Future perspectives of wastewater-based epidemiology: Monitoring infectious disease spread and resistance to the community level

Community-Scale Wastewater Surveillance of Candida auris during an Ongoing Outbreak in Southern Nevada

Candida auris is an opportunistic fungal pathogen and an emerging global public health threat, given its high mortality among infected individuals, antifungal resistance, and persistence in healthcare environments. This study explored the applicability of wastewater surveillance for C. auris in a metropolitan area with reported outbreaks across multiple healthcare facilities. Influent or primary effluent samples were collected over 10 weeks from seven sewersheds in Southern Nevada. Pelleted solids were analyzed using an adapted quantitative polymerase chain reaction (qPCR) assay targeting the ITS2 region of the C. auris genome. Positive detection was observed in 72 of 91 samples (79%), with higher detection frequencies in sewersheds serving healthcare facilities involved in the outbreak (94 vs 20% sample positivity). Influent wastewater concentrations ranged from 2.8 to 5.7 log₁₀ gene copies per liter (gc/L), and primary clarification achieved an average log reduction value (LRV) of 1.24 ± 0.34. Presumptive negative surface water and wastewater controls were non-detect. These results demonstrate that wastewater surveillance may assist in tracking the spread of C. auris and serve as an early warning tool for public health action. These findings provide the foundation for future application of wastewater-based epidemiology (WBE) to community- or facility-level surveillance of C. auris and other high consequence, healthcare-associated infectious agents.

Performance of methods for SARS-CoV-2 variant detection and abundance estimation within mixed population samples

Background

The accurate identification of SARS-CoV-2 (SC2) variants and estimation of their abundance in mixed population samples (e.g., air or wastewater) is imperative for successful surveillance of community level trends. Assessing the performance of SC2 variant composition estimators (VCEs) should improve our confidence in public health decision making. Here, we introduce a linear regression based VCE and compare its performance to four other VCEs: two re-purposed DNA sequence read classifiers (Kallisto and Kraken2), a maximum-likelihood based method (Lineage deComposition for Sars-Cov-2 pooled samples (LCS)), and a regression based method (Freyja).

Methods

We simulated DNA sequence datasets of known variant composition from both Illumina and Oxford Nanopore Technologies (ONT) platforms and assessed the performance of each VCE. We also evaluated VCEs performance using publicly available empirical wastewater samples collected for SC2 surveillance efforts. Bioinformatic analyses were performed with a custom NextFlow workflow (C-WAP, CFSAN Wastewater Analysis Pipeline). Relative root mean squared error (RRMSE) was used as a measure of performance with respect to the known abundance and concordance correlation coefficient (CCC) was used to measure agreement between pairs of estimators.

Results

Based on our results from simulated data, Kallisto was the most accurate estimator as it had the lowest RRMSE, followed by Freyja. Kallisto and Freyja had the most similar predictions, reflected by the highest CCC metrics. We also found that accuracy was platform and amplicon panel dependent. For example, the accuracy of Freyja was significantly higher with Illumina data compared to ONT data; performance of Kallisto was best with ARTICv4. However, when analyzing empirical data there was poor agreement among methods and variations in the number of variants detected (e.g., Freyja ARTICv4 had a mean of 2.2 variants while Kallisto ARTICv4 had a mean of 10.1 variants).

Conclusion

This work provides an understanding of the differences in performance of a number of VCEs and how accurate they are in capturing the relative abundance of SC2 variants within a mixed sample (e.g., wastewater). Such information should help officials gauge the confidence they can have in such data for informing public health decisions.

