surveillance programs locally, regionally, nationally, and internationally, common globally-adopted data standards have not yet been established within the research community. Establishing such standards will require national and international consensus on what metainformation should accompany SARS-CoV-2 wastewater measurements. To establish a recommendation on minimum information to accompany reporting of SARS-CoV-2 occurrence in wastewater for the research community, the United States National Science Foundation (NSF) Research Coordination Network on Wastewater Surveillance for SARS-CoV-2 hosted a workshop in February 2021 with participants from academia, government agencies, private companies, wastewater utilities, public health laboratories, and research institutes. This report presents the primary two outcomes of the workshop: (i) a recommendation on the set of minimum meta-information that is needed to confidently interpret wastewater SARS-CoV-2 data, and (ii) insights from workshop discussions on how to improve standardization of data reporting.

Wastewater network infrastructure in public health: Applications and learnings from the COVID-19 pandemic

Accurate estimates of COVID-19 burden of infections in communities can inform public health strategy for the current pandemic. Wastewater based epidemiology (WBE) leverages sewer infrastructure to provide insights on rates of infection by measuring viral concentrations in wastewater. By accessing the sewer network at various junctures, important insights regarding COVID-19 disease activity can be gained. The analysis of sewage at the wastewater treatment plant level enables population-level surveillance of disease trends and virus mutations. At the neighborhood level, WBE can be used to describe trends in infection rates in the community thereby facilitating local efforts at targeted disease mitigation. Finally, at the building level, WBE can suggest the presence of infections and prompt individual testing. In this critical review, we describe the types of data that can be obtained through varying levels of WBE analysis, concrete plans for implementation, and public health actions that can be taken based on WBE surveillance data of infectious diseases, using recent and successful applications of WBE during the COVID-19 pandemic for illustration.