Surveillance of wastewater revealed peaks of SARS-CoV-2 preceding those of hospitalized patients with COVID-19

High prevalence of hepatitis E and rat hepatitis E viruses in wastewater in Gothenburg, Sweden

Hepatitis E virus (HEV) and Rat Hepatitis E virus (RHEV), recognized for their zoonotic potential, pose significant public health concerns. Our previous research identified both viruses in effluent wastewater in Gothenburg, Sweden. However, there are lingering inquiries regarding the prevalence and genetic diversity of these viruses in influent wastewater, as well as the utility of wastewater surveillance in elucidating their community circulation dynamics. To address these knowledge gaps, we conducted weekly collection of wastewater samples at the Rya wastewater treatment plant in Gothenburg throughout 2023. The concentrations of HEV and RHEV were quantified using quantitative polymerase chain reaction (qPCR). Additionally, two semi/nested-PCR were utilized to amplify viral strains. Furthermore, HEV strains from patients within the same region, as well as other regions in Sweden in 2023, were incorporated into the analysis. Remarkably, we observed a high prevalence of HEV (86%) and RHEV (98%) in wastewater samples, with the majority of HEV sequences identified as subtype 3c/i (9/12). In contrast, HEV subtype 3f was the most sequenced among clinical patient samples (6/12). Notably, previously unreported HEV-3b and unclassified strains were detected in wastewater. Almost all RHEV strains (20/21) were clustered into European groups, with none of the RHEV genetically close to strains previously found in human cases. The notable discordance in prevalence and identified subtypes of HEV-3 in wastewater compared to clinical samples suggests either a significant underdiagnosis of HEV infections or differences in viral loads and shedding durations among humans between HEV-3 subtypes. This underscores the urgent need for improved diagnostic techniques and heightened awareness of HEV transmission dynamics. Furthermore, the consistent detection of RHEV in wastewater underscores the necessity for further investigations to assess the potential role of RHEV in hepatitis cases of unknown etiology, given that most currently available clinical diagnostic assays fail to detect RHEV.

Are we using more sugar substitutes? Wastewater analysis reveals differences and rising trends in artificial sweetener usage in Swedish urban catchments

The market for artificial sweeteners as substitutes for conventional sugar (sucrose) is growing, despite potential health risks associated with their intake. Estimating population usage of artificial sweeteners is therefore crucial, and wastewater analysis can serve as a complement to existing methods. This study evaluated spatial and temporal usage of artificial sweeteners in five Swedish communities based on wastewater analysis. We further compared their levels measured in wastewater with the restrictions during the COVID-19 pandemic in Sweden and assessed health risks to the Swedish population. Influent wastewater samples (n = 194) collected in March 2019-February 2022 from communities in central and southern Sweden were analyzed for acesulfame, saccharin, and sucralose using liquid-chromatography coupled with tandem mass spectrometry. Spatial differences in loads for individual artificial sweetener were observed, with sucralose being higher in Kalmar (southern Sweden), and acesulfame and saccharin in Enköping and Östhammar (central Sweden). Based on sucrose equivalent doses, all communities showed a consistent prevalence pattern of sucralose > acesulfame > saccharin. Four communities with relatively short monitoring periods showed no apparent temporal changes in usage, but the four-year monitoring in Uppsala revealed a significant (p < 0.05) annual increase of ∼19 % for sucralose, ∼9 % for acesulfame and ∼8 % for saccharin. This trend showed no instant or delayed effects from COVID-19 restrictions, reflecting positively on the studied population which retained similar exposure to the artificial sweeteners despite potential pandemic stresses. Among the three artificial sweeteners, only acesulfame's levels were at the lower end of the health-related threshold for consumption of artificially sweetened beverages; yet, all were far below the acceptable daily intake, indicating no appreciable health risks. Our study provided valuable, pilot insights into the spatio-temporal usage of artificial sweeteners in Sweden and their associated health risks. This shows the usefulness of wastewater analysis for public health authorities wishing to assess future relevant interventions.

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

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

Measuring SARS-CoV-2 RNA concentrations in neighborhood wastewater

Longitudinal wastewater sampling during the COVID-19 pandemic was an important aspect of disease surveillance, adding to a more complete understanding of infection dynamics and providing important data for community public health monitoring and intervention planning. This was largely accomplished by testing SARS-CoV-2 RNA concentrations in samples from municipal wastewater treatment plants (WWTPs). We evaluated the utility of testing for virus levels upstream from WWTP within the residential neighborhoods that feed into the WWTP. We propose that monitoring virus dynamics across residential neighborhoods could reveal important public health-relevant information about community sub-group heterogeneity in virus concentrations. PRINCIPAL RESULTS: Virus concentration patterns display heterogeneity within neighborhoods and between neighborhoods over time. Sewage SARS-CoV-2 RNA concentrations as measured by RT-qPCR also corresponded closely to verified COVID-19 infection counts within individual neighborhoods. More importantly, our data suggest the loss of disease-relevant public health information when sampling occurs only at the level of WWTP instead of upstream in neighborhoods. Spikes in SARS-CoV-2 RNA concentrations in neighborhoods are often masked by dilution from other neighborhoods in the WWTP samples. MAJOR CONCLUSIONS: Wastewater-based epidemiology (WBE) employed at WWTP reliably detects SARS-CoV-2 in a city-sized population but provides less actionable public health information about neighborhoods experiencing greater viral infection and disease. Neighborhood sewershed sampling reveals important population-based information about local virus dynamics and improves opportunities for public health intervention. Longitudinally employed, neighborhood sewershed surveillance may provide a 3-6 day early warning of SARS-CoV-2 infection spikes and, importantly, highly specific information on subpopulations in a community particularly at higher risk at different points in time. Sampling in neighborhoods may thus provide timely and cost-saving information for targeted interventions within communities.

Wastewater based epidemiology as a surveillance tool during the current COVID-19 pandemic on a college campus (East Carolina University) and its accuracy in predicting SARS-CoV-2 outbreaks in dormitories

The COVID-19 outbreak led governmental officials to close many businesses and schools, including colleges and universities. Thus, the ability to resume normal campus operation required adoption of safety measures to monitor and respond to COVID-19. The objective of this study was to determine the efficacy of wastewater-based epidemiology as a surveillance method in monitoring COVID-19 on a college campus. The use of wastewater monitoring as part of a surveillance program to control COVID-19 outbreaks at East Carolina University was evaluated. During the Spring and Fall 2021 semesters, wastewater samples (N = 830) were collected every Monday, Wednesday, and Friday from the sewer pipes exiting the dormitories on campus. Samples were analyzed for SARS-CoV-2 and viral quantification was determined using qRT-PCR. During the Spring 2021 semester, there was a significant difference in SARS-CoV-2 virus copies in wastewater when comparing dorms with the highest number student cases of COVID-19 and those with the lowest number of student cases, (p = 0.002). Additionally, during the Fall 2021 semester it was observed that when weekly virus concentrations exceeded 20 copies per ml, there were new confirmed COVID-19 cases 85% of the time during the following week. Increases in wastewater viral concentration spurred COVID-19 swab testing for students residing in dormitories, aiding university officials in effectively applying COVID testing policies. This study showed wastewater-based epidemiology can be a cost-effective surveillance tool to guide other surveilling methods (e.g., contact tracing, nasal/salvia testing, etc.) to identify and isolate afflicted individuals to reduce the spread of pathogens and potential outbreaks within a community.