Occurrence of Z-drugs, benzodiazepines, and ketamine in wastewater in the United States and Mexico during the Covid-19 pandemic

Z-drugs, benzodiazepines and ketamine are classes of psychotropic drugs prescribed for treating anxiety, sleep disorders and depression with known side effects including an elevated risk of addiction and substance misuse. These drugs have a strong potential for misuse, which has escalated over the years and was hypothesized here to have been exacerbated during the COVID-19 pandemic. Wastewater-based epidemiology (WBE) constitutes a fast, easy, and relatively inexpensive approach to epidemiological surveys for understanding the incidence and frequency of uses of these drugs. In this study, we analyzed wastewater (n = 376) from 50 cities across the United States and Mexico from July to October 2020 to estimate drug use rates during a pandemic event. Both time and flow proportional composite and grab samples of untreated municipal wastewater were analyzed using solid-phase extraction followed by liquid chromatography-tandem mass spectrometry to determine loadings of alprazolam, clonazepam, diazepam, ketamine, lorazepam, nordiazepam, temazepam, zolpidem, and zaleplon in raw wastewater. Simultaneously, prescription data of the aforementioned drugs were extracted from the Medicaid database from 2019 to 2021. Results showed high detection frequencies of ketamine (90 %), lorazepam (87 %), clonazepam (76 %) and temazepam (73 %) across both Mexico and United States and comparatively lower detection frequencies for zaleplon (22 %), zolpidem (9 %), nordiazepam (<1 %), diazepam (<1 %), and alprazolam (<1 %) during the pandemic. Average mass consumption rates, estimated using WBE and reported in units of mg/day/1000 persons, ranged between 62 (temazepam) and 1100 (clonazepam) in the United States. Results obtained from the Medicaid database also showed a significant change (p < 0.05) in the prescription volume between the first quarter of 2019 (before the pandemic) and the first quarter of 2021 (pandemic event) for alprazolam, clonazepam and lorazepam. Study results include the first detections of zaleplon and zolpidem in wastewater from North America.

Evaluation of population estimation methods for wastewater-based epidemiology in a metropolitan city

This study evaluated the effect of population estimation on the calculation of drug biomarker consumption using wastewater-based epidemiology. Population estimates using mobile phone data, census data, and wastewater quality parameters, such as biological oxygen demand (BOD), total nitrogen (TN), and total phosphorus (TP), were evaluated in six different wastewater treatment plant catchment areas of Busan Metropolitan City, South Korea. The population based on mobile phone data was affected by the patterns of non-resident population movements in each area. The population-normalized daily loads (PNDLs) of methamphetamine were compared according to the different population results. The PNDLs using the population based on mobile phone data (PNDL_Mobile) was 5.87-27.0 mg/d/1000 people. The PNDL_Mobile values were notably different from the PNDLs using wastewater quality parameters (PNDL_Wastewater) (PNDL_Wastewater/PNDL_Mobile: 51-148 %, mean 93 %, relative standard deviation (RSD) 36 %), indicating the unsuitability of population estimation using BOD, TN, and TP. In areas with a large concentration of workplaces, the PNDLs using census data (PNDL_Census) differed from the PNDL_Mobile values (PNDL_Census/PNDL_Mobile: 57-124 %, mean 94 %, RSD 27 %), whereas other areas showed similar values for PNDL_Census and PNDL_Mobile (PNDL_Census/PNDL_Mobile: 95-108 %, mean 102 %, RSD 4.2 %). In particular, the total population estimates of the six survey areas using census data were approximately the same as those based on mobile phone data (RSD: 0.8 %), indicating a decrease in the influence of the non-residential active population in the entire metropolitan city.

Detection of human adenovirus F41 in wastewater and its relationship to clinical cases of acute hepatitis of unknown aetiology

As of 8 July 2022, the World Health Organization (WHO) have reported 1010 probable cases of acute hepatitis of unknown aetiology in children worldwide, including approximately 250 cases in the United Kingdom (UK). Clinical presentations have often been severe, with liver transplantation a frequent clinical outcome. Human adenovirus F41 (HAdV-F41) has been detected in most children with acute hepatitis, but its role in the pathogenesis of this infection has yet to be established. Wastewater-based epidemiology (WBE) has become a well-established tool for monitoring the community spread of SARS-CoV-2, as well as other pathogens and chemicals. In this study, we adopted a WBE approach to monitoring levels of HAdV-F40/41 in wastewater before and during an acute hepatitis outbreak in Northern Ireland. We report increasing detection of HAdV-F40/41 in wastewater, concomitant with increasing numbers of clinical cases. Amplicon whole genome sequencing further classified the wastewater-derived HAdV as belonging to the F41 genotype which in turn was homologous to clinically derived sequences. We propose that WBE has the potential to inform community surveillance of HAdV-F41 and can further contribute to the ongoing global discussion supporting HAdV-F41 involvement in acute hepatitis cases.

