Wastewater-Based Epidemiology for Early Detection of Viral Outbreaks

Epidemiological monitoring of sewage sludge and wastewater from an upflow anaerobic sludge blanket reactor using SARS-CoV-2 as a model

This study explores the potential of sludge-based monitoring from an upflow anaerobic sludge blanket reactor for epidemiological surveillance, using SARS-CoV-2 as a model. We monitored SARS-CoV-2 copy numbers and mutations, and compared concentrations in sludge to concentrations in wastewater samples taken on the same days. From January to August 2021, 32 sludge samples were analyzed; 30 (93%) were positive for SARS-CoV-2, and copy numbers varied from 0.147 to 2.314 copies ×106/L. In wastewater samples collected on the same days, 31 (96%) were positive for SARS-CoV-2, and copy numbers ranged from 0.058 to 3.014 copies ×106/L. The concentration of SARS-CoV-2 in the sludge rose along with confirmed hospitalization cases in March, while wastewater SARS-CoV-2 concentrations rose 2 weeks earlier along with numbers of new confirmed cases. Mutations of variants of concern, Gamma and Delta, were identified in sludge samples in the same months that they became dominant in the corresponding regions. Our results indicate that, although monitoring of sewage sludge was not effective in anticipating infection numbers, it is a promising way to gain insight into the epidemiological situation in a city or region.

Community case study: an academia-industry-government partnership that monitors and predicts outbreaks in Tri-County Detroit area since 2017

The Tri-County Detroit Area (TCDA) is the 12th most populous metropolitan area in the United States with over three million people. Multiple communicable diseases are endemic in the TCDA. In 2017, to explore innovative methods that may provide early warnings of outbreaks affecting populations in the TCDA, an exploratory partnership that was funded by a U.S. National Science Foundation Early-concept Grant for Exploratory Research (EAGER) began. Since 2017, a project team including the College of Engineering at Michigan State University (MSU), the City of Detroit, the Great Lakes Water Authority (GLWA), industry, and local government and health departments, has been testing municipal wastewater from the TCDA to survey and predict surges in communicable diseases in the area. This ongoing effort started years before wastewater-based epidemiology became a widespread method in public health practice, due to the COVID-19 pandemic, and is now supported by the U.S. Centers for Disease Control and Prevention (CDC). The work of the partnership led to significant breakthroughs in the field of wastewater surveillance/wastewater epidemiology. The results of our surveillance efforts are used to assist local health departments in their understanding and response efforts for health issues in the TCDA, facilitating public health messaging for local awareness, targeted clinical testing, and increased vaccination efforts. Our data are available to the local health departments, and our methodological advancements are published and have been used by other communities nationwide and beyond. This paper describes the partnership, lessons learned, significant achievements, and provides a look into the future. The successful implementations and advancements of wastewater surveillance in the TCDA advocate the importance of frequent communications and interactions within the partnership, idea generations from each stakeholder for decision-making, maintenance of scientific rigor, ethical awareness, and more.

Development of Bayesian segmented Poisson regression model to forecast COVID-19 dynamics based on wastewater data: a case study in Nanning City, China

INTRODUCTION

COVID-19 has caused tremendous hardships and challenges around the globe. Due to the prevalence of asymptomatic and pre-symptomatic carriers, relying solely on disease testing to screen for infections is not entirely reliable, which may affect the accuracy of predictions about the pandemic trends. This study is dedicated to developing a predictive model aimed at estimating of the dynamics of COVID-19 at an early stage based on wastewater data, to assist in establishing an effective early warning system for disease control.

METHOD

Viral load in wastewater and the number of daily reported COVID-19 cases were collected from Nanning CDC and the Chinese Disease Prevention and Control Information System, respectively. We used the viral load to estimate daily reported cases by a Bayesian linear regression model. Subsequently, a Bayesian (segmented) Poisson regression model was developed, using data from the first wave of the epidemic as prior information, to predict the COVID-19 epidemic trend of the second wave. Finally, in order to explore the optimal training data for predicting outbreak dynamics during the pandemic, we fitted the model using various training sets.

RESULTS

The results revealed the estimated cases, using the viral load with a 3-day lag, were consistent with the actual reported cases, with adjusted R² value of 0.935 (p < 0.001). Our model successfully predicted the epidemic peak time and provided early warnings on the third day after the outbreak began. Furthermore, after using data from the first 6 days of the outbreak, the model's MAPE rapidly decreasing to lower levels (MAPE = 29.34%) and eventually stabilized at approximately 20%. Compared to using non-informative priors, this result allows for an advance warning of approximately two weeks. Importantly, as the inclusion of data from early outbreak increased, the predictive results of the model became more stable and accurate.

CONCLUSION

This study demonstrates the potential of wastewater-based epidemiology combined with Bayesian methods as a monitoring and predictive tool during infectious disease outbreaks.

Evaluation of Three Viral Capsid Integrity qPCR Methods for Wastewater-Based Viral Surveillance

Capsid Integrity qPCR (CI-qPCR) assays offer a promising alternative to cell culture-based infectivity assays for assessing pathogenic human virus viability in wastewater. This study compared three CI-qPCR methods: two novel (Crosslinker, TruTiter) and one established (PMAxx dye). These methods were evaluated on heat-inactivated and non-heat-inactivated 'live' viruses spiked into phosphate-buffered saline (PBS) and wastewater, as well as on viruses naturally present in wastewater samples. The viral panel included Human adenovirus 5 (HAdV), enterovirus A71 (EV), hepatitis-A virus (HAV), influenza-A H3N2 (IAV), respiratory syncytial virus A2 (RSV), norovirus GI, norovirus GII, and SARS-CoV-2. All three methods successfully differentiated between degraded, heat-inactivated, and live viruses in PBS. While all three methods were comparable for HAdV and norovirus GI, PMAxx detected significantly lower gene copies for EV and IAV. In spiked wastewater, PMAxx yielded significantly lower gene copies for all heat-inactivated viruses (HAdV, EV, HAV, IAV, and RSV) compared to the Crosslinker and TruTiter methods. For viruses naturally present in wastewater (un-spiked), no significant difference was observed between PMAxx and TruTiter methods. Intact, potentially infectious viruses were detected using both PMAxx and TruTiter on untreated and treated wastewater samples. A comparative analysis of qPCR data and TEM images revealed that viral flocculation of IAV may interfere with capsid integrity assays using intercalating dyes. In summary, our findings not only advance the development of more effective methods for assessing viral viability in wastewater, but also highlight the potential of CI-qPCR techniques to enhance early warning systems for emerging pathogens, thereby strengthening public health preparedness and response strategies.

Detection of SARS-CoV-2 in wastewater as an earlier predictor of COVID-19 epidemic peaks in Venezuela

Wastewater-based epidemiological surveillance has proven to be a useful and cost-effective tool for detecting COVID-19 outbreaks. Here, our objective was to evaluate its potential as an early warning system in Venezuela by detecting SARS-CoV-2 RNA in wastewater and its correlation with reported cases of COVID-19. Viral RNA was concentrated from wastewater collected at various sites in Caracas (northern Venezuela), from September 2021 to July 2023, using the polyethylene glycol (PEG) precipitation method. Viral quantification was performed by RT-qPCR targeting the N1 and ORF1ab genes. A significant association (p < 0.05) was found between viral load in wastewater and reported cases of COVID-19 up to six days after sampling. During the whole study, two populated areas of the city were persistent hotspots of viral infection. The L452R mutation, suggestive of the presence of the Delta variant, was identified in the only sample where a complete genomic sequence could be obtained. Significant differences (p < 0.05) between the physicochemical conditions of the wastewater samples positive and negative for the virus were found. Our results support proof of concept that wastewater surveillance can serve as an early warning system for SARS-CoV-2 outbreaks, complementing public health surveillance in those regions where COVID-19 is currently underreported.

Tracking Chlamydia and Syphilis in the Detroit Metro Area by Molecular Analysis of Environmental Samples

This paper describes one of the first studies applying wastewater surveillance to monitor Chlamydia and Syphilis and back-estimate infections in the community, based on bacterial shedding and wastewater surveillance data. Molecular biology laboratory methods were optimized, and a workflow was designed to implement wastewater surveillance tracking Chlamydia and Syphilis in the Detroit metro area (DMA), one of the most populous metropolitan areas in the U.S. Untreated composite wastewater samples were collected weekly from the three main interceptors that service DMA, which collect wastewater and discharge it to the Great Lakes Water Authority Water Resource Recovery Facility. Additionally, untreated wastewater was also collected from street manholes in three neighborhood sewersheds in Wayne, Macomb, and Oakland counties. Centrifugation, DNA extraction, and ddPCR methods were optimized and performed, targeting Chlamydia trachomatis and Treponema pallidum, the causative agents of Chlamydia and Syphilis, respectively. The limit of blank and limit of detection methods were determined experimentally for both targets. Both targets were detected and monitored in wastewater between December 25th, 2023, and April 22nd, 2024. The magnitudes of C. trachomatis and T. pallidum concentrations observed in neighborhood sewersheds were higher as compared to the concentrations observed in the interceptors. Infections of Chlamydia and Syphilis were back-estimated through an optimized formula based on shedding dynamics and wastewater surveillance data, which indicated potentially underreported conditions relative to publicly available clinical data.