A modeling pipeline to relate municipal wastewater surveillance and regional public health data

As COVID-19 becomes endemic, public health departments benefit from improved passive indicators, which are independent of voluntary testing data, to estimate the prevalence of COVID-19 in local communities. Quantification of SARS-CoV-2 RNA from wastewater has the potential to be a powerful passive indicator. However, connecting measured SARS-CoV-2 RNA to community prevalence is challenging due to the high noise typical of environmental samples. We have developed a generalized pipeline using in- and out-of-sample model selection to test the ability of different correction models to reduce the variance in wastewater measurements and applied it to data collected from treatment plants in the Chicago area. We built and compared a set of multi-linear regression models, which incorporate pepper mild mottle virus (PMMoV) as a population biomarker, Bovine coronavirus (BCoV) as a recovery control, and wastewater system flow rate into a corrected estimate for SARS-CoV-2 RNA concentration. For our data, models with BCoV performed better than those with PMMoV, but the pipeline should be used to reevaluate any new data set as the sources of variance may change across locations, lab methods, and disease states. Using our best-fit model, we investigated the utility of RNA measurements in wastewater as a leading indicator of COVID-19 trends. We did this in a rolling manner for corrected wastewater data and for other prevalence indicators and statistically compared the temporal relationship between new increases in the wastewater data and those in other prevalence indicators. We found that wastewater trends often lead other COVID-19 indicators in predicting new surges.

Results from the SARS-CoV-2 wastewater-based surveillance system in Denmark, July 2021 to June 2022

The microbiological analysis of wastewater samples is increasingly used for the surveillance of SARS-CoV-2 globally. We described the setup process of the national SARS-CoV-2 wastewater-based surveillance system in Denmark, presented its main results during the first year of activities, from July 2021 to June 2022, and discussed their operational significance. The Danish SARS-CoV-2 wastewater-based surveillance system was designed to cover 85 % of the population in Denmark and it entailed taking three weekly samples from 230 sites. Samples were RT-qPCR tested for SARS-CoV-2 RNA, targeting the genetic markers N1, N2 and RdRp, and for two faecal indicators, Pepper Mild Mottle Virus and crAssphage. We calculated the weekly SARS-CoV-2 RNA concentration in the wastewater from each sampling site and monitored it in view of the results from individual testing, at the national and regional levels. We attempted to use wastewater results to identify potential local outbreaks, and we sequenced positive wastewater samples using Nanopore sequencing to monitor the circulation of viral variants in Denmark. The system reached its full implementation by October 2021 and covered up to 86.4 % of the Danish population. The system allowed for monitoring of the national and regional trends of SARS-CoV-2 infections in Denmark. However, the system contribution to the identification of potential local outbreaks was limited by the extensive information available from clinical testing. The sequencing of wastewater samples identified relevant variants of concern, in line with results from sequencing of human samples. Amidst the COVID-19 pandemic, Denmark implemented a nationwide SARS-CoV-2 wastewater-based surveillance system that integrated routine surveillance from individual testing. Today, while testing for COVID-19 at the community level has been discontinued, the system is on the frontline to monitor the occurrence and spread of SARS-CoV-2 in Denmark.

Beyond linear regression: Modeling COVID-19 clinical cases with wastewater surveillance of SARS-CoV-2 for the city of Athens and Ohio University campus

Wastewater-based surveillance has emerged as a detection tool for population-wide infectious diseases, including coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Infected individuals shed the virus, which can be detected in wastewater using molecular techniques such as reverse transcription-digital polymerase chain reaction (RT-dPCR). This study examined the association between the number of clinical cases and the concentration of SARS-CoV-2 in wastewater beyond linear regression and for various normalizations of viral loads. Viral loads were measured in a total of 446 wastewater samples during the period from August 2021 to April 2022. These samples were collected from nine different locations, with 220 samples taken from four specific sites within the city of Athens and 226 samples from five sites within Ohio University. The correlation between COVID-19 cases and wastewater viral concentrations, which was estimated using the Pearson correlation coefficient, was statistically significant and ranged from 0.6 to 0.9. In addition, time-lagged cross correlation was applied to identify the lag time between clinical and wastewater data, estimated 4 to 7 days. While we also explored the effect on the correlation coefficients of various normalizations of viral loads accounting for procedural loss or amount of fecal material and of estimated lag times, these alternative specifications did not change our substantive conclusions. Additionally, several linear and non-linear regression models were applied to predict the COVID-19 cases given wastewater data as input. The non-linear approach was found to yield the highest R-squared and Pearson correlation and lowest Mean Absolute Error values between the predicted and actual number of COVID-19 cases for both aggregated OHIO Campus and city data. Our results provide support for previous studies on correlation and time lag and new evidence that non-linear models, approximated with artificial neural networks, should be implemented for WBS of contagious diseases.

Clinically relevant sequence types of carbapenemase-producing Escherichia coli and Klebsiella pneumoniae detected in Finnish wastewater in 2021-2022

BACKGROUND

Antimicrobial resistance (AMR) is a critical threat to human health. Escherichia coli and Klebsiella pneumoniae are clinically the most important species associated with AMR and are the most common carbapenemase-producing (CP) Enterobacterales detected in human specimens in Finland. Wastewater surveillance has emerged as a potential approach for population-level surveillance of AMR, as wastewater could offer a reflection from a larger population with one sample and minimal recognized ethical issues. In this study, we investigated the potential of wastewater surveillance to detect CP E. coli and K. pneumoniae strains similar to those detected in human specimens.

METHODS

Altogether, 89 composite samples of untreated community wastewater were collected from 10 wastewater treatment plants across Finland in 2021-2022. CP E. coli and K. pneumoniae were isolated using selective culture media and identified using MALDI-TOF MS. Antimicrobial susceptibility testing was performed using disk diffusion test and broth microdilution method, and a subset of isolates was characterized using whole-genome sequencing.

RESULTS

CP E. coli was detected in 26 (29.2%) and K. pneumoniae in 25 (28.1%) samples. Among E. coli, the most common sequence type (ST) was ST410 (n = 7/26, 26.9%), while ST359 (n = 4/25, 16.0%) predominated among K. pneumoniae. Globally successful STs were detected in both E. coli (ST410, ST1284, ST167, and ST405) and K. pneumoniae (ST512, ST101, and ST307). K. pneumoniae carbapenemases (KPC) were the most common carbapenemases in both E. coli (n = 11/26, 42.3%) and K. pneumoniae (n = 13/25, 52.0%), yet also other carbapenemases, such as blaNDM-5, blaOXA-48, and blaOXA-181, were detected. We detected isolates harboring similar ST and enzyme type combinations previously linked to clusters in Finland, such as E. coli ST410 with blaKPC-2 and K. pneumoniae ST512 with blaKPC-3.

CONCLUSIONS

Our study highlights the presence of clinically relevant strains of CP E. coli and K. pneumoniae in community wastewater. The results indicate that wastewater surveillance could serve as a monitoring tool for CP Enterobacterales. However, the specificity and sensitivity of the methods should be improved, and technologies, like advanced sequencing methods, should be utilized to distinguish data with public health relevance, harness the full potential of wastewater surveillance, and implement the data in public health surveillance.