Monkeypox outbreak: Wastewater and environmental surveillance perspective

Monkeypox disease (MPXD), a viral disease caused by the monkeypox virus (MPXV), is an emerging zoonotic disease endemic in some countries of Central and Western Africa but seldom reported outside the affected region. Since May 2022, MPXD has been reported at least in 74 countries globally, prompting the World Health Organization to declare the MPXD outbreak a Public Health Emergency of International Concern. As of July 24, 2022; 92 % (68/74) of the countries with reported MPXD cases had no historical MPXD case reports. From the One Health perspective, the spread of MPXV in the environment poses a risk not only to humans but also to small mammals and may, ultimately, spread to potent novel host populations. Wastewater-based surveillance (WBS) has been extensively utilized to monitor communicable diseases, particularly during the ongoing COVID-19 pandemic. It helped in monitoring infectious disease caseloads as well as specific viral variants circulating in communities. The detection of MPXV DNA in lesion materials (e.g. skin, vesicle fluid, crusts), skin rashes, and various body fluids, including respiratory and nasal secretions, saliva, urine, feces, and semen of infected individuals, supports the possibility of using WBS as an early proxy for the detection of MPXV infections. WBS of MPXV DNA can be used to monitor MPXV activity/trends in sewerage network areas even before detecting laboratory-confirmed clinical cases within a community. However, several factors affect the detection of MPXV in wastewater including, but not limited to, routes and duration time of virus shedding by infected individuals, infection rates in the relevant affected population, environmental persistence, the processes and analytical sensitivity of the used methods. Further research is needed to identify the key factors that impact the detection of MPXV biomarkers in wastewater and improve the utility of WBS of MPXV as an early warning and monitoring tool for safeguarding human health. In this review, we shortly summarize aspects of the MPXV outbreak relevant to wastewater monitoring and discuss the challenges associated with WBS.

Piezocatalytic Medicine: An Emerging Frontier using Piezoelectric Materials for Biomedical Applications

Emerging piezocatalysts have demonstrated their remarkable application potential in diverse medical fields. In addition to their ultrahigh catalytic activities, their inherent and unique charge-carrier-releasing properties can be used to initiate various redox catalytic reactions, displaying bright prospects for future medical applications. Triggered by mechanical energy, piezocatalytic materials can release electrons/holes, catalyze redox reactions of substrates, or intervene in biological processes to promote the production of effector molecules for medical purposes, such as decontamination, sterilization, and therapy. Such a medical application of piezocatalysis is termed as piezocatalytic medicine (PCM) herein. To pioneer novel medical technologies, especially therapeutic modalities, this review provides an overview of the state-of-the-art research progress in piezocatalytic medicine. First, the principle of piezocatalysis and the preparation methodologies of piezoelectric materials are introduced. Then, a comprehensive summary of the medical applications of piezocatalytic materials in tumor treatment, antisepsis, organic degradation, tissue repair and regeneration, and biosensing is provided. Finally, the main challenges and future perspectives in piezocatalytic medicine are discussed and proposed, expecting to fuel the development of this emerging scientific discipline.

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.

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.

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.