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.

Exposure assessment of severe acute respiratory syndrome coronavirus 2 and norovirus genogroup I/genogroup II in aerosols generated by a municipal wastewater treatment plant

The presence of severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2) in wastewater and its potential as an airborne transmission source require extensive investigation, particularly in wastewater treatment plants (WWTPs), where few studies have been conducted. The aim of this study was to investigate the presence of SARS‐CoV‐2 and norovirus (NoV) RNA in wastewater and air samples collected from a municipal WWTP. In addition, the study assessed the potential risk of viral exposure among WWTP employees. In both the summer and winter campaigns of this study, SARS‐CoV‐2 and NoV RNA were quantified in wastewater/sludge samples other than effluent. Viral RNA was not detected in any of the air samples collected. The exposure risk assessment with the SARS‐CoV‐2 RNA concentrations in the influent pumping station of this study shows a lower risk than the calculation with the historical data provided by AquaVall, but both show a low‐to‐medium exposure risk for the WWTP workers. The sensitivity analysis shows that the result of the model is strongly influenced by the SARS‐CoV‐2 RNA quantification in the wastewater. This study underscores the need for extensive investigations into the presence and viability of SARS‐CoV‐2 in wastewater, especially as a potential airborne transmission source within WWTPs.

Urban Wastewater-Based Surveillance of SARS-CoV-2 Virus: A Two-Year Study Conducted in City of Patras, Greece

Wastewater-based epidemiology, during the COVID-19 pandemic years, has been applied as a complementary approach, worldwide, for tracking SARS-CoV-2 virus into the community and used as an early warning of the prevalence of COVID-19 infection. The present study presents the results of the 2-year surveillance project, in the city of Patras, Greece. The purpose of the study was to monitor SARS-CoV-2 and implement WBE as an early warning method of monitoring Public Health impact. The presence of SARS-CoV-2 was determined and quantified in 310 samples using RT-qPCR assays. For the years 2022 and 2023, 93.5% and 78.7% of samples were found positive, respectively. Comparison of detection methods have been conducted to select the method with the highest recovery of the viral load. A seasonal variation of the virus was recorded, showing a recession in summer months confirming the country's epidemiological data as indicated by positive correlation of wastewater viral load with registered cases of COVID-19 infections during these years (p < 0.05) and moreover sealed with a significant negative correlation observed with Daily Average (p < 0.01) and Daily Maximum Temperature (p < 0.01). More research was carried out to elucidate a possible association of physicochemical characteristics of wastewater with viral load showing positive correlation with Chlorides (p < 0.01) advocating possible increased use of chlorine-based disinfectants and Electrical Conductivity (p < 0.01) indicates that wastewater during periods of increased infections is more heavily loaded with ions from chemical and biological pollutants. No correlation found with rainfall and physicochemical indicators, such as COD, BOD5, Total Phosphorus, Total Nitrogen, and Total Suspended Solids. According to the findings, WBE represents a useful tool in the management of epidemics based on an environmental approach and it can also shed light on the interacting parameters that capture Public Health since any infections that may lead to epidemics lead to a parallel change in the use of pharmaceuticals, antimicrobials, disinfectants, and microbial load in urban wastewater.

Development and implementation of assays to monitor human adenovirus F40/41 in wastewater: Trends preceding, during, and following the non-A-to-E hepatitis outbreak in Stockholm

Human adenovirus (HAdV) type F41 has been identified as a possible cause of the non-A-to-E hepatitis outbreak. This study uses wastewater monitoring to track HAdV F40 and F41, supporting clinical investigations and providing insights into the pathogen's role in the outbreak. Given the limited clinical monitoring in Sweden of HAdV-F40/41, this approach also helps estimate the true infection burden of this pathogen during the outbreak. This study developed three qPCR assays for the hexon, penton, and fiber genes of HAdV F40 and F41. The hexon assay was F41-specific, while the fiber assay detected multiple HAdV-F strains. Comprehensive monitoring of HAdV-F40/41 levels in Stockholm's wastewater was conducted over 1.5 years, capturing the period before, during, and after the outbreak. A significant infection wave was observed in spring 2022, with strains beyond lineage 2 contributing to the outbreak. Moreover, simultaneous SARS-CoV-2 surveillance revealed that HAdV-F infections peaked at different times from COVID-19, but the HAdV-F wave aligned with the relaxation of pandemic restrictions. These findings offer valuable insights for future HAdV-F investigations and confirm its role in the non-A-to-E hepatitis outbreak.

Estimating the COVID-19 prevalence from wastewater

Wastewater based epidemiology has become a widely used tool for monitoring trends of concentrations of different pathogens, most notably and widespread of SARS-CoV-2. Therefore, in 2022, also in Rhineland-Palatinate, the Ministry of Science and Health has included 16 wastewater treatment sites in a surveillance program providing biweekly samples. However, the mere viral load data is subject to strong fluctuations and has limited value for political deciders on its own. Therefore, the state of Rhineland-Palatinate has commissioned the University Medical Center at Johannes Gutenberg University Mainz to conduct a representative cohort study called SentiSurv, in which an increasing number of up to 12,000 participants have been using sensitive antigen self-tests once or twice a week to test themselves for SARS-CoV-2 and report their status. This puts the state of Rhineland-Palatinate in the fortunate position of having time series of both, the viral load in wastewater and the prevalence of SARS-CoV-2 in the population. Our main contribution is a calibration study based on the data from 2023-01-08 until 2023-10-01 where we identified a scaling factor ( 0.208 ± 0.031 ) and a delay ( 5.07 ± 2.30 days) between the virus load in wastewater, normalized by the pepper mild mottle virus (PMMoV), and the prevalence recorded in the SentiSurv study. The relation is established by fitting an epidemiological model to both time series. We show how that can be used to estimate the prevalence when the cohort data is no longer available and how to use it as a forecasting instrument several weeks ahead of time. We show that the calibration and forecasting quality and the resulting factors depend strongly on how wastewater samples are normalized.

Bioinformatics-based screening tool identifies a wide variety of human and zoonotic viruses in Trujillo-Peru wastewater

Peru was one of the most affected countries during the COVID-19 pandemic. Moreover, multiple other viral diseases (enteric, respiratory, bloodborne, and vector-borne) are endemic and rising. According to Peru's Ministry of Health, various health facilities in the country were reallocated for the COVID-19 pandemic, thereby leading to reduced action to curb other diseases. Many viral diseases in the area are under-reported and not recognized. The One Health approach, in addition to clinical testing, incorporates environmental surveillance for detection of infectious disease outbreaks. The purpose of this work is to use a screening tool that is based on molecular methods, high throughput sequencing and bioinformatics analysis of wastewater samples to identify virus-related diseases circulating in Trujillo-Peru. To demonstrate the effectiveness of the tool, we collected nine untreated wastewater samples from the Covicorti wastewater utility in Trujillo-Peru on October 22, 2022. High throughput metagenomic sequencing followed by bioinformatic analysis was used to assess the viral diversity of the samples. Our results revealed the presence of sequences associated with multiple human and zoonotic viruses including Orthopoxvirus, Hepatovirus, Rhadinovirus, Parechovirus, Mamastrovirus, Enterovirus, Varicellovirus, Norovirus, Kobuvirus, Bocaparvovirus, Simplexvirus, Spumavirus, Orthohepevirus, Cardiovirus, Molliscipoxvirus, Salivirus, Parapoxvirus, Gammaretrovirus, Alphavirus, Lymphocryptovirus, Erythroparvovirus, Sapovirus, Cosavirus, Deltaretrovirus, Roseolovirus, Flavivirus, Betacoronavirus, Rubivirus, Lentivirus, Betapolyomavirus, Rotavirus, Hepacivirus, Alphacoronavirus, Mastadenovirus, Cytomegalovirus and Alphapapillomavirus. For confirmation purposes, we tested the samples for the presence of selective viruses belonging to the genera detected above. PCR based molecular methods confirmed the presence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), monkeypox virus (MPXV), noroviruses GI and GII (NoVGI and NoVGII), and rotavirus A (RoA) in our samples. Furthermore, publicly available clinical data for selected viruses confirm our findings. Wastewater or other environmental media surveillance, combined with bioinformatics methods, has the potential to serve as a systematic screening tool for the identification of human or zoonotic viruses that may cause disease. The results of this method can guide further clinical surveillance efforts and allocation of resources. Incorporation of this bioinformatic-based screening tool by public health officials in Peru and other Latin American countries will help manage endemic and emerging diseases that could save human lives and resources.