Development of highly sensitive one-step reverse transcription-quantitative PCR for SARS-CoV-2 detection in wastewater

The emergence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants is a major public health concern that has highlighted the need to monitor circulating strains to better understand the coronavirus disease 2019 (COVID-19) pandemic. This study was carried out to monitor SARS-CoV-2 RNA and its variant-specific mutations in wastewater using reverse transcription-quantitative polymerase chain reaction (RT-qPCR). One-step RT-qPCR using the SARS-CoV-2 Detection RT-qPCR Kit for Wastewater (Takara Bio), which amplified two N-gene regions simultaneously using CDC N1 and N2 assays with a single fluorescence dye, demonstrated better performance in detecting SARS-CoV-2 RNA (positive ratio, 66 %) compared to two-step RT-qPCR using CDC N1 or N2 assay (40 % each, and 52 % when combined), with significantly lower Ct values. The one-step RT-qPCR assay detected SARS-CoV-2 RNA in 59 % (38/64) of influent samples collected from a wastewater treatment plant in Japan between January 2021 and March 2022. The correlation between the concentration of SARS-CoV-2 RNA in the wastewater and the number of COVID-19 cases reported each day for 7 days pre- and post-sampling was significant (p < 0.05, r = 0.76 ± 0.03). Thirty-one influent samples which showed two-well positive for SARS-CoV-2 RNA were further tested by six mutations site-specific one-step RT-qPCR (E484K, L452R, N501Y, T478K, G339D, and E484A mutations). The N501Y mutation was detected between March and June 2021 but was replaced by the L452R and T478K mutations between July and October 2021, reflecting the shift from Alpha to Delta variants in the study region. The G339D and E484A mutations were identified in January 2022 and later when the incidence of the Omicron variant peaked. These findings indicate that wastewater-based epidemiology has the epidemiological potential to complement clinical tests to track the spread of COVID-19 and monitor variants circulating in communities.

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

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

Zooming in to the neighborhood level: A year-long wastewater-based epidemiology monitoring campaign for COVID-19 in small intraurban catchments

In recent years, wastewater-based epidemiology (WBE) has emerged as a valuable and cost-effective tool for monitoring the prevalence of COVID-19. Large-scale monitoring efforts have been implemented in numerous countries, primarily focusing on sampling at the entrance of wastewater treatment plants (WWTPs) to cover a large population. However, sampling at a finer spatial scale, such as at the neighborhood level (NGBs), pose new challenges, including the absence of composite sampling infrastructure and increased uncertainty due to the dynamics of small catchments. This study aims to investigate the feasibility and accuracy of WBE when deployed at the neighborhood level (sampling in sewers) compared to the city level (sampling at the entrance of a WWTP). To achieve this, we deployed specific WBE sampling stations at the intraurban scale within three NGBs in Barcelona, Spain. The study period covers the 5th and the 6th waves of COVID-19 in Spain, spanning from March 2021 to March 2022, along with the WWTP downstream from the NGBs. The results showed a strong correlation between the dynamics of COVID-19 clinical cases and wastewater SARS-CoV-2 loads at both the NGB and city levels. Notably, during the 5th wave, which was dominated by the Delta SARS-CoV-2 variant, wastewater loads were higher than during the 6th wave (Omicron variant), despite a lower number of clinical cases recorded during the 5th wave. The correlations between wastewater loads and clinical cases at the NGB level were stronger than at the WWTP level. However, the early warning potential varied across neighborhoods and waves, with some cases showing a one-week early warning and others lacking any significant early warning signal. Interestingly, the prevalence of COVID-19 did not exhibit major differences among NGBs with different socioeconomic statuses.

Optimal Selection of Sampling Points within Sewer Networks for Wastewater-Based Epidemiology Applications

Wastewater-based epidemiology (WBE) has great potential to monitor community public health, especially during pandemics. However, it faces substantial hurdles in pathogen surveillance through WBE, encompassing data representativeness, spatiotemporal variability, population estimates, pathogen decay, and environmental factors. This paper aims to enhance the reliability of WBE data, especially for early outbreak detection and improved sampling strategies within sewer networks. The tool implemented in this paper combines a monitoring model and an optimization model to facilitate the optimal selection of sampling points within sewer networks. The monitoring model utilizes parameters such as feces density and average water consumption to define the detectability of the virus that needs to be monitored. This allows for standardization and simplicity in the process of moving from the analysis of wastewater samples to the identification of infection in the source area. The entropy-based model can select optimal sampling points in a sewer network to obtain the most specific information at a minimum cost. The practicality of our tool is validated using data from Hildesheim, Germany, employing SARS-CoV-2 as a pilot pathogen. It is important to note that the tool's versatility empowers its extension to monitor other pathogens in the future.

SARS-CoV-2 removal in municipal wastewater treatment plants: Focus on conventional activated sludge, membrane bioreactor and anaerobic digestion

This work focuses on the removal of SARS-CoV-2 RNA in the various stages of a full-scale municipal WWTP characterised by two biological processes in parallel: (i) conventional activated sludge (CAS) and (ii) membrane bioreactor (MBR). The monitoring was carried out during the Omicron wave in 2022, a period characterised by a high concentration of SARS-CoV-2 in influent wastewater. The average concentration of SARS-CoV-2 in influent wastewater was 3.7 × 104 GU/L. In the primary sedimentation, the removal of SARS-CoV-2 was not appreciable. The largest log removal value of SARs-CoV-2 occurred in the biological stages, with 1.8 ± 0.9 and 2.2 ± 0.7 logs in CAS and MBR systems. The mean concentrations of SARS-CoV-2 in the CAS and MBR effluents were 6.8 × 102 GU/L and 6.4 × 102 GU/L, respectively. The MBR effluent showed more negative samples, because small particles are retained by membrane and cake layer. The analysis of the different types of sludge confirmed the accumulation of SARS-CoV-2 in primary (5.2 × 104 GU/L) and secondary sludge (3.5 × 104 GU/L), due to the affinity of enveloped viruses towards biosolids. A SARS-CoV-2 concentration in the digested sludge equal to 4.8 × 104 GU/L denotes a negligible reduction in the mesophilic anaerobic digester at temperature of 31-33 °C.

Bayesian monitoring of COVID-19 in Sweden

In an effort to provide regional decision support for the public healthcare, we design a data-driven compartment-based model of COVID-19 in Sweden. From national hospital statistics we derive parameter priors, and we develop linear filtering techniques to drive the simulations given data in the form of daily healthcare demands. We additionally propose a posterior marginal estimator which provides for an improved temporal resolution of the reproduction number estimate as well as supports robustness checks via a parametric bootstrap procedure. From our computational approach we obtain a Bayesian model of predictive value which provides important insight into the progression of the disease, including estimates of the effective reproduction number, the infection fatality rate, and the regional-level immunity. We successfully validate our posterior model against several different sources, including outputs from extensive screening programs. Since our required data in comparison is easy and non-sensitive to collect, we argue that our approach is particularly promising as a tool to support monitoring and decisions within public health. Significance: Using public data from Swedish patient registries we develop a national-scale computational model of COVID-19. The parametrized model produces valuable weekly predictions of healthcare demands at the regional level and validates well against several different sources. We also obtain critical epidemiological insights into the disease progression, including, e.g., reproduction number, immunity and disease fatality estimates. The success of the model hinges on our novel use of filtering techniques which allows us to design an accurate data-driven procedure using data exclusively from healthcare demands, i.e., our approach does not rely on public testing and is therefore very cost-effective.

Presence of SARS-CoV-2 virus in wastewater in the Kingdom of Bahrain during the COVID-19 pandemic

Background

Several countries, including Bahrain, used wastewater surveillance for disease activity monitoring. This study aimed to determine the presence of SARS-CoV-2 in untreated wastewater and to correlate it with the disease spread.

Methods

A retrospective review was conducted for all wastewater samples tested for SARS-CoV-2 in public health laboratories from November 2020 to October 2022. Samples were collected weekly between February and October 2022 from different areas across Bahrain. Real-time polymerase chain reaction was used to test for the presence of SARS-CoV-2 in wastewater, and the results were correlated with the number of COVID-19 cases in the same area.

Results

Of 387 wastewater samples, 103 (26.6%) samples tested positive for SARS-CoV-2. In late 2020, of 42 samples collected initially, four (9.5%) samples tested positive for SARS-CoV-2 in the four locations that hosted COVID-19 isolation facilities. Between February and October 2022, 345 specimens of wastewater were tested, and 99 (28.7%) were positive. The highest detection rate was in February, June, and July (60%, 45%, and 43%, respectively), which corresponded to COVID-19 peaks during 2022, and the lowest detection rate was in August and September (11% and 0%, respectively), corresponding to the low number of COVID-19 cases.