Water and wastewater digital surveillance for monitoring and early detection of the COVID-19 hotspot: industry 4.0

There are a high number of COVID-19 cases per capita in the world that goes undetected including clinical diseases compatible with COVID-19. While the presence of the COVID-19 in untreated drinking water is possible, it is yet to be detected in the drinking-water supplies. COVID-19 viral fragments have been found in excrete, this call for wastewater monitoring and analysis (wastewater surveillance) of the potential health risk. This raises concern about the potential of the SARS-CoV-2 transmission via the water systems. The economic limits on the medical screening for the SARS-CoV-2 or COVID-19 worldwide are turning to wastewater-based epidemiology as great potential tools for assessing and management of the COVID-19 pandemic. Surveillance and tracking of the pathogens in the wastewater are key to the early warning system and public health strategy monitoring of the COVID-19. Currently, RT-qPCR assays is been developed for SARS-CoV-2 RNA specimen clinical testing and detection in the water system. Convectional wastewater treatment methods and disinfection are expected to eradicate the SAR-CoV-2. Chlorine, UV radiation, ozone, chloramine is been used to inactivate and disinfect the water treatment system against the SARS-CoV-2. Water management and design of the water infrastructure require major changes to accommodate climate change, water cycle, reimaging of digitalization, infrastructure and privacy protection. The water digital revolution, biosensors and nanoscale, contact tracing, knowledge management can accelerate with disruption of the COVID-19 outbreak (water-health-digital nexus).

Optimization of sewage sampling for wastewater-based epidemiology through stochastic modeling

The proliferation of the SARS-CoV-2 global pandemic has brought to attention the need for epidemiological tools that can detect diseases in specific geographical areas through non-contact means. Such methods may protect those potentially infected by facilitating early quarantine policies to prevent the spread of the disease. Sampling of municipal wastewater has been studied as a plausible solution to detect pathogen spread, even from asymptomatic patients. However, many challenges exist in wastewater-based epidemiology such as identifying a representative sample for a population, determining the appropriate sample size, and establishing the right time and place for samples. In this work, a new approach to address these questions is assessed using stochastic modeling to represent wastewater sampling given a particular community of interest. Using estimates for various process parameters, inferences on the population infected are generated with Monte Carlo simulation output. A case study at the University of Oklahoma is examined to calibrate and evaluate the model output. Finally, extensions are provided for more efficient wastewater sampling campaigns in the future. This research provides greater insight into the effects of viral load, the percentage of the population infected, and sampling time on mean SARS-CoV-2 concentration through simulation. In doing so, an earlier warning of infection for a given population may be obtained and aid in reducing the spread of viruses.

Building health system resilience and pandemic preparedness using wastewater-based epidemiology from SARS-CoV-2 monitoring in Bengaluru, India

The COVID-19 pandemic was a watershed event for wastewater-based epidemiology (WBE). It highlighted the inability of existing disease surveillance systems to provide sufficient forewarning to governments on the existing stage and scale of disease spread and underscored the need for an effective early warning signaling system. Recognizing the potentiality of environmental surveillance (ES), in May 2021, COVIDActionCollaborative launched the Precision Health platform. The idea was to leverage ES for equitable mapping of the disease spread in Bengaluru, India and provide early information regarding any inflection in the epidemiological curve of COVID-19. By sampling both networked and non-networked sewage systems in the city, the platform used ES for both equitable and comprehensive surveillance of the population to derive precise information on the existing stage of disease maturity across communities and estimate the scale of the approaching threat. This was in contrast to clinical surveillance, which during the peak of the COVID-19 pandemic in Bengaluru excluded a significant proportion of poor and vulnerable communities from its ambit of representation. The article presents the findings of a sense-making tool which the platform developed for interpreting emerging signals from wastewater data to map disease progression and identifying the inflection points in the epidemiological curve. Thus, the platform accurately generated early warning signals on disease escalation and disseminated it to the government and the general public. This information enabled concerned audiences to implement preventive measures in advance and effectively plan their next steps for improved disease management.