Statistical analysis of three data sources for Covid-19 monitoring in Rhineland-Palatinate, Germany

In Rhineland-Palatinate, Germany, a system of three data sources has been established to track the Covid-19 pandemic. These sources are the number of Covid-19-related hospitalizations, the Covid-19 genecopies in wastewater, and the prevalence derived from a cohort study. This paper presents an extensive comparison of these parameters. It is investigated whether wastewater data and a cohort study can be valid surrogate parameters for the number of hospitalizations and thus serve as predictors for coming Covid-19 waves. We observe that this is possible in general for the cohort study prevalence, while the wastewater data suffer from a too large variability to make quantitative predictions by a purely data-driven approach. However, the wastewater data and the cohort study prevalence are able to detect hospitalizations waves in a qualitative manner. Furthermore, a detailed comparison of different normalization techniques of wastewater data is provided.

Surveillance of Wastewater to Monitor the Prevalence of Gastroenteritis Viruses in Chiba Prefecture (2014-2019)

BACKGROUND

In Japan, sentinel surveillance is used to monitor the trend of infectious gastroenteritis. Another method of pathogen surveillance, wastewater-based epidemiology, has been used recently because it can help to monitor infectious disease without relying on patient data. Here, we aimed to determine the viral trends reflected in the number of reported patients and number of gastroenteritis virus-positive samples. We focused on gastroenteritis viruses present in wastewater and investigated the usefulness of wastewater surveillance for the surveillance of infectious gastroenteritis.

METHODS

Real-time polymerase chain reaction was used for viral gene detection in wastewater. The number of reported patients per pediatric sentinel site and number of viral genome copies were compared for correlation potential. The number of gastroenteritis virus-positive samples reported by National Epidemiological Surveillance of Infectious Disease (NESID) and the status of gastroenteritis viruses detected in wastewater were also evaluated.

RESULTS

Genes of norovirus genotype I, norovirus genotype II, sapovirus, astrovirus, rotavirus group A, and rotavirus group C were detected in wastewater samples. Viruses were detected in wastewater during periods when no gastroenteritis virus-positive samples were reported to NESID.

CONCLUSION

Norovirus genotype II and other gastroenteritis viruses were detected in wastewater even during periods when no gastroenteritis virus-positive samples were found. Therefore, surveillance using wastewater can complement sentinel surveillance and is an effective tool for the surveillance of infectious gastroenteritis.

Is the reliability of wastewater-based epidemiology affected by season? Comparative analysis with pharmaceuticals prescriptions

Wastewater-based epidemiology (WBE) has been already proposed by several authors for estimating the consumption of drugs, mainly the illicit ones. However, not much information is available about the actual reliability of this tool given the absence of comparison with the actual consumption. This work aims to evaluate the reliability of the WBE as a tool for estimating the consumption of pharmaceuticals in urban area. Measured consumption back-calculated with a WBE approach was compared with prescription of pharmaceutical products as "control." Moreover, seasonal influence on (i) pharmaceutical consumption, (ii) load of pharmaceutical products in the sewer system, and (iii) reliability of WBE was evaluated. Ciprofloxacin, sulfamethoxazole, metoprolol, carbamazepine, and citalopram were estimated by WBE with a difference respect to the "control" value lower than 0.2 order of magnitude while only trimethoprim and sotalol exceeded the 0.5 order of magnitude of difference but below the 1 order of magnitude. Sedatives were the best represented by WBE (on average 0.15 order of magnitude of difference compared to prescription data). However, further studies are suggested to fully estimate the influence of the type of APs on the reliability of the WBE. Seasonal patterns were found for the load of ciprofloxacin in the sewer and for the consumption of sulfamethoxazole and trimethoprim by population but seasonal changes did not have a significant impact (p > 0.05) on the reliability of WBE. Despite some gaps remained to optimize the reliability of the tool, WBE can be considered a valid method to estimate the consumption of prescribed drugs from the analysis of the sewer system.

Influenza transmission dynamics quantified from RNA in wastewater in Switzerland

INTRODUCTION

Influenza infections are challenging to monitor at the population level due to many mild and asymptomatic cases and similar symptoms to other common circulating respiratory diseases, including COVID-19. Methods for tracking cases outside of typical reporting infrastructure could improve monitoring of influenza transmission dynamics. Influenza shedding into wastewater represents a promising source of information where quantification is unbiased by testing or treatment-seeking behaviours.

METHODS

We quantified influenza A and B virus loads from influent at Switzerland's three largest wastewater treatment plants, serving about 14% of the Swiss population (1.2 million individuals). We estimated trends in infection incidence and the effective reproductive number (Re) in these catchments during a 2021/22 epidemic and compared our estimates to typical influenza surveillance data.

RESULTS

Wastewater data captured the same overall trends in infection incidence as laboratory-confirmed case data at the catchment level. However, the wastewater data were more sensitive in capturing a transient peak in incidence in December 2021 than the case data. The Re estimated from the wastewater data was roughly at or below the epidemic threshold of 1 during work-from-home measures in December 2021 but increased to at or above the epidemic threshold in two of the three catchments after the relaxation of these measures. The third catchment yielded qualitatively the same results but with wider confidence intervals. The confirmed case data at the catchment level yielded comparatively less precise R_e estimates before and during the work-from-home period, with confidence intervals that included one before and during the work-from-home period.

DISCUSSION

Overall, we show that influenza RNA in wastewater can help monitor nationwide influenza transmission dynamics. Based on this research, we developed an online dashboard for ongoing wastewater-based influenza surveillance in Switzerland.

Wastewater surveillance of the most common circulating respiratory viruses in Athens: The impact of COVID-19 on their seasonality

Due to governments' actions to contain the spread of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), the activity of common circulating respiratory viruses was significantly disrupted after the COVID-19 pandemic and thorough surveillance of respiratory pathogens was considered essential worldwide. Wastewater-based epidemiology has proven to be a valuable tool, that provides complementary information on disease outbreaks and is increasingly used to study the infection dynamics of other viruses, apart from SARS-CoV-2. The aims of the present study were the detection of four commonly circulating respiratory viruses: SARS-CoV-2, influenza A, B and Human Respiratory Syncytial Virus (RSV), the evaluation of the COVID-19 pandemic impact on their seasonality and the determination of the possible common trends in the viral load of these viruses in the wastewater of the Attica region. A standardized and validated concentration and extraction protocol was used, generic for all four viruses, followed by Reverse Transcription quantitative Polymerase Chain Reaction (RT-qPCR) assays. The study proved that there was a prolonged period when all four viruses circulated in the population and an early outbreak of seasonal influenza and RSV in 2022-2023, compared to data from the pre-COVID-19 period. SARS-CoV-2, influenza A and RSV concentrations showed peak levels during December, followed by a slight decline in influenza A concentrations, followed by steady increase of influenza B concentrations in January 2023. SARS-CoV-2 was the dominant virus throughout the whole study period. This is the first study in Greece that investigated the most common circulating viruses simultaneously and in one of the largest timelines, providing crucial information about their infection dynamics during a period when an outbreak of respiratory diseases was declared by the National Public Health Organization. Presented results highlight the establishment of environmental surveillance as a non-invasive and complementary virus outbreak monitoring tool and the importance of influenza A, B and RSV integration into a wastewater-based surveillance system to help in disease management.

Analysis of drug of abuse compounds using passive sampling and ultrahigh-liquid chromatography coupled to mass spectrometry

The present study proposes the monitoring of compounds of drugs of abuse through the use of passive samplers in water systems. Initially, four positive ion compounds of interest were determined according to national surveys, and then composite sampling and passive sampling were implemented using continuous-flow passive samplers containing two types of sorbents, the Empore disk and Gerstel Twister. Two study sites were established at the beginning and at the end of the middle Bogotá River basin. After 4 days, the sorbents were removed so that they could be desorbed and analyzed using UHPLC-MS in the laboratory. For the composite samples, the results were below the first calibration curve point (FCCP) of the chromatographic method, and for passive sampling, peaks of benzoylecgonine (BE) (21427.3 pg mL-1), methamphetamine (MET) (67101.5 pg mL-1), MDMA (ecstasy) (225844.8 pg mL-1) and 2-ethylidene-1,5-dimethyl-3,3-diphenylpyrrolidine (EDDP) (15908.4 pg mL-1) were found. Therefore, passive sampling could be suggested as an alternative to composite sampling for the monitoring of compounds.