Conclusion

The detection rate of SARS-CoV-2 in wastewater samples from Bahrain was high and was significantly correlated with the number of reported COVID-19 cases. Wastewater surveillance can aid the existing surveillance system in monitoring SARS-CoV-2 spread.

Establishing a Statewide Wastewater Surveillance System in Response to the COVID-19 Pandemic: A Reliable Model for Continuous and Emerging Public Health Threats

CONTEXT

The COVID-19 pandemic sparked efforts across the globe to implement wastewater surveillance for SARS-CoV-2.

PROGRAM

New York State (NYS) established the NYS Wastewater Surveillance Network to estimate the levels of COVID-19 community risk and to provide an early indication of SARS-CoV-2 transmission trends. The network is designed to provide a better understanding of public health burdens and to assist health departments to respond effectively to public health threats.

IMPLEMENTATION

Wastewater surveillance across NYS increased from sporadic and geographically spare in 2020 to routine and widespread in 2022, reaching all 62 counties in the state and covering 74% of New Yorkers. The network team focused on engaging local health departments and wastewater treatment plants to provide wastewater samples, which are then analyzed through a network-affiliated laboratory. Both participating local health departments and wastewater treatment plants receive weekly memos on current SARS-CoV-2 trends and levels. The data are also made publicly available at the state dashboard.

EVALUATION

Using standard indicators to evaluate infectious disease surveillance systems, the NYS Wastewater Surveillance Network was assessed for accuracy, timeliness, and completeness during the first year of operations. We observed 96.5% sensitivity of wastewater to identify substantial/high COVID-19 transmission and 99% specificity to identify low COVID-19 transmission. In total, 80% of results were reported within 1 day of sample collection and were published on the public dashboard within 2 days of sample collection. Among participating wastewater treatment plants, 32.5% provided weekly samples with zero missing data, 31% missed 1 or 2 weeks, and 36.5% missed 3 or more weeks.

DISCUSSION

The NYS Wastewater Surveillance Network continues to be a key component of the state and local health departments' pandemic response. The network fosters prompt public health actions through real-time data, enhancing the preparedness capability for both existing and emerging public health threats.

Reduction of SARS-CoV-2 by biological nutrient removal and disinfection processes in full-scale wastewater treatment plants

Detection of SARS-CoV-2 RNA in wastewater poses people's concerns regarding the potential risk in water bodies receiving wastewater treatment effluent, despite the infectious risk of SARS-CoV-2 in wastewater being speculated to be low. Unlike well-studied nonenveloped viruses, SARS-CoV-2 in wastewater is present abundantly in both solid and liquid fractions of wastewater. Reduction of SARS-CoV-2 in past studies were likely underestimated, as SARS-CoV-2 in influent wastewater were quantified in either solid or liquid fraction only. The objectives of this study were (i) to clarify the reduction in SARS-CoV-2 RNA during biological nutrient removal and disinfection processes in full-scale WWTPs, considering the SARS-CoV-2 present in both solid and liquid fractions of wastewater, and (ii) to evaluate applicability of pepper mild mottle virus (PMMoV) as a performance indicator for reduction of SARS-CoV-2 in WWTPs. Accordingly, large amount of SARS-CoV-2 RNA were partitioned in the solid fraction of influent wastewater for composite sampling than grab sampling. When SARS-CoV-2 RNA in the both solid and liquid fractions were considered, log reduction values (LRVs) of SARS-CoV-2 during step-feed multistage biological nitrogen removal (SM-BNR) and enhanced biological phosphorus removal (EBPR) processes ranged between>2.1-4.4 log and did not differ significantly from those in conventional activated sludge (CAS). The LRVs of SARS-CoV-2 RNA in disinfection processes by ozonation and chlorination did not differ significantly. PMMoV is a promising performance indicator to secure reduction of SARS-CoV-2 in WWTPs, because of its higher persistence in wastewater treatment processes and abundance at a detectable concentration even in the final effluent after disinfection.

Measures against COVID-19 affected the spread of human enteric viruses in a Swedish community, as found when monitoring wastewater

The quantification of viral genomes in wastewater reflects the prevalence of viral infections within the community. Knowledge of how the spread of common enteric viruses in the community was affected by the Swedish COVID-19 interventions is limited. To investigate this, the weekly wastewater samples collected for monitoring SARS-CoV-2 throughout the COVID-19 pandemic at the Rya sewage treatment plant in Gothenburg were also analyzed for adenovirus, norovirus GII, astrovirus, and rotavirus. The amount of each viral genome was quantified by real-time-qPCR and compared with the quantity of these viral genomes in wastewater from 2017. The results showed that the winter seasonality of norovirus GII and rotavirus in wastewater observed in 2017 was interrupted shortly after the introduction of the COVID-19 interventions, and they remained at low level throughout the pandemic. The circulation pattern of astrovirus and adenovirus was less affected. When the COVID-19 restrictions were lifted in 2022, a dramatic increase was observed in the amount of norovirus GII, rotavirus, and adenovirus genomes in wastewater. The changes in abundance and seasonality of some viruses identified through wastewater monitoring were consistent with changes in the number of patients diagnosed with these viruses. These findings suggest that moderate intervention to prevent COVID-19 significantly reduced the spread of some enteric viruses in the community. The results show that wastewater monitoring is a valuable tool for detecting the spread and outbreaks of viral infections that may cause gastroenteritis also when people do not seek medical help, such as during the COVID-19 pandemic.

A critical assessment of SARS-CoV-2 in aqueous environment: Existence, detection, survival, wastewater-based surveillance, inactivation methods, and effective management of COVID-19

In early January 2020, the causal agent of unspecified pneumonia cases detected in China and elsewhere was identified as a novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and was the major cause of the COVID-19 outbreak. Later, the World Health Organization (WHO) proclaimed the COVID-19 pandemic a worldwide public health emergency on January 30, 2020. Since then, many studies have been published on this topic. In the present study, bibliometric analysis has been performed to analyze the research hotspots of the coronavirus. Coronavirus transmission, detection methods, potential risks of infection, and effective management practices have been discussed in the present review. Identification and quantification of SARS-CoV-2 viral loads in various water matrices have been reviewed. It was observed that the viral shedding through urine and feces of COVID-19-infected patients might be a primary mode of SARS-CoV-2 transmission in water and wastewater. In this context, the present review highlights wastewater-based epidemiology (WBE)/sewage surveillance, which can be utilized as an effective tool for tracking the transmission of COVID-19. This review also emphasizes the role of different disinfection techniques, such as chlorination, ultraviolet irradiation, and ozonation, for the inactivation of coronavirus. In addition, the application of computational modeling methods has been discussed for the effective management of COVID-19.