Wastewater surveillance of antibiotic-resistant bacterial pathogens: A systematic review

Infectious diseases caused by antibiotic-resistant bacterial (ARB) pathogens are a serious threat to human and animal health. The active surveillance of ARB using an integrated one-health approach can help to reduce the emergence and spread of ARB, reduce the associated economic impact, and guide antimicrobial stewardship programs. Wastewater surveillance (WWS) of ARB provides composite samples for a total population, with easy access to the mixed community microbiome. This concept is emerging rapidly, but the clinical utility, sensitivity, and uniformity of WWS of ARB remain poorly understood especially in relation to clinical evidence in sewershed communities. Here, we systematically searched the literature to identify studies that have compared findings from WWS of ARB and antibiotic resistance genes (ARG) with clinical evidence in parallel, thereby evaluating how likely WWS of ARB and ARG can relate to the clinical cases in communities. Initially, 2,235 articles were obtained using the primary search keywords, and 1,219 articles remained after de-duplication. Among these, 35 articles fulfilled the search criteria, and an additional 13 relevant articles were included by searching references in the primary literature. Among the 48 included papers, 34 studies used a culture-based method, followed by 11 metagenomics, and three PCR-based methods. A total of 28 out of 48 included studies were conducted at the single sewershed level, eight studies involved several countries, seven studies were conducted at national or regional scales, and five at hospital levels. Our review revealed that the performance of WWS of ARB pathogens has been evaluated more frequently for Escherichia coli, Enterococcus spp., and other members of the family Enterobacteriaceae, but has not been uniformly tested for all ARB pathogens. Many wastewater-based ARB studies comparing the findings with clinical evidence were conducted to evaluate the public health risk but not to relate with clinical evidence and to evaluate the performance of WWS of ARB. Indeed, relating WWS of ARB with clinical evidence in a sewershed is not straightforward, as the source of ARB in wastewater cannot be only from symptomatic human individuals but can also be from asymptomatic carriers as well as from animal sources. Further, the varying fates of each bacterial species and ARG within the sewerage make the aim of connecting WWS of ARB with clinical evidence more complicated. Therefore, future studies evaluating the performance of many AMR pathogens and their genes for WWS one by one can make the process simpler and the interpretation of results easier.

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.

A Framework for Public Health Authorities to Evaluate Health Determinants for Wastewater-Based Epidemiology

BACKGROUND

Wastewater-based epidemiology (WBE) is rapidly developing as a powerful public health tool. It can provide information about a wide range of health determinants (HDs), including community exposure to environmental hazards, trends in consumption of licit and illicit substances, spread of infectious diseases, and general community health. As such, the list of possible candidate HDs for WBE is almost limitless. Consequently, a means to evaluate and prioritize suitable candidates for WBE is useful, particularly for public health authorities, who often face resource constraints.

OBJECTIVES

We have developed a framework to assist public health authorities to decide what HDs may be appropriate for WBE and what biomarkers could be used. This commentary reflects the experience of the authors, who work at the interface of research and public health implementation.

DISCUSSION

To be suitable for WBE, a candidate HD should address a public health or scientific issue that would benefit from better understanding at the population level. For HDs where information on individual exposures or stratification by population subgroups is required, WBE is less suitable. Where other methodologies are already used to monitor the candidate HD, consideration must be given to whether WBE could provide better or complementary information to the current approach. An essential requirement of WBE is a biomarker specific for the candidate HD. A biomarker in this context refers to any human-excreted chemical or biological that could act as an indicator of consumption or exposure to an environmental hazard or of the human health state. Suitable biomarkers should meet several criteria outlined in this commentary, which requires background knowledge for both the biomarker and the HD. An evaluation tree summarizing key considerations for public health authorities when assessing the suitability of candidate HDs for WBE and an example evaluation are presented. https://doi.org/10.1289/EHP11115.

Spectrum of environmental surveillance of SARS-CoV-2 fragments: Questions, quests, and conquest

This works examines the entire spectrum of 'Environmental Surveillance (EnvSurv)' of SARS-CoV-2 fragments i.e. the questions, quests, and conquests of the technology since early year 2020. The prime focus of the present work to document the journey with achieved objectives and remaining ambitions associated with the technology. Despite the EnvSurv may be regarded as the techniques, which rather achieved more than expected, will it win the struggle for its existence or lose its way once the pandemic and fear associated with it completely fades. Pertaining to this discussions, major researched topics were investigated, followed by enlisting of ten bullets of the past experiences along with corresponding challenges, and finally key targets for the techniques are enlisted. The article targets to be a simple guide of the journey of EnvSur in terms of its effectiveness for treatment, infectivity, monitoring & estimation (TIME) till date.