Wastewater-based surveillance models for COVID-19: A focused review on spatio-temporal models

The evident shedding of the SARS-CoV-2 RNA particles from infected individuals into the wastewater opened up a tantalizing array of possibilities for prediction of COVID-19 prevalence prior to symptomatic case identification through community testing. Many countries have therefore explored the use of wastewater metrics as a surveillance tool, replacing traditional direct measurement of prevalence with cost-effective approaches based on SARS-CoV-2 RNA concentrations in wastewater samples. Two important aspects in building prediction models are: time over which the prediction occurs and space for which the predicted case numbers is shown. In this review, our main focus was on finding mathematical models which take into the account both the time-varying and spatial nature of wastewater-based metrics into account. We used six main characteristics as our assessment criteria: i) modelling approach; ii) temporal coverage; iii) spatial coverage; iv) sample size; v) wastewater sampling method; and vi) covariates included in the modelling. The majority of studies in the early phases of the pandemic recognized the temporal association of SARS-CoV-2 RNA concentration level in wastewater with the number of COVID-19 cases, ignoring their spatial context. We examined 15 studies up to April 2023, focusing on models considering both temporal and spatial aspects of wastewater metrics. Most early studies correlated temporal SARS-CoV-2 RNA levels with COVID-19 cases but overlooked spatial factors. Linear regression and SEIR models were commonly used (n = 10, 66.6 % of studies), along with machine learning (n = 1, 6.6 %) and Bayesian approaches (n = 1, 6.6 %) in some cases. Three studies employed spatio-temporal modelling approach (n = 3, 20.0 %). We conclude that the development, validation and calibration of further spatio-temporally explicit models should be done in parallel with the advancement of wastewater metrics before the potential of wastewater as a surveillance tool can be fully realised.

Adsorption of Respiratory Syncytial Virus, Rhinovirus, SARS-CoV-2, and F+ Bacteriophage MS2 RNA onto Wastewater Solids from Raw Wastewater

Despite the widespread adoption of wastewater surveillance, more research is needed to understand the fate and transport of viral genetic markers in wastewater. This information is essential for optimizing monitoring strategies and interpreting wastewater surveillance data. In this study, we examined the solid-liquid partitioning behavior of four viruses in wastewater: SARS-CoV-2, respiratory syncytial virus (RSV), rhinovirus (RV), and F+ coliphage/MS2. We used two approaches: (1) laboratory partitioning experiments using lab-grown viruses and (2) distribution experiments using endogenous viruses in raw wastewater. Partition experiments were conducted at 4 and 22 °C. Wastewater samples were spiked with varying concentrations of each virus, solids and liquids were separated via centrifugation, and viral RNA concentrations were quantified using reverse-transcription-digital droplet PCR (RT-ddPCR). For the distribution experiments, wastewater samples were collected from six wastewater treatment plants and processed without spiking exogenous viruses; viral RNA concentrations were measured in wastewater solids and liquids. In both experiments, RNA concentrations were higher in the solid fraction than the liquid fraction by approximately 3-4 orders of magnitude. Partition coefficients (KF) ranged from 2000-270,000 mL·g-1 across viruses and temperature conditions. Distribution coefficients (Kd) were consistent with results from partitioning experiments. Further research is needed to understand how virus and wastewater characteristics might influence the partitioning of viral genetic markers in wastewater.

Temporal and spatial relationships of CrAssphage and enteric viral and bacterial pathogens in wastewater in North Carolina

Enteric disease remains one of the most common concerns for public health, particularly when it results from human exposure to surface and recreational waters contaminated with wastewater. Characterizing the temporal and spatial variation of enteric pathogens prevalent in wastewater is critical to develop approaches to mitigate their distribution in the environment. In this study, we aim to characterize pathogen variability and test the applicability of the human-associated wastewater indicator crAssphage as an indicator of enteric viral and bacterial pathogens. We conducted weekly samplings for 14 months from four wastewater treatment plants in North Carolina, USA. Untreated wastewater samples were processed using hollow fiber ultrafiltration, followed by secondary concentration methods. Adenovirus, norovirus, enterovirus, Salmonella, Shiga toxin 2 (stx2), Campylobacter, and crAssphage were measured by quantitative polymerase chain reaction (qPCR) and reverse transcriptase (rt)-qPCR. Our results revealed significant correlations between crAssphage and human adenovirus, enterovirus, norovirus, Salmonella, and Campylobacter (p<0.01). Pathogens and crAssphage concentrations in untreated wastewater showed distinct seasonal patterns, with peak concentrations of crAssphage and viral pathogens in fall and winter, while bacterial pathogens showed peaked concentrations in either winter (Campylobacter), fall (Salmonella), or summer (stx2). This study enhances the understanding of crAssphage as an alternative molecular indicator for both bacterial and viral pathogens. The findings of this study can also inform microbial modeling efforts for the prediction of the impact of wastewater pathogens on surface waters due to increased flooding events and wastewater overflows associated with climate change.

Wastewater surveillance beyond COVID-19: a ranking system for communicable disease testing in the tri-county Detroit area, Michigan, USA

Introduction

Throughout the coronavirus disease 2019 (COVID-19) pandemic, wastewater surveillance has been utilized to monitor the disease in the United States through routine national, statewide, and regional monitoring projects. A significant canon of evidence was produced showing that wastewater surveillance is a credible and effective tool for disease monitoring. Hence, the application of wastewater surveillance can extend beyond monitoring SARS-CoV-2 to encompass a diverse range of emerging diseases. This article proposed a ranking system for prioritizing reportable communicable diseases (CDs) in the Tri-County Detroit Area (TCDA), Michigan, for future wastewater surveillance applications at the Great Lakes Water Authority's Water Reclamation Plant (GLWA's WRP).

Methods

The comprehensive CD wastewater surveillance ranking system (CDWSRank) was developed based on 6 binary and 6 quantitative parameters. The final ranking scores of CDs were computed by summing the multiplication products of weighting factors for each parameter, and then were sorted based on decreasing priority. Disease incidence data from 2014 to 2021 were collected for the TCDA. Disease incidence trends in the TCDA were endowed with higher weights, prioritizing the TCDA over the state of Michigan.

Results

Disparities in incidences of CDs were identified between the TCDA and state of Michigan, indicating epidemiological differences. Among 96 ranked CDs, some top ranked CDs did not present relatively high incidences but were prioritized, suggesting that such CDs require significant attention by wastewater surveillance practitioners, despite their relatively low incidences in the geographic area of interest. Appropriate wastewater sample concentration methods are summarized for the application of wastewater surveillance as per viral, bacterial, parasitic, and fungal pathogens.

Discussion

The CDWSRank system is one of the first of its kind to provide an empirical approach to prioritize CDs for wastewater surveillance, specifically in geographies served by centralized wastewater collection in the area of interest. The CDWSRank system provides a methodological tool and critical information that can help public health officials and policymakers allocate resources. It can be used to prioritize disease surveillance efforts and ensure that public health interventions are targeted at the most potentially urgent threats. The CDWSRank system can be easily adopted to geographical locations beyond the TCDA.

Hepatitis A virus monitoring in wastewater: A complementary tool to clinical surveillance

Monitoring wastewater is an effective tool for tracking information on trends of enteric viral dissemination. This study aimed to perform molecular detection and genetic characterization of HAV in wastewater and to correlate the results with those obtained from clinical surveillance. Wastewater samples (n=811) of the second most populous city in Argentina were collected from the main wastewater treatment plant (BG-WWTP, n=261), and at 7 local neighborhood collector sewers (LNCS, n=550) during 2017-2022. Clinical samples of acute hepatitis A cases (HA, n=54) were also analyzed. HAV molecular detection was performed by real time RT-PCR, and genetic characterization by RT-Nested PCR, sequencing and phylogenetic analysis. RNA-HAV was detected in sewage samples throughout the entire period studied, and detection frequencies varied according to the location and year (2.9% - 56.5%). In BG-WWTP, 23% of the samples were RNA-HAV+. The highest detection rates were in 2017 (30.0%), 2018 (41.7%) and 2022 (56.5%), which coincides with the highest number of HA cases reported. Twenty-eight (28) sequences were obtained (from clinical and sewage samples), and all were genotype IA. Two monophyletic clusters were identified: one that grouped clinical and wastewater samples from 2017-2018, and another with specimens from 2022, evidencing that environmental surveillance might constitute a replica of viral circulation in the population. These findings evidence that WBE, in a centralized and decentralized sewage monitoring, might be an effective strategy to track HAV circulation trends over time, contributing to the knowledge of HAV in the new post-vaccination epidemiological scenarios in Argentina and in Latin America.

Metagenomic analyses of plant virus sequences in sewage water for plant viruses monitoring

Frequent monitoring of emerging viruses of agricultural crops is one of the most important missions for plant virologists. A fast and precise identification of potential harmful viruses may prevent the occurrence of serious epidemics. Nowadays, high-throughput sequencing (HTS) technologies became an accessible and powerful tool for this purpose. The major discussion of this strategy resides in the process of sample collection, which is usually laborious, costly and nonrepresentative. In this study, we assessed the use of sewage water samples for monitoring the widespread, numerous, and stable plant viruses using HTS analysis and RT-qPCR. Plant viruses belonged to 12 virus families were found, from which Virgaviridae, Solemoviridae, Tymoviridae, Alphaflexiviridae, Betaflexiviridae, Closteroviridae and Secoviridae were the most abundant ones with more than 20 species. Additionally, we detected one quarantine virus in Brazil and a new tobamovirus species. To assess the importance of the processed foods as virus release origins to sewage, we selected two viruses, the tobamovirus pepper mild mottle virus (PMMoV) and the carlavirus garlic common latent virus (GarCLV), to detect in processed food materials by RT-qPCR. PMMoV was detected in large amount in pepper-based processed foods and in sewage samples, while GarCLV was less frequent in dried and fresh garlic samples, and in the sewage samples. This suggested a high correlation of virus abundance in sewage and processed food sources. The potential use of sewage for a virus survey is discussed in this study.