Co-incidence of BA.1 and BA.2 at the start of Singapore's Omicron wave revealed by Community and University Campus wastewater surveillance

Wastewater surveillance (WWS) has been globally recognised to be a useful tool in quantifying SARS-CoV-2 RNA at the community and residential levels without biases associated with case-reporting. The emergence of variants of concern (VOCs) have given rise to an unprecedented number of infections even though populations are increasingly vaccinated. This is because VOCs have been reported to possess higher transmissibility and can evade host immune responses. The B.1.1.529 lineage (Omicron) has severely disrupted global plans to return to normalcy. In this study, we developed an allele-specific (AS) RT-qPCR assay which simultaneously targets the stretch of deletions and mutations in the spike protein from position 24-27 for quantitative detection of Omicron BA.2. Together with previous assays that detect mutations associated with Omicron BA.1 (deletion at position 69 and 70) and all Omicron (mutation at position 493 and 498), we report the validation and time series of these assays from September 2021 to May 2022 using influent samples from two wastewater treatment plants and across four University campus sites in Singapore. Viral RNA concentrations at the treatment plants corroborate with locally reported clinical cases, AS RT-qPCR assays revealed co-incidence of Omicron BA.1 and BA.2 on 12 January 2022, almost two months after initial BA.1 detection in South Africa and Botswana. BA.2 became the dominant variant by the end of January 2022 and completely displaced BA.1 by mid-March 2022. University campus sites were similarly positive for BA.1 and/or BA.2 in the same week as first detection at the treatment plants, where BA.2 became rapidly established as the dominant lineage within three weeks. These results corroborate clinical incidence of the Omicron lineages in Singapore and indicate minimal silent circulation prior to January 2022. The subsequent simultaneous spread of both variant lineages followed strategic relaxation of safe management measures upon meeting nationwide vaccination goals.

Comparative evaluation of advanced oxidation processes (AOPs) for reducing SARS-CoV-2 viral load from campus sewage water

Presence of SARS-CoV-2 in wastewater is a major concern as the wastewater meets rivers and other water bodies and is used by the population for various purposes. Hence it is very important to treat sewage water in an efficient manner in order to reduce the public health risk. In the present work, various advanced oxidation processes (AOPs) have been evaluated for disinfection of SARS-CoV-2 from sewage water collected from STP inlet of academic institutional residential. The sewage water was subjected to ten AOPs, which include Ozone (O3), Hydrodynamic cavitation (HC), Ultraviolet radiation (UV), and their hybrid combinations like HC/O3, HC/O3/H2O2, HC/H2O2, O3/UV, UV/H2O2, UV/H2O2/O3, and O3/H2O2 to reduce SARS-CoV-2 viral load. Further, AOP treated sewage water was subjected to total nucleic acid isolation followed by RT-qPCR for viral load estimation. The sewage water treatment techniques were evaluated based on their viral concentration-reducing efficiency. It was found that ozone and ozone-coupled hybrid AOPs showed the most promising result with more than 98 % SARS-CoV-2 viral load reducing efficiency from sewage water. Interestingly, the best six AOPs used in this study significantly reduced both the SARS-CoV-2 and PMMoV (faecal indicator) viral load and improved water quality in terms of increasing DO and decreasing TOC.

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

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

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.

Temporal Series Analysis of Population Cycle Threshold Counts as a Predictor of Surge in Cases and Hospitalizations during the SARS-CoV-2 Pandemic

Tools to predict surges in cases and hospitalizations during the COVID-19 pandemic may help guide public health decisions. Low cycle threshold (CT) counts may indicate greater SARS-CoV-2 concentrations in the respiratory tract, and thereby may be used as a surrogate marker of enhanced viral transmission. Several population studies have found an association between the oscillations in the mean CT over time and the evolution of the pandemic. For the first time, we applied temporal series analysis (Granger-type causality) to validate the CT counts as an epidemiological marker of forthcoming pandemic waves using samples and analyzing cases and hospital admissions during the third pandemic wave (October 2020 to May 2021) in Madrid. A total of 22,906 SARS-CoV-2 RT-PCR-positive nasopharyngeal swabs were evaluated; the mean CT value was 27.4 (SD: 2.1) (22.2% below 20 cycles). During this period, 422,110 cases and 36,727 hospital admissions were also recorded. A temporal association was found between the CT counts and the cases of COVID-19 with a lag of 9-10 days (p ≤ 0.01) and hospital admissions by COVID-19 (p < 0.04) with a lag of 2-6 days. According to a validated method to prove associations between variables that change over time, the short-term evolution of average CT counts in the population may forecast the evolution of the COVID-19 pandemic.

Early detection of local SARS-CoV-2 outbreaks by wastewater surveillance: a feasibility study

Wastewater surveillance and quantitative analysis of SARS-CoV-2 RNA are increasingly used to monitor the spread of COVID-19 in the community. We studied the feasibility of applying the surveillance data for early detection of local outbreaks. A Monte Carlo simulation model was constructed, applying data on reported variation in RNA gene copy concentration in faeces and faecal masses shed. It showed that, even with a constant number of SARS-CoV-2 RNA shedders, the variation in concentrations found in wastewater samples will be large, and that it will be challenging to translate viral concentrations into incidence estimates, especially when the number of shedders is low. Potential signals for early detection of hypothetical outbreaks were analysed for their performance in terms of sensitivity and specificity of the signals. The results suggest that a sudden increase in incidence is not easily identified on the basis of wastewater surveillance data, especially in small sampling areas and in low-incidence situations. However, with a high number of shedders and when combining data from multiple consecutive tests, the performance of wastewater sampling is expected to improve considerably. The developed modelling approach can increase our understanding of the results from wastewater surveillance of SARS-CoV-2.

A simple SEIR-V model to estimate COVID-19 prevalence and predict SARS-CoV-2 transmission using wastewater-based surveillance data

Wastewater-based surveillance (WBS) has been widely used as a public health tool to monitor SARS-CoV-2 transmission. However, epidemiological inference from WBS data remains understudied and limits its application. In this study, we have established a quantitative framework to estimate COVID-19 prevalence and predict SARS-CoV-2 transmission through integrating WBS data into an SEIR-V model. We conceptually divide the individual-level viral shedding course into exposed, infectious, and recovery phases as an analogy to the compartments in a population-level SEIR model. We demonstrated that the effect of temperature on viral losses in the sewer can be straightforwardly incorporated in our framework. Using WBS data from the second wave of the pandemic (Oct 02, 2020-Jan 25, 2021) in the Greater Boston area, we showed that the SEIR-V model successfully recapitulates the temporal dynamics of viral load in wastewater and predicts the true number of cases peaked earlier and higher than the number of reported cases by 6-16 days and 8.3-10.2 folds (R = 0.93). This work showcases a simple yet effective method to bridge WBS and quantitative epidemiological modeling to estimate the prevalence and transmission of SARS-CoV-2 in the sewershed, which could facilitate the application of wastewater surveillance of infectious diseases for epidemiological inference and inform public health actions.

Not a waste: Wastewater surveillance to enhance public health

Domestic wastewater, when collected and evaluated appropriately, can provide valuable health-related information for a community. As a relatively unbiased and non-invasive approach, wastewater surveillance may complement current practices towards mitigating risks and protecting population health. Spurred by the COVID-19 pandemic, wastewater programs are now widely implemented to monitor viral infection trends in sewersheds and inform public health decision-making. This review summarizes recent developments in wastewater-based epidemiology for detecting and monitoring communicable infectious diseases, dissemination of antimicrobial resistance, and illicit drug consumption. Wastewater surveillance, a quickly advancing Frontier in environmental science, is becoming a new tool to enhance public health, improve disease prevention, and respond to future epidemics and pandemics.

Understanding the dynamic relation between wastewater SARS-CoV-2 signal and clinical metrics throughout the pandemic

Wastewater surveillance (WWS) of SARS-CoV-2 was proven to be a reliable and complementary tool for population-wide monitoring of COVID-19 disease incidence but was not as rigorously explored as an indicator for disease burden throughout the pandemic. Prior to global mass immunization campaigns and during the spread of the wildtype COVID-19 and the Alpha variant of concern (VOC), viral measurement of SARS-CoV-2 in wastewater was a leading indicator for both COVID-19 incidence and disease burden in communities. As the two-dose vaccination rates escalated during the spread of the Delta VOC in Jul. 2021 through Dec. 2021, relations weakened between wastewater signal and community COVID-19 disease incidence and maintained a strong relationship with clinical metrics indicative of disease burden (new hospital admissions, ICU admissions, and deaths). Further, with the onset of the vaccine-resistant Omicron BA.1 VOC in Dec. 2021 through Mar. 2022, wastewater again became a strong indicator of both disease incidence and burden during a period of limited natural immunization (no recent infection), vaccine escape, and waned vaccine effectiveness. Lastly, with the populations regaining enhanced natural and vaccination immunization shortly prior to the onset of the Omicron BA.2 VOC in mid-Mar 2022, wastewater is shown to be a strong indicator for both disease incidence and burden. Hospitalization-to-wastewater ratio is further shown to be a good indicator of VOC virulence when widespread clinical testing is limited. In the future, WWS is expected to show moderate indication of incidence and strong indication of disease burden in the community during future potential seasonal vaccination campaigns.