Assessment of Commonly Measured Wastewater Parameters to Estimate Sewershed Populations for Use in Wastewater-Based Epidemiology: Insights into Population Dynamics in New York City during the COVID-19 Pandemic

Understanding per capita rates of disease incidence or prevalence from wastewater surveillance data requires an estimate of the population contributing to wastewater samples, given that populations in large urban areas are dynamic, especially if major events, such as the onset of the COVID-19 pandemic, cause large population shifts. To assess whether commonly measured wastewater parameters can be used to estimate sewershed populations, we used wastewater data collected from New York City's (NYC) 14 wastewater treatment facilities to evaluate the relationship between influent loads of four wastewater parameters-ammonia, total Kjeldahl nitrogen, total suspended solids, and five-day carbonaceous biochemical oxygen demand-and census-based population estimates of the corresponding sewersheds during 2019, when populations were assumed to be relatively stable. Ammonia mass load had the most consistent relationship with sewershed population, regardless of wet weather contributions to NYC's predominantly combined sewer system. Changes in ammonia loads due to COVID-19 restrictions enacted in March 2020 generally reflected population shifts in sewersheds serving areas of Manhattan and Brooklyn, for which previous studies report decreased commuter mobility and residential populations. Our findings highlight the utility of ammonia mass load in influent wastewater as a population indicator to normalize wastewater-based epidemiology data and track sewershed population dynamics.

RNA in Municipal Wastewater Reveals Magnitudes of COVID-19 Outbreaks across Four Waves Driven by SARS-CoV-2 Variants of Concern

There are no standardized protocols for quantifying severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in wastewater to date, especially for population normalization. Here, a pipeline was developed, applied, and assessed to quantify SARS-CoV-2 and key variants of concern (VOCs) RNA in wastewater at Saskatoon, Canada. Normalization approaches using recovery ratio and extraction efficiency, wastewater parameters, or population indicators were assessed by comparing to daily numbers of new cases. Viral load was positively correlated with daily new cases reported in the sewershed. Wastewater surveillance (WS) had a lead time of approximately 7 days, which indicated surges in the number of new cases. WS revealed the variant α and δ driving the third and fourth wave, respectively. The adjustment with the recovery ratio and extraction efficiency improved the correlation between viral load and daily new cases. Normalization of viral concentration to concentrations of the artificial sweetener acesulfame K improved the trend of viral load during the Christmas and New Year holidays when populations were dynamic and variable. Acesulfame K performed better than pepper mild mottle virus, creatinine, and ammonia for population normalization. Hence, quality controls to characterize recovery ratios and extraction efficiencies and population normalization with acesulfame are promising for precise WS programs supporting decision-making in public health.

Subsewershed SARS-CoV-2 Wastewater Surveillance and COVID-19 Epidemiology Using Building-Specific Occupancy and Case Data