Supplementary Information

The online version contains supplementary material available at 10.1007/s40858-023-00575-8.

Establishment of quantitative and recovery method for detection of dengue virus in wastewater with noncognate spike control

Wastewater-based epidemiology (WBE) represents an efficient approach for public pathogen surveillance as it provides early warning of disease outbreaks; however, it has not yet been applied to dengue virus (DENV), which might cause endemics via mosquito spread. In this study, a working platform was established to provide direct virus recovery and qPCR quantification from wastewater samples that were artificially loaded with target DENV serotypes I to IV and noncognate spike control viral particles. The results showed qPCR efficiencies of 91.2 %, 94.8 %, 92.6 % and 88.7 % for DENV I, II, III, and IV, respectively, and a broad working range over 6 orders of magnitude using the preferred primer sets. Next, the results revealed that the ultrafiltration method was superior to the skimmed milk flocculation method for recovering either DENV or control viral particles from wastewater. Finally, DENV-2 was loaded simultaneously with the noncognate spike control and could be recovered at comparable levels either in PBS or in wastewater, indicating the applicability of noncognate spike control particles to reflect the efficiency of experimental steps. In conclusion, our data suggest that DENV particles in wastewater could be recovered and quantitatively detected in absolute amounts, indicating the feasibility of DENV surveillance using the WBE approach.

Number of COVID-19 cases required in a population to detect SARS-CoV-2 RNA in wastewater in the province of Alberta, Canada: Sensitivity assessment

With a unique and large size of testing results of 1,842 samples collected from 12 wastewater treatment plants (WWTP) for 14 months through from low to high prevalence of COVID-19, the sensitivity of RT-qPCR detection of SARS-CoV-2 RNA in wastewater that correspond to the communities was computed by using Probit analysis. This study determined the number of new COVID-19 cases per 100,000 population required to detect SARS-CoV-2 RNA in wastewater at defined probabilities and provided an evidence-based framework of wastewater-based epidemiology surveillance (WBE). Input data were positive and negative test results of SARS-CoV-2 RNA in wastewater samples and the corresponding new COVID-19 case rates per 100,000 population served by each WWTP. The analyses determined that RT-qPCR-based SARS-CoV-2 RNA detection threshold at 50%, 80% and 99% probability required a median of 8 (range: 4-19), 18 (9-43), and 38 (17-97) of new COVID-19 cases /100,000, respectively. Namely, the positive detection rate at 50%, 80% and 99% probability were 0.01%, 0.02%, and 0.04% averagely for new cases in the population. This study improves understanding of the performance of WBE SARS-CoV-2 RNA detection using the large datasets and prolonged study period. Estimated COVID-19 burden at a community level that would result in a positive detection of SARS-CoV-2 in wastewater is critical to support WBE application as a supplementary warning/monitoring system for COVID-19 prevention and control.

Quantification of SARS-CoV-2 in wastewater samples from hospitals treating COVID-19 patients during the first wave of the pandemic in Brazil

The COVID-19 pandemic has caused a global health crisis, and wastewater-based epidemiology (WBE) has emerged as an important tool to assist public health decision-making. Recent studies have shown that the SARS-CoV-2 RNA concentration in wastewater samples is a reliable indicator of the severity of the pandemic for large populations. However, few studies have established a strong correlation between the number of infected people and the viral concentration in wastewater due to variations in viral shedding over time, viral decay, infiltration, and inflow. Herein we present the relationship between the number of COVID-19-positive patients and the viral concentration in wastewater samples from three different hospitals (A, B, and C) in the city of Belo Horizonte, Minas Gerais, Brazil. A positive and strong correlation between wastewater SARS-CoV-2 concentration and the number of confirmed cases was observed for Hospital B for both regions of the N gene (R = 0.89 and 0.77 for N1 and N2, respectively), while samples from Hospitals A and C showed low and moderate correlations, respectively. Even though the effects of viral decay and infiltration were minimized in our study, the variability of viral shedding throughout the infection period and feces dilution due to water usage for different activities in the hospitals could have affected the viral concentrations. These effects were prominent in Hospital A, which had the smallest sewershed population size, and where no correlation between the number of defecations from COVID-19 patients and viral concentration in wastewater was observed. Although we could not determine trends in the number of infected patients through SARS-CoV-2 concentrations in hospitals' wastewater samples, our results suggest that wastewater monitoring can be efficient for the detection of infected individuals at a local level, complementing clinical data.

SARS-CoV-2 raw wastewater surveillance from student residences on an urban university campus

The COVID-19 pandemic brought about an urgent need to monitor the community prevalence of infection and detect the presence of SARS-CoV-2. Testing individual people is the most reliable method to measure the spread of the virus in any given community, but it is also the most expensive and time-consuming. Wastewater-based epidemiology (WBE) has been used since the 1960s when scientists implemented monitoring to measure the effectiveness of the Polio vaccine. Since then, WBE has been used to monitor populations for various pathogens, drugs, and pollutants. In August 2020, the University of Tennessee-Knoxville implemented a SARS-CoV-2 surveillance program that began with raw wastewater surveillance of the student residence buildings on campus, the results of which were shared with another lab group on campus that oversaw the pooled saliva testing of students. Sample collection began at 8 am, and the final RT-qPCR results were obtained by midnight. The previous day's results were presented to the campus administrators and the Student Health Center at 8 am the following morning. The buildings surveyed included all campus dormitories, fraternities, and sororities, 46 buildings in all representing an on-campus community of over 8,000 students. The WBE surveillance relied upon early morning "grab" samples and 24-h composite sampling. Because we only had three Hach AS950 Portable Peristaltic Sampler units, we reserved 24-h composite sampling for the dormitories with the highest population of students. Samples were pasteurized, and heavy sediment was centrifuged and filtered out, followed by a virus concentration step before RNA extraction. Each sample was tested by RT-qPCR for the presence of SARS-CoV-2, using the CDC primers for N Capsid targets N1 and N3. The subsequent pooled saliva tests from sections of each building allowed lower costs and minimized the total number of individual verification tests that needed to be analyzed by the Student Health Center. Our WBE results matched the trend of the on-campus cases reported by the student health center. The highest concentration of genomic copies detected in one sample was 5.06 × 107 copies/L. Raw wastewater-based epidemiology is an efficient, economical, fast, and non-invasive method to monitor a large community for a single pathogen or multiple pathogen targets.

Detection of SARS-CoV-2 RNA in wastewater and comparison to COVID-19 cases in two sewersheds, North Carolina, USA

Wastewater surveillance of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) may be useful for monitoring population-wide coronavirus disease 2019 (COVID-19) infections, especially given asymptomatic infections and limitations in diagnostic testing. We aimed to detect SARS-CoV-2 RNA in wastewater and compare viral concentrations to COVID-19 case numbers in the respective counties and sewersheds. Influent 24-hour composite wastewater samples were collected from July to December 2020 from two municipal wastewater treatment plants serving different population sizes in Orange and Chatham Counties in North Carolina. After a concentration step via HA filtration, SARS-CoV-2 RNA was detected and quantified by reverse transcription droplet digital polymerase chain reaction (RT-ddPCR) and quantitative PCR (RT-qPCR), targeting the N1 and N2 nucleocapsid genes. SARS-CoV-2 RNA was detected by RT-ddPCR in 100 % (24/24) and 79 % (19/24) of influent wastewater samples from the larger and smaller plants, respectively. In comparison, viral RNA was detected by RT-qPCR in 41.7 % (10/24) and 8.3 % (2/24) of samples from the larger and smaller plants, respectively. Positivity rates and method agreement further increased for the RT-qPCR assay when samples with positive signals below the limit of detection were counted as positive. The wastewater data from the larger plant generally correlated (⍴ ~0.5, p < 0.05) with, and even anticipated, the trends in reported COVID-19 cases, with a notable spike in measured viral RNA preceding a spike in cases when students returned to a college campus in the Orange County sewershed. Correlations were generally higher when using estimates of sewershed-level case data rather than county-level data. This work supports use of wastewater surveillance for tracking COVID-19 disease trends, especially in identifying spikes in cases. Wastewater-based epidemiology can be a valuable resource for tracking disease trends, allocating resources, and evaluating policy in the fight against current and future pandemics.