Removal performance of SARS-CoV-2 in wastewater treatment by membrane bioreactor, anaerobic-anoxic-oxic, and conventional activated sludge processes

The potential risk of SARS-CoV-2 in treated effluent from a wastewater treatment plant (WWTP) is concerned since SARS-CoV-2 is contained in wastewater during the COVID-19 outbreak. However, the removal of SARS-CoV-2 in WWTP has not been well investigated. The objectives of this study were (i) to clarify the removal performance of SARS-CoV-2 during wastewater treatment, (ii) to compare the removal performance of different secondary treatment processes, and (iii) to evaluate applicability of pepper mild mottle of virus (PMMoV) as a performance indicator for the reduction of SARS-CoV-2 RNA in wastewater treatment. Influent wastewater, secondary-treatment effluent (before chlorination), and final effluent (after chlorination) samples were collected from a WWTP from May 28 to September 24, 2020, during the COVID-19 outbreak in Japan. The target WWTP had three parallel treatment systems employing conventional activated sludge (CAS), anaerobic-anoxic -oxic (A2O), and membrane bioreactor (MBR) processes. SARS-CoV-2 in both the liquid and solid fractions of the influent wastewater was concentrated and quantified using RT-qPCR. SARS-CoV-2 in treated effluent was concentrated from 10 L samples to achieve a detection limit as low as 10 copies/L. The log reduction value (LRV) of SARS-CoV-2 was 2.7 ± 0.86 log10 in CAS, 1.6 ± 0.50 log10 in A2O, and 3.6 ± 0.62 log10 in MBR. The lowest LRV observed during the sampling period was 2.8 log10 in MBR, 1.2 log10 in CAS, and 1.0 log10 in A2O process, indicating that the MBR had the most stable reduction performance. PMMoV was found to be a good indicator virus to evaluate reduction performance of SARS-CoV-2 independent of the process configuration because the LRV of PMMoV was significantly lower than that of SARS-CoV-2 in the CAS, A2O and MBR processes.

A State-of-the-Art Scoping Review on SARS-CoV-2 in Sewage Focusing on the Potential of Wastewater Surveillance for the Monitoring of the COVID-19 Pandemic

The outbreak of coronavirus infectious disease-2019 (COVID-19), caused by the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), has rapidly spread throughout the world. Several studies have shown that detecting SARS-CoV-2 in untreated wastewater can be a useful tool to identify new outbreaks, establish outbreak trends, and assess the prevalence of infections. On 06 May 2021, over a year into the pandemic, we conducted a scoping review aiming to summarize research data on SARS-CoV-2 in sewage. Papers dealing with raw sewage collected at wastewater treatment plants, sewer networks, septic tanks, and sludge treatment facilities were included in this review. We also reviewed studies on sewage collected in community settings such as private or municipal hospitals, healthcare facilities, nursing homes, dormitories, campuses, airports, aircraft, and cruise ships. The literature search was conducted using the electronic databases PubMed, EMBASE, and Web Science Core Collection. This comprehensive research yielded 1090 results, 66 of which met the inclusion criteria and are discussed in this review. Studies from 26 countries worldwide have investigated the occurrence of SARS-CoV-2 in sewage of different origin. The percentage of positive samples in sewage ranged from 11.6 to 100%, with viral concentrations ranging from ˂LOD to 4.6 × 108 genome copies/L. This review outlines the evidence currently available on wastewater surveillance: (i) as an early warning system capable of predicting COVID-19 outbreaks days or weeks before clinical cases; (ii) as a tool capable of establishing trends in current outbreaks; (iii) estimating the prevalence of infections; and (iv) studying SARS-CoV-2 genetic diversity. In conclusion, as a cost-effective, rapid, and reliable source of information on the spread of SARS-CoV-2 and its variants in the population, wastewater surveillance can enhance genomic and epidemiological surveillance with independent and complementary data to inform public health decision-making during the ongoing pandemic.

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

BACKGROUND

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

OBJECTIVES

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

DISCUSSION

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

Molecular Monitoring of SARS-CoV-2 in Different Sewage Plants in Venice and the Implications for Genetic Surveillance

Wastewater-based epidemiology is now widely used as an indirect tool to monitor the spread of SARS-CoV-2. In this study, five different sample matrices representing diverse phases of the wastewater treatment process were collected during the second wave of SARS-CoV-2 from two wastewater treatment plants (WWTPs) serving the Civil Hospital and Sacca Fisola island in Venice, Italy. Positive SARS-CoV-2 detections occurred at both WWTPs, and data on viral genome detection rate and quantification suggest that the pellet (i.e., the particulate resulting from the influent) is a sensitive matrix that permits reliable assessment of infection prevalence while reducing time to results. On the contrary, analysis of post-treatment matrices provides evidence of the decontamination efficacy of both WWTPs. Finally, direct sequencing of wastewater samples enabled us to identify B.1.177 and B.1.160 as the prevalent SARS-CoV-2 lineages circulating in Venice at the time of sampling. This study confirmed the suitability of wastewater testing for studying SARS-CoV-2 circulation and established a simplified workflow for the prompt detection and characterization of the virus.

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.

Development of forecast models for COVID-19 hospital admissions using anonymized and aggregated mobile network data

Reliable forecast of COVID-19 hospital admissions in near-term horizons can help enable effective resource management which is vital in reducing pressure from healthcare services. The use of mobile network data has come to attention in response to COVID-19 pandemic leveraged on their ability in capturing people social behavior. Crucially, we show that there are latent features in irreversibly anonymized and aggregated mobile network data that carry useful information in relation to the spread of SARS-CoV-2 virus. We describe development of the forecast models using such features for prediction of COVID-19 hospital admissions in near-term horizons (21 days). In a case study, we verified the approach for two hospitals in Sweden, Sahlgrenska University Hospital and Södra Älvsborgs Hospital, working closely with the experts engaged in the hospital resource planning. Importantly, the results of the forecast models were used in year 2021 by logisticians at the hospitals as one of the main inputs for their decisions regarding resource management.

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.

An opinion on Wastewater-Based Epidemiological Monitoring (WBEM) with Clinical Diagnostic Test (CDT) for detecting high-prevalence areas of community COVID-19 Infections

Wastewater-Based Epidemiological Monitoring (WBEM) is an efficient surveillance tool during the COVID-19 pandemic as it meets all requirements of a complete monitoring system including early warning, tracking the current trend, prevalence of the disease, detection of genetic diversity as well asthe up-surging SARS-CoV-2 new variants with mutations from the wastewater samples. Subsequently, Clinical Diagnostic Test is widely acknowledged as the global gold standard method for disease monitoring, despite several drawbacks such as high diagnosis cost, reporting bias, and the difficulty of tracking asymptomatic patients (silent spreaders of the COVID-19 infection who manifest nosymptoms of the disease). In this current reviewand opinion-based study, we first propose a combined approach) for detecting COVID-19 infection in communities using wastewater and clinical sample testing, which may be feasible and effective as an emerging public health tool for the long-term nationwide surveillance system. The viral concentrations in wastewater samples can be used as indicatorsto monitor ongoing SARS-CoV-2 trends, predict asymptomatic carriers, and detect COVID-19 hotspot areas, while clinical sampleshelp in detecting mostlysymptomaticindividuals for isolating positive cases in communities and validate WBEM protocol for mass vaccination including booster doses for COVID-19.

Wastewater-based epidemiology: A Brazilian SARS-COV-2 surveillance experience

Since 2020, developed countries have rapidly shared both publicly and academically relevant wastewater surveillance information. Data on SARS-CoV-2 circulation is pivotal for guiding public health policies and improving the COVID-19 pandemic response. Conversely, low- and middle-income countries, such as Latin America and the Caribbean, showed timid activities in the Wastewater-Based Epidemiology (WBE) context. In these countries, isolated groups perform viral wastewater monitoring, and the data are unevenly shared or accessible to health agencies and the scientific community. This manuscript aims to highlight the relevance of a multiparty effort involving research, public health, and governmental agencies to support usage of WBE methodology to its full potential during the COVID-19 pandemic as part of a joint One Health surveillance approach. Thus, in this study, we explored the results obtained from wastewater surveillance in different regions of Brazil as a part of the COVID-19 Wastewater Monitoring Network ANA (National Water Agency), MCTI (Ministry of Science, Technology, and Innovations) and MS (Ministry of Health). Over the epidemiological weeks of 2021 and early 2022, viral RNA concentrations in wastewater followed epidemiological trends and variations. The highest viral loads in wastewater samples were detected during the second Brazilian wave of COVID-19. Corroborating international reports, our experience demonstrated usefulness of the WBE approach in viral surveillance. Wastewater surveillance allows hotspot identification, and therefore, early public health interventions. In addition, this methodology allows tracking of asymptomatic and oligosymptomatic individuals, who are generally underreported, especially in emerging countries with limited clinical testing capacity. Therefore, WBE undoubtedly contributes to improving public health responses in the context of this pandemic, as well as other sanitary emergencies.

Global occurrence of SARS-CoV-2 in environmental aquatic matrices and its implications for sanitation and vulnerabilities in Brazil and developing countries

This paper includes a systematic review of the SARS-CoV-2 occurrence in environmental aquatic matrices and a critical sanitation analysis. We discussed the interconnection of sanitation services (wastewater, water supply, solid waste, and stormwater drainage) functioning as an important network for controlling the spread of SARS-CoV-2 in waters. We collected 98 studies containing data of the SARS-CoV-2 occurrence in aquatic matrices around the world, of which 40% were from developing countries. Alongside a significant number of people infected by the virus, developing countries face socioeconomic deficiencies and insufficient public investment in infrastructure. Therefore, our study focused on highlighting solutions to provide sanitation in developing countries, considering the virus control in waters by disinfection techniques and sanitary measures, including alternatives for the vulnerable communities. The need for multilateral efforts to improve the universal coverage of sanitation services demands urgent attention in a pandemic scenario.

Effectiveness of building-level sewage surveillance during both community-spread and sporadic-infection phases of SARS-CoV-2 in a university campus population

Pathogen surveillance within wastewater rapidly progressed during the SARS-CoV-2 pandemic and informed public health management. In addition to the successful monitoring of entire sewer catchment basins at the treatment facility scale, subcatchment or building-level monitoring enabled targeted support of resource deployment. However, optimizing the temporal and spatial resolution of these monitoring programs remains complex due to population dynamics and within-sewer physical, chemical, and biological processes. To address these limitations, this study explores the advancement of the building-scale network that monitored the on-campus residential population at the University of Colorado Boulder between August 2020 and May 2021 through a daily SARS-CoV-2 surveillance campaign. During the study period, SARS-CoV-2 infection prevalence transitioned from robust community spread in Fall 2020 to sporadic infections in Spring 2021. Temporally, these distinct phases enabled investigating the effectiveness of resource commitment by exploring subsets of the original daily sampling data. Spatially, select sampling sites were installed along the flow path of the pipe network, enabling the exploration of the conservation of viral concentrations within the wastewater. Infection prevalence and resource commitment for informed action displayed an inverted relationship: higher temporal and spatial resolution surveillance is more imperative during sporadic infection phases than during high prevalence periods. This relationship was reinforced when norovirus (two minor clusters) and influenza (primarily absent) were additionally surveilled at a weekly frequency. Overall, resource commitment should scale to meet the objectives of the monitoring campaign-providing a general prevalence estimate requires fewer resources than an early-warning and targeted-action monitoring framework.

Leveraging water-wastewater data interdependencies to understand infrastructure systems' behaviors during COVID-19 pandemic

Social distancing policies (SDPs) implemented worldwide in response to COVID-19 pandemic have led to spatiotemporal variations in water demand and wastewater flow, creating potential operational and service-related quality issues in water-sector infrastructure. Understanding water-demand variations is especially challenging in contexts with limited availability of smart meter infrastructure, hindering utilities' ability to respond in real time to identified system vulnerabilities. Leveraging water and wastewater infrastructures' interdependencies, this study proposes the use of high-granular wastewater-flow data as a proxy to understand both water and wastewater systems' behaviors during active SDPs. Enabled by a random-effects model of wastewater flow in an urban metropolitan city in Texas, we explore the impacts of various SDPs (e.g., stay home-work safe, reopening phases) using daily flow data gathered between March 19, 2019, and December 31, 2020. Results indicate an increase in residential flow that offset a decrease in nonresidential flow, demonstrating a spatial redistribution of wastewater flow during the stay home-work safe period. Our results show that the three reopening phases had statistically significant relationships to wastewater flow. While this yielded only marginal net effects on overall wastewater flow, it serves as an indicator of behavioral changes in water demand at sub-system spatial scales given demand-flow interdependencies. Our assessment should enable utilities without smart meters in their water system to proactively target their operational response during pandemics, such as (1) monitoring wastewater-flow velocity to alleviate potential blockages in sewer pipes in case of decreased flows, and (2) closely investigating any consequential water-quality problems due to decreased demands.

Assessing population-level stress through glucocorticoid hormone monitoring in wastewater

Stress is oftentimes overlooked in societies, despite its life-threatening impact. Here, we assessed the feasibility of measuring endogenous stress hormones to estimate population-level stress by wastewater-based epidemiology (WBE). Two primary glucocorticoids, cortisol and cortisone, were monitored in wastewater by liquid chromatography tandem mass spectrometry (LC-MS/MS), to assess changes in these physiological markers of stress in a student population (n = 26,000 ± 7100) on a university campus in the southwestern U.S. Daily composite samples were collected for seven consecutive days each month during the Fall (Autumn) 2017 and Spring 2018 academic semesters (n = 134). Reproducible weekly patterns were seen in stress hormone excretion, with the highest levels occurring on Mondays (124 ± 44 μg d^-1 per person) and Tuesdays (127 ± 54 μg d^-1 per person) and the lowest on Sundays (87 ± 32 μg d^-1 per person). Stress levels on weekdays (defined by class schedules Monday-Thursday) were significantly higher than on weekends (p < 0.05). During both Fall and Spring semesters, per person stress levels of these hormones were significantly higher (p < 0.05) during the first two months of each semester, 162 ± 28 μg d^-1 per person (August), 104 ± 29 μg d^-1 per person (September), 180 ± 14 μg d^-1 per person (January), and 114 ± 54 μg d^-1 per person (February) than in the remaining measured weeks in the semester, including finals week captured in both semesters. Overall Spring semester stress levels (113 ± 45 μg d^-1 per person) were significantly higher than the Fall (94 ± 42 μg d^-1 per person), p < 0.01. This study is the first to demonstrate the utility of endogenous biomarkers, specifically glucocorticoid hormones, to monitor population health status (in this instance community stress) in near real-time by wastewater assessments.