Longitudinal metatranscriptomic sequencing of Southern California wastewater representing 16 million people from August 2020-21 reveals widespread transcription of antibiotic resistance genes

Municipal wastewater provides a representative sample of human fecal waste across a catchment area and contains a wide diversity of microbes. Sequencing wastewater samples provides information about human-associated and medically important microbial populations, and may be useful to assay disease prevalence and antimicrobial resistance (AMR). Here, we present a study in which we used untargeted metatranscriptomic sequencing on RNA extracted from 275 sewage influent samples obtained from eight wastewater treatment plants (WTPs) representing approximately 16 million people in Southern California between August 2020 - August 2021. We characterized bacterial and viral transcripts, assessed metabolic pathway activity, and identified over 2,000 AMR genes/variants across all samples. Because we did not deplete ribosomal RNA, we have a unique window into AMR carried as ribosomal mutants. We show that AMR diversity varied between WTPs (as measured through PERMANOVA, P < 0.001) and that the relative abundance of many individual AMR genes/variants increased over time (as measured with MaAsLin2, P_adj < 0.05). Similarly, we detected transcripts mapping to human pathogenic bacteria and viruses suggesting RNA sequencing is a powerful tool for wastewater-based epidemiology and that there are geographical signatures to microbial transcription. We captured the transcription of gene pathways common to bacterial cell processes, including central carbon metabolism, nucleotide synthesis/salvage, and amino acid biosynthesis. We also posit that due to the ubiquity of many viruses and bacteria in wastewater, new biological targets for microbial water quality assessment can be developed. To the best of our knowledge, our study provides the most complete longitudinal metatranscriptomic analysis of a large population's wastewater to date and demonstrates our ability to monitor the presence and activity of microbes in complex samples. By sequencing RNA, we can track the relative abundance of expressed AMR genes/variants and metabolic pathways, increasing our understanding of AMR activity across large human populations and sewer sheds.

An exploration of challenges associated with machine learning for time series forecasting of COVID-19 community spread using wastewater-based epidemiological data

Wastewater-based epidemiology (WBE) has gained increasing attention as a complementary tool to conventional surveillance methods with potential for significant resource and labour savings when used for public health monitoring. Using WBE datasets to train machine learning algorithms and develop predictive models may also facilitate early warnings for the spread of outbreaks. The challenges associated with using machine learning for the analysis of WBE datasets and timeseries forecasting of COVID-19 were explored by running Random Forest (RF) algorithms on WBE datasets across 108 sites in five regions: Scotland, Catalonia, Ohio, the Netherlands, and Switzerland. This method uses measurements of SARS-CoV-2 RNA fragment concentration in samples taken at the inlets of wastewater treatment plants, providing insight into the prevalence of infection in upstream wastewater catchment populations. RF's forecasting performance at each site was quantitatively evaluated by determining mean absolute percentage error (MAPE) values, which was used to highlight challenges affecting future implementations of RF for WBE forecasting efforts. Performance was generally poor using WBE datasets from Catalonia, Scotland, and Ohio with 'reasonable' or better forecasts constituting 0 %, 5 %, and 0 % of these regions' forecasts, respectively. RF's performance was much stronger with WBE data from the Netherlands and Switzerland, which provided 55 % and 45 % 'reasonable' or better forecasts respectively. Sampling frequency and training set size were identified as key factors contributing to accuracy, while inclusion of too many unnecessary variables (or e.g., flow data) was identified as a contributing factor to poor performance. The contribution of catchment population on forecast accuracy was more ambiguous. This study determined that the factors governing RF's forecast performance are complicated and interrelated, which presents challenges for further work in this space. A sufficiently accurate further iteration of the tool discussed within this study would provide significant but varying value for public health departments for monitoring future, or ongoing outbreaks, assisting the implementation of on-time health response measures.

Using geographic information systems to link population estimates to wastewater surveillance data in New York State, USA

Sewer systems provide many services to communities that have access to them beyond removal of waste and wastewater. Understanding of these systems' geographic coverage is essential for wastewater-based epidemiology (WBE), which requires accurate estimates for the population contributing wastewater. Reliable estimates for the boundaries of a sewer service area or sewershed can be used to link upstream populations to wastewater samples taken at treatment plants or other locations within a sewer system. These geographic data are usually managed by public utilities, municipal offices, and some government agencies, however, there are no centralized databases for geographic information on sewer systems in New York State. We created a database for all municipal sewersheds in New York State for the purpose of supporting statewide wastewater surveillance efforts to support public health. We used a combination of public tax records with sewer access information, physical maps, and municipal records to organize and draw digital boundaries compatible with geographic information systems. The methods we employed to create these data will be useful to inform similar efforts in other jurisdictions and the data have many public health applications as well as being informative for water/environmental research and infrastructure projects.

Direct wastewater extraction as a simple and effective method for SARS-CoV-2 surveillance and COVID-19 community-level monitoring

Wastewater surveillance has proven to be an effective tool to monitor the transmission and emergence of infectious agents at a community scale. Workflows for wastewater surveillance generally rely on concentration steps to increase the probability of detection of low-abundance targets, but preconcentration can substantially increase the time and cost of analyses while also introducing additional loss of target during processing. To address some of these issues, we conducted a longitudinal study implementing a simplified workflow for SARS-CoV-2 detection from wastewater, using a direct column-based extraction approach. Composite influent wastewater samples were collected weekly for 1 year between June 2020 and June 2021 in Athens-Clarke County, Georgia, USA. Bypassing any concentration step, low volumes (280 µl) of influent wastewater were extracted using a commercial kit, and immediately analyzed by RT-qPCR for the SARS-CoV-2 N1 and N2 gene targets. SARS-CoV-2 viral RNA was detected in 76% (193/254) of influent samples, and the recovery of the surrogate bovine coronavirus was 42% (IQR: 28%, 59%). N1 and N2 assay positivity, viral concentration, and flow-adjusted daily viral load correlated significantly with per-capita case reports of COVID-19 at the county-level (ρ = 0.69-0.82). To compensate for the method's high limit of detection (approximately 106-107 copies l-1 in wastewater), we extracted multiple small-volume replicates of each wastewater sample. With this approach, we detected as few as five cases of COVID-19 per 100 000 individuals. These results indicate that a direct-extraction-based workflow for SARS-CoV-2 wastewater surveillance can provide informative and actionable results.

COVID-19 surveillance in wastewater: An epidemiological tool for the monitoring of SARS-CoV-2

The coronavirus disease 2019 (COVID-19) pandemic has prompted a lot of questions globally regarding the range of information about the virus's possible routes of transmission, diagnostics, and therapeutic tools. Worldwide studies have pointed out the importance of monitoring and early surveillance techniques based on the identification of viral RNA in wastewater. These studies indicated the presence of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) RNA in human feces, which is shed via excreta including mucus, feces, saliva, and sputum. Subsequently, they get dumped into wastewater, and their presence in wastewater provides a possibility of using it as a tool to help prevent and eradicate the virus. Its monitoring is still done in many regions worldwide and serves as an early "warning signal"; however, a lot of limitations of wastewater surveillance have also been identified.

Surveillance of SARS-CoV-2 in nine neighborhood sewersheds in Detroit Tri-County area, United States: Assessing per capita SARS-CoV-2 estimations and COVID-19 incidence

Wastewater-based epidemiology (WBE) has been suggested as a useful tool to predict the emergence and investigate the extent of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). In this study, we screened appropriate population biomarkers for wastewater SARS-CoV-2 normalization and compared the normalized SARS-CoV-2 values across locations with different demographic characteristics in southeastern Michigan. Wastewater samples were collected between December 2020 and October 2021 from nine neighborhood sewersheds in the Detroit Tri-County area. Using reverse transcriptase droplet digital polymerase chain reaction (RT-ddPCR), concentrations of N1 and N2 genes in the studied sites were quantified, with N1 values ranging from 1.92 × 102 genomic copies/L to 6.87 × 103 gc/L and N2 values ranging from 1.91 × 102 gc/L to 6.45 × 103 gc/L. The strongest correlations were observed with between cumulative COVID-19 cases per capita (referred as COVID-19 incidences thereafter), and SARS-CoV-2 concentrations normalized by total Kjeldahl nitrogen (TKN), creatinine, 5-hydroxyindoleacetic acid (5-HIAA) and xanthine when correlating the per capita SARS-CoV-2 and COVID-19 incidences. When SARS-CoV-2 concentrations in wastewater were normalized and compared with COVID-19 incidences, the differences between neighborhoods of varying demographics were reduced as compared to differences observed when comparing non-normalized SARS-CoV-2 with COVID-19 cases. This indicates when studying the disease burden in communities of different demographics, accurate per capita estimation is of great importance. The study suggests that monitoring selected water quality parameters or biomarkers, along with RNA concentrations in wastewater, will allow adequate data normalization for spatial comparisons, especially in areas where detailed sanitary sewage flows and contributing populations in the catchment areas are not available. This opens the possibility of using WBE to assess community infections in rural areas or the developing world where the contributing population of a sample could be unknown.

One-Year Surveillance of SARS-CoV-2 and Rotavirus in Water Matrices from a Hot Spring Area

The pandemic of Coronavirus Disease 2019 (COVID-19) caused by the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) is still impacting not only on human health but also all economic activities, especially in those related to tourism. In this study, in order to characterize the presence of SARS-CoV-2 in a hot spring park in Uruguay, swimming pools water, wastewater, and surface water from this area were analyzed by quantitative PCR. Wastewater from Salto city located next to the hydrothermal spring area was also evaluated as well as the presence of Rotavirus (RV). Overall, SARS-CoV-2 was detected in 13% (13/102) of the analyzed samples. Moreover, this virus was not detected in any of the samples from the swimming pools water and was present in 18% (3/17) of wastewater samples from the hotels area showing the same trend between the titer of SARS-CoV-2 and the number of infected people in Salto city. SARS-CoV-2 was also detected in wastewater samples (32% (11/34)) from Salto city, detecting the first positive sample when 105 persons were positive for SARS-CoV-2. Rotavirus was detected only in 10% (2/24) of the wastewater samples analyzed in months when partial lockdown measures were taken, however, this virus was detected in nearly all wastewater samples analyzed when social distancing measures and partial lockdown were relaxed. Wastewater results confirmed the advantages of using the detection and quantification of viruses in this matrix in order to evaluate the presence of these viruses in the population, highlighting the usefulness of this approach to define and apply social distancing. This study suggests that waters from swimming pools are not a source of infection for SARS-CoV-2, although more studies are needed including infectivity assays in order to confirm this statement.

SARS-CoV-2 in municipal wastewater treatment plant, collection network, and hospital wastewater

The current outbreak of coronavirus disease (COVID-19) has led to creating a public health emergency conditions since 2019. COVID-19, which is caused by SARS-CoV-2, is spread via human-to-human transmission by direct contact or droplets. Through conducting this study, we were looking for detecting SARS-CoV-2 in wastewater produced in Iran country (Ardabil, Nir, Khalkhal, and Kowsar) (wastewater collection network, wastewater treatment plant, and hospital wastewater). In this research, samples (n=76) were collected from influent and effluent of municipal and hospital wastewater treatment plants, and some samples were also collected from Ardabil municipal wastewater manholes. The sampling duration included the white (lower risk of COVID-19) and red (high risk of COVID-19) conditions. Samples were stored at -20 °C for further diagnostic tests. The specific primer and probe real-time reverse transcriptase-polymerase chain reaction (real-time PCR) targeting ORF1ab and N genes (nucleoprotein gene) were applied to detect viral genomes of the SARS-CoV-2 virus in the wastewater samples. Out of 76 samples, a total of 15 samples (19.73%) collected from wastewater in Ardabil province (Ardabil, Nir, Khalkhal, and Kowsar), were positive in terms of SARS-CoV-2. Wastewater epidemiology can facilitate detection of the incidence of pathogens through metropolises, measurement of population prevalence without direct testing, and provision of information to the public health system about the efficiency of intervening efforts. Graphical abstract.

Building-level wastewater surveillance of SARS-CoV-2 is associated with transmission and variant trends in a university setting

The University of South Carolina (UofSC) was among the first universities to include building-level wastewater surveillance of SARS-CoV-2 to complement clinical testing during its reopening in the Fall 2020 semester. In the Spring 2021 semester, 24h composite wastewater samples were collected twice per week from 10 residence halls and the on-campus student isolation and quarantine building. The isolation and quarantine building served as a positive control site. The wastewater was analyzed using RT-ddPCR for the quantification of nucleocapsid genes (N1 and N2) to identify viral transmission trends within residence halls. Log10 SARS-CoV-2 RNA concentrations were compared to both new clinical cases identified in the days following wastewater collection and recovered cases returning to sites during the days preceding sample collection to test temporal and spatial associations. There was a statistically significant positive relationship between the number of cases reported from the sites during the seven-day period following wastewater sampling and the log10 viral RNA copies/L (overall IRR 1.08 (1.02, 1.16) p-value 0.0126). Additionally, a statistically significant positive relationship was identified between the number of cases returning to the residence halls after completing isolation during the seven-day period preceding wastewater sampling and the log10 viral RNA copies/L (overall 1.09 (1.01, 1.17) p-value 0.0222). The statistical significance of both identified cases and recovered return cases on log10 viral RNA copies/L in wastewater indicates the importance of including both types of clinical data in wastewater-based epidemiology (WBE) research. Genetic mutations associated with variants of concern (VOCs) were also monitored. The emergence of the Alpha variant on campus was identified, which contributed to the second wave of COVID-19 cases at UofSC. The study was able to identify sub-community transmission hotspots for targeted intervention in real-time, making WBE cost-effective and creating less of a burden on the general public compared to repeated individual testing methods.

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.

Relationship between SARS-CoV-2 in wastewater and clinical data from five wastewater sheds

The Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) has resulted in a global pandemic starting in 2019 with nearly 500 million confirmed cases as of April 2022. Infection with SARS-CoV-2 is accompanied by shedding of virus in stool, and its presence in wastewater samples has been documented globally. Therefore, monitoring of SARS-CoV-2 in wastewater offers a promising approach to assess the pandemic situation covering pre-symptomatic and asymptomatic cases in areas with limited clinical testing. In this study, the presence of SARS-CoV-2 RNA in wastewater from five wastewater resource recovery facilities (WRRFs), located in two adjacent counties, was investigated and compared with the number of clinical COVID-19 cases during a 2020-2021 outbreak in United States. Statistical correlation analyses of SARS-CoV-2 viral abundance in wastewater and COVID-19 daily vs weekly clinical cases was performed. While a weak correlation on a daily basis was observed, this correlation improved when weekly clinical case data were applied. The viral fecal indicator Pepper Mild Mottle Virus (PMMoV) was furthermore used to assess the effects of normalization and the impact of dilution due to infiltration in the wastewater sheds. Normalization did not improve the correlations with clinical data. However, PMMoV provided important information about infiltration and presence of industrial wastewater discharge in the wastewater sheds. This study showed the utility of WBE to assist in public health responses to COVID-19, emphasizing that routine monitoring of large WRRFs could provide sufficient information for large-scale dynamics.

Biosensors for the detection of disease outbreaks through wastewater-based epidemiology

Wastewater-Based Epidemiology (WBE) is a novel community-wide monitoring tool that provides comprehensive real-time data of the public and environmental health status and can contribute to public health interventions, including those related to infectious disease outbreaks (e.g., the ongoing COVID-19 pandemic). Nonetheless, municipalities without centralized laboratories are likely still not able to process WBE samples. Biosensors are a potentially cost-effective solution to monitor the development of diseases through WBE to prevent local outbreaks. This review discusses the economic and technical feasibility of eighteen recently developed biosensors for the detection and monitoring of infectious disease agents in wastewater, prospecting the prevention of future pandemics.

Assessing wastewater-based epidemiology for the prediction of SARS-CoV-2 incidence in Catalonia

While wastewater-based epidemiology has proven a useful tool for epidemiological surveillance during the COVID-19 pandemic, few quantitative models comparing virus concentrations in wastewater samples and cumulative incidence have been established. In this work, a simple mathematical model relating virus concentration and cumulative incidence for full contagion waves was developed. The model was then used for short-term forecasting and compared to a local linear model. Both scenarios were tested using a dataset composed of samples from 32 wastewater treatment plants and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) incidence data covering the corresponding geographical areas during a 7-month period, including two contagion waves. A population-averaged dataset was also developed to model and predict the incidence over the full geography. Overall, the mathematical model based on wastewater data showed a good correlation with cumulative cases and allowed us to anticipate SARS-CoV-2 incidence in one week, which is of special relevance in situations where the epidemiological monitoring system cannot be fully implemented.

Wastewater-based epidemiology (WBE) for SARS-CoV-2 - A review focussing on the significance of the sewer network using a Dublin city catchment case study

Wastewater-based epidemiology (WBE) has been employed by many countries globally since the beginning of the COVID-19 pandemic in order to assess the benefits of this surveillance tool in the context of informing public health measures. WBE has been successfully employed to detect SARS-CoV-2 at wastewater treatment plants for community-wide surveillance, as well as in smaller catchments and institutions for targeted surveillance of COVID-19. In addition, WBE has been successfully used to detect new variants, identify areas of high infection levels, as well as to detect new infection outbreaks. However, due to to the large number of inherent uncertainties in the WBE process, including the inherent intricacies of the sewer network, decay of the virus en route to a monitoring point, levels of recovery from sampling and quantification methods, levels of faecal shedding among the infected population, as well as population normalisation methods, the usefulness of wastewater samples as a means of accurately quantifying SARS-CoV-2 infection levels among a population remains less clear. The current WBE programmes in place globally will help to identify new areas of research aimed at reducing the levels of uncertainty in the WBE process, thus improving WBE as a public health monitoring tool for future pandemics. In the meantime, such programmes can provide valuable comparisons to clinical testing data and other public health metrics, as well being an effective early warning tool for new variants and new infection outbreaks. This review includes a case study of sampled wastewater from the sewer network in Dublin, Ireland, during a peak infection period of COVID-19 in the city, which evaluates the different uncertainties in the WBE process.