Assessment of two types of passive sampler for the efficient recovery of SARS-CoV-2 and other viruses from wastewater

Wastewater-based epidemiology (WBE) has proven to be a useful surveillance tool during the ongoing SARS-CoV-2 pandemic, and has driven research into evaluating the most reliable and cost-effective techniques for obtaining a representative sample of wastewater. When liquid samples cannot be taken efficiently, passive sampling approaches have been used, however, insufficient data exists on their usefulness for multi-virus capture and recovery. In this study, we compared the virus-binding capacity of two passive samplers (cotton-based tampons and ion exchange filter papers) in two different water types (deionised water and wastewater). Here we focused on the capture of wastewater-associated viruses including Influenza A and B (Flu-A & B), SARS-CoV-2, human adenovirus (AdV), norovirus GII (NoVGII), measles virus (MeV), pepper mild mottle virus (PMMoV), the faecal marker crAssphage and the process control virus Pseudomonas virus phi6. After deployment, we evaluated four different methods to recover viruses from the passive samplers namely, (i) phosphate buffered saline (PBS) elution followed by polyethylene glycol (PEG) precipitation, (ii) beef extract (BE) elution followed by PEG precipitation, (iii) no-elution into PEG precipitation, and (iv) direct extraction. We found that the tampon-based passive samplers had higher viral recoveries in comparison to the filter paper. Overall, the preferred viral recovery method from the tampon passive samplers was the no-elution/PEG precipitation method. Furthermore, we evidenced that non-enveloped viruses had higher percent recoveries from the passive samplers than enveloped viruses. This is the first study of its kind to assess passive sampler and viral recovery methods amongst a plethora of viruses commonly found in wastewater or used as a viral surrogate in wastewater studies.

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.

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.

Predicting the dispersal of SARS-CoV-2 RNA from the wastewater treatment plant to the coast

Viral pathogens including SARS-CoV-2 RNA have been detected in wastewater treatment effluent, and untreated sewage overflows, that pose an exposure hazard to humans. We assessed whether SARS-CoV-2 RNA was likely to have been present in detectable quantities in UK rivers and estuaries during the first wave of the Covid-19 pandemic. We simulated realistic viral concentrations parameterised on the Camel and Conwy catchments (UK) and their populations, showing detectable SARS-CoV-2 RNA concentrations for untreated but not for treated loading, but also being contingent on viral decay, hydrology, catchment type/shape, and location. Under mean or low river flow conditions, viral RNA concentrated within the estuaries allowing for viral build-up and caused a lag by up to several weeks between the peak in community infections and the viral peak in the environment. There was an increased hazard posed by SARS-CoV-2 RNA with a T 90 decay rate >24 h, as the estuarine build-up effect increased. High discharge events transported the viral RNA downstream and offshore, increasing the exposure risk to coastal bathing waters and shellfisheries - although dilution in this case reduced viral concentrations well below detectable levels. Our results highlight the sensitivity of exposure to viral pathogens downstream of wastewater treatment, across a range of viral loadings and catchment characteristics - with implications to environmental surveillance.

Examining challenges and multi-strategic approaches in waste management during the COVID-19 pandemic: A systematic review

The COVID-19 pandemic has a negative impact on the environment. Waste generation and improper management during the COVID-19 pandemic posed a major threat to human health and the environment. Irregular and improper waste collection, handling, suspension of waste recycling and unsanitary disposal were all important issues in the processing and management of generated waste. This study emphasised a systematic review and content analysis to categorise all types of waste management (WM) during the COVID-19 pandemic to accomplish a well understanding of the relation between the COVID-19 pandemic and its impacts on WM within the literature. In this systematic review, a number of published papers on different aspects of WM during March 2020 to February 2021 were considered in order to identify major challenges in handling WM during the pandemic time and highlight multi-strategic approaches suggested. A content analysis of the 58 relevant papers was carried out by incorporating different types of WM at local as well as global scales. The present review results revealed that the COVID-19 has impacted the quantity and composition of waste, and the crisis caused by the pandemic has also altered the nature of global WM system. A comprehensive analysis on how the systems of WM were affected through the advancement of COVID-19 and what would be the healthier solutions was also highlighted in this systematic review. The results of this systematic review would be beneficial for better policymakers to holistically address potential future pandemics, if any.

The amount of SARS-CoV-2 RNA in wastewater relates to the development of the pandemic and its burden on the health system

Virus surveillance in wastewater can be a useful indicator of the development of the COVID-19 pandemic in communities. However, knowledge about how the amount of SARS-CoV-2 RNA in wastewater relates to different data on the burden on the health system is still limited. Herein, we monitored the amount of SARS-CoV-2 RNA and the spectrum of virus variants in weekly pooled wastewater samples for two years from mid-February 2020 and compared with several clinical data. The two-year monitoring showed the weekly changes in the amount of viral RNA in wastewater preceded the hospital care needs for COVID-19 and the number of acute calls on adult acute respiratory distress by 1-2 weeks during the first three waves of COVID-19. Our study demonstrates that virus surveillance in wastewater can predict the development of a pandemic and its burden on the health system, regardless of society's test capacity and possibility of tracking infected cases.

Comparison of SARS-CoV-2 spike RNA sequences in feces and nasopharynx indicates intestinal replication

Background

Little is known of possible selection and replication of SARS-CoV-2 in the intestines and if viral load in feces is associated with severity of disease. Therefore, sequence variations of the spike region in strains collected from feces and nasopharynx (NPH) from the same patients were compared. It was also investigated whether viral load in feces related to severity of COVID-19 in hospitalized patients.

Results

SARS-CoV-2 RNA was found in 88 (79%) fecal samples from 112 patients. The complete spike region could be sequenced in 15 fecal and 14 NPH samples. Fourteen Alpha-variants and one Beta-variant of SARS-CoV-2 were identified. The majority of the viral genetic variants (viral populations) in two fecal samples, but none in NPH, had a reversion of the H69/V70 amino acid deletion normally seen in the Alpha variants. Nine fecal samples contained up to nine minority variants, each which may constitute a separate viral population. Five NPH samples had one genetic variant each, and one NPH sample contained nine minority populations of SARS-CoV-2 spike genes.

Conclusions

The higher genomic diversity of SARS-CoV-2 in feces compared to NPH, and the reversion of the H69/V70 deletion in Alpha variants from feces indicate a selection of viral strains and replication of SARS-CoV-2 in the gastrointestinal tract.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13099-022-00509-w.

Effectiveness of environmental surveillance of SARS-CoV-2 as an early warning system during the first year of the COVID-19 pandemic: a systematic review

Since infected persons shed SARS-CoV-2 in faeces before symptoms appear, environmental surveillance (ES) may serve as an early warning system (EWS) for COVID-19 and new variants of concern. The ES of SARS-CoV-2 has been widely reviewed; however, its effectiveness as an EWS for SARS-CoV-2 in terms of timeliness, sensitivity and specificity has not been systematically assessed. We conducted a systematic review to identify and synthesise evidence on the ES of SARS-CoV-2 as an EWS to evaluate the added value for public health. Of 1,014 studies identified, we considered 29 for a qualitative synthesis of the timeliness of ES as an EWS for COVID-19, while six studies were assessed for the ability to detect new variants and two for both aims. The synthesis indicates ES may serve as an EWS of 1-2 weeks. ES could complement clinical surveillance for SARS-CoV-2; however, its cost-benefit value for public health decisions needs to be assessed based on the stage of the pandemic and resources available. Studies focusing methodological knowledge gaps as well as how to use and interpret ES signals for public health actions are needed, as is the sharing of knowledge within countries/areas with long experience of such surveillance.