Global public health implications of human exposure to viral contaminated water

Enteric viruses are common waterborne pathogens found in environmental water bodies contaminated with either raw or partially treated sewage discharge. Examples of these viruses include adenovirus, rotavirus, noroviruses, and other caliciviruses and enteroviruses like coxsackievirus and polioviruses. They have been linked with gastroenteritis, while some enteric viruses have also been implicated in more severe infections such as encephalitis, meningitis, hepatitis (hepatitis A and E viruses), cancer (polyomavirus), and myocarditis (enteroviruses). Therefore, this review presents information on the occurrence of enteric viruses of public health importance, diseases associated with human exposure to enteric viruses, assessment of their presence in contaminated water, and their removal in water and wastewater sources. In order to prevent illnesses associated with human exposure to viral contaminated water, we suggest the regular viral monitoring of treated wastewater before discharging it into the environment. Furthermore, we highlight the need for more research to focus on the development of more holistic disinfection methods that will inactivate waterborne viruses in municipal wastewater discharges, as this is highly needed to curtail the public health effects of human exposure to contaminated water. Moreover, such a method must be devoid of disinfection by-products that have mutagenic and carcinogenic potential.

Wastewater Sequencing-An Innovative Method for Variant Monitoring of SARS-CoV-2 in Populations

The SARS-CoV-2 outbreak has already affected more than 555 million people, and 6.3 million people have died. Due to its high infectivity, it is crucial to track SARS-CoV-2 outbreaks early to prevent the spread of infection. Wastewater monitoring appears to be a powerful and effective tool for managing epidemiological situations. Due to emerging mutations of SARS-CoV-2, there is a need to monitor mutations in order to control the pandemic. Since the sequencing of randomly chosen individuals is time-consuming and expensive, sequencing of wastewater plays an important role in revealing the dynamics of infection in a population. The sampling method used is a crucial factor and significantly impacts the results. Wastewater can be collected as a grab sample or as a 24 h composite sample. Another essential factor is the sample volume, as is the method of transport used. This review discusses different pretreatment procedures and RNA extraction, which may be performed using various methods, such as column-based extraction, TRIzol, or magnetic extraction. Each of the methods has its advantages and disadvantages, which are described accordingly. RT-qPCR is a procedure that confirms the presence of SARS-CoV-2 genes before sequencing. This review provides an overview of currently used methods for preparing wastewater samples, from sampling to sequencing.

Longitudinal metatranscriptomic sequencing of Southern California wastewater representing 16 million people from August 2020-21 reveals widespread transcription of antibiotic resistance genes

Municipal wastewater provides a representative sample of human fecal waste across a catchment area and contains a wide diversity of microbes. Sequencing wastewater samples provides information about human-associated and medically-important microbial populations, and may be useful to assay disease prevalence and antimicrobial resistance (AMR). Here, we present a study in which we used untargeted metatranscriptomic sequencing on RNA extracted from 275 sewage influent samples obtained from eight wastewater treatment plants (WTPs) representing approximately 16 million people in Southern California between August 2020 - August 2021. We characterized bacterial and viral transcripts, assessed metabolic pathway activity, and identified over 2,000 AMR genes/variants across all samples. Because we did not deplete ribosomal RNA, we have a unique window into AMR carried as ribosomal mutants. We show that AMR diversity varied between WTPs and that the relative abundance of many individual AMR genes/variants increased over time and may be connected to antibiotic use during the COVID-19 pandemic. Similarly, we detected transcripts mapping to human pathogenic bacteria and viruses suggesting RNA sequencing is a powerful tool for wastewater-based epidemiology and that there are geographical signatures to microbial transcription. We captured the transcription of gene pathways common to bacterial cell processes, including central carbon metabolism, nucleotide synthesis/salvage, and amino acid biosynthesis. We also posit that due to the ubiquity of many viruses and bacteria in wastewater, new biological targets for microbial water quality assessment can be developed. To the best of our knowledge, our study provides the most complete longitudinal metatranscriptomic analysis of a large population's wastewater to date and demonstrates our ability to monitor the presence and activity of microbes in complex samples. By sequencing RNA, we can track the relative abundance of expressed AMR genes/variants and metabolic pathways, increasing our understanding of AMR activity across large human populations and sewer sheds.

A systematic review on the occurrence, fate, and remediation of SARS-CoV-2 in wastewater

The COVID-19 has been declared a pandemic by the World Health Organization. Along with impairing the respiratory system, it also affects the gastrointestinal system. By reviewing experiments on the wastewater analysis for the detection of coronavirus, this study explores the fate, persistence, and various remediation strategies for the virus removal from the wastewater. The results indicated that the virus can be detected in the wastewater samples, feces, and sewage, even before the onset of symptoms. Coronavirus can be a potential panzootic disease, as several mammalian species get infected by the deadly virus. The disinfection strategies used earlier for the treatment of wastewater are not sufficient for the removal of viruses from the wastewater. Therefore, concerted efforts should be made to understand their fate, sources, and occurrence in the environmental matrices. To prevent the spread of the panzootic disease, revised guidelines should be issued for the remediation of the virus. Recent viral remediation methods such as membrane bioreactors and advanced oxidation methods can be used. Therefore, the present review puts a light on the current knowledge on the occurrence of coronaviruses in wastewater, the possible sources, fate, and removal strategies.

Quantification of infectious Human mastadenovirus in environmental matrices using PMAxx-qPCR

Molecular methodologies providing data on viral concentration and infectivity have been successfully used in environmental virology, supporting quantitative risk assessment studies. The present study aimed to assess human mastadenovirus (HAdV) intact particles using a derivative of propidium monoazide associated with qPCR (PMAxx-qPCR) in aquatic matrices. Initially, different concentrations of PMAxx were evaluated to establish an optimal protocol for treating different naturally contaminated matrices, using 10 min incubation in the dark at 200 rpm at room temperature and 15 min of photoactivation in the PMA-Lite™ LED photolysis device. There was no significant reduction in the quantification of infectious HAdV with increasing concentration of PMAxx used (20 μM, 50 μM, and 100 μM), except for sewage samples. In this matrix, a reduction of 5.01 log of genomic copies (GC)/L was observed from the concentration of 50 μM and revealed 100% HAdV particles with damaged capsids. On the other hand, the mean reduction of 0.51 log in stool samples using the same concentration mentioned above demonstrated 83% of damaged particles eliminated in the stool. Following, 50 μM PMAxx-qPCR protocol revealed a log reduction of 0.91, 0.67, and 1.05 in other samples of raw sewage, brackish, and seawater where HAdV concentration reached 1.47 × 10⁴, 6.81 × 10², and 2.33 × 10² GC/L, respectively. Fifty micrometers of PMAxx protocol helped screen intact viruses from different matrices, including sea and brackish water.

Monitoring SARS-CoV-2 in the Wastewater and Rivers of Tapachula, a Migratory Hub in Southern Mexico

The COVID-19 pandemic has been monitored by applying different strategies, including SARS-CoV-2 detection with clinical testing or through wastewater-based epidemiology (WBE). We used the latter approach to follow SARS-CoV-2 dispersion in Tapachula city, located in Mexico's tropical southern border region. Tapachula is a dynamic entry point for people seeking asylum in Mexico or traveling to the USA. Clinical testing facilities for SARS-CoV-2 monitoring are limited in the city. A total of eighty water samples were collected from urban and suburban rivers and sewage and a wastewater treatment plant over 4 months in Tapachula. We concentrated viral particles with a PEG-8000-based method, performed RNA extraction, and detected SARS-CoV-2 particles through RT-PCR. We considered the pepper mild mottle virus as a fecal water pollution biomarker and analytical control. SARS-CoV-2 viral loads (N1 and N2 markers) were quantified and correlated with official regional statistics of COVID-19 bed occupancy and confirmed cases (r > 91%). Our results concluded that WBE proved a valuable tool for tracing and tracking the COVID-19 pandemic in tropical countries with similar water temperatures (21-29 °C). Monitoring SARS-CoV-2 through urban and suburban river water sampling would be helpful in places lacking a wastewater treatment plant or water bodies with sewage discharges.

Multi-Faceted Approach to COVID-19 Surveillance at a Large University Campus in the Southeastern United States

The coronavirus disease 2019 (COVID-19) pandemic continues to present unique public health challenges both within the United States and across the globe. Institutions of higher learning are tasked with preventing and responding to COVID-19 on campus while also considering implications for the surrounding communities. The process of re-opening campus, whether at full or partial capacity, has tasked these institutions with overcoming complex challenges associated with balancing the resumption of campus operations while simultaneously protecting university affiliates and surrounding community members from COVID-19 through robust surveillance, contact tracing, and testing efforts. Here, we provide a concise outline related to the development and implementation of the comprehensive and sustainable COVID-19 surveillance program at the University of Florida. We also critically discuss the successes and pitfalls of this program while also providing recommendations for the development of similar programs in the future.