To evaluate the use of wastewater-based surveillance and epidemiology to monitor and predict SARS-CoV-2 virus trends, over the 2020-2021 academic year we collected wastewater samples twice weekly from 17 manholes across Virginia Tech's main campus. We used data from external door swipe card readers and student isolation/quarantine status to estimate building-specific occupancy and COVID-19 case counts at a daily resolution. After analyzing 673 wastewater samples using reverse transcription quantitative polymerase chain reaction (RT-qPCR), we reanalyzed 329 samples from isolation and nonisolation dormitories and the campus sewage outflow using reverse transcription digital droplet polymerase chain reaction (RT-ddPCR). Population-adjusted viral copy means from isolation dormitory wastewater were 48% and 66% higher than unadjusted viral copy means for N and E genes (1846/100 mL to 2733/100 mL/100 people and 2312/100 mL to 3828/100 mL/100 people, respectively; n = 46). Prespecified analyses with random-effects Poisson regression and dormitory/cluster-robust standard errors showed that the detection of N and E genes were associated with increases of 85% and 99% in the likelihood of COVID-19 cases 8 days later (incident-rate ratio (IRR) = 1.845, p = 0.013 and IRR = 1.994, p = 0.007, respectively; n = 215), and one-log increases in swipe card normalized viral copies (copies/100 mL/100 people) for N and E were associated with increases of 21% and 27% in the likelihood of observing COVID-19 cases 8 days following sample collection (IRR = 1.206, p < 0.001, n = 211 for N; IRR = 1.265, p < 0.001, n = 211 for E). One-log increases in swipe normalized copies were also associated with 40% and 43% increases in the likelihood of observing COVID-19 cases 5 days after sample collection (IRR = 1.403, p = 0.002, n = 212 for N; IRR = 1.426, p < 0.001, n = 212 for E). Our findings highlight the use of building-specific occupancy data and add to the evidence for the potential of wastewater-based epidemiology to predict COVID-19 trends at subsewershed scales.

Comparative Analysis of RNA-Extraction Approaches and Associated Influences on RT-qPCR of the SARS-CoV-2 RNA in a University Residence Hall and Quarantine Location

Wastewater-based epidemiology (WBE) provides an early warning and trend analysis approach for determining the presence of COVID-19 in a community and complements clinical testing in assessing the population level, even as viral loads fluctuate. Here, we evaluate combinations of two wastewater concentration methods (i.e., ultrafiltration and composite supernatant-solid), four pre-RNA extraction modifications, and three nucleic acid extraction kits using two different wastewater sampling locations. These consisted of a quarantine facility containing clinically confirmed COVID-19-positive inhabitants and a university residence hall. Of the combinations examined, composite supernatant-solid with pre-RNA extraction consisting of water concentration and RNA/DNA shield performed the best in terms of speed and sensitivity. Further, of the three nucleic acid extraction kits examined, the most variability was associated with the Qiagen kit. Focusing on the quarantine facility, viral concentrations measured in wastewater were generally significantly related to positive clinical cases, with the relationship dependent on method, modification, kit, target, and normalization, although results were variable-dependent on individual time points (Kendall's Tau-b (τ) = 0.17 to 0.6) or cumulatively (Kendall's Tau-b (τ) = -0.048 to 1). These observations can support laboratories establishing protocols to perform wastewater surveillance and monitoring efforts for COVID-19.

Spatiotemporal Surveillance of SARS-CoV-2 in the Sewage of Three Major Urban Areas in Peru: Generating Valuable Data Where Clinical Testing Is Extremely Limited

Peru has been severely affected by the COVID-19 pandemic. By January 2022, Peru had surpassed 200 000 COVID-19 deaths, constituting the highest death rate per capita worldwide. Peru has had several limitations during the pandemic: insufficient testing access, limited contact tracing, a strained medical infrastructure, and many economic hurdles. These limitations hindered the gathering of accurate information about infected individuals with spatial resolution in real time, a critical aspect of effectively controlling the pandemic. Wastewater monitoring for SARS-CoV-2 RNA offered a promising alternative for providing needed population-wide information to complement health care indicators. In this study, we demonstrate the feasibility and value of implementing a decentralized SARS-CoV-2 RNA wastewater monitoring system to assess the spatiotemporal distribution of COVID-19 in three major cities in Peru: Lima, Callao, and Arequipa. Our data on viral loads showed the same trends as health indicators such as incidence and mortality. Furthermore, we were able to identify hot spots of contagion within the surveyed urban areas to guide the efforts of health authorities. Viral decay in the sewage network of the cities studied was found to be negligible (<2%). Overall, our results support wastewater monitoring for SARS-CoV-2 as a valuable and cost-effective tool for monitoring the COVID-19 pandemic in the Peruvian context.

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.

Assessment of a mass balance equation for estimating community-level prevalence of COVID-19 using wastewater-based epidemiology in a mid-sized city

Wastewater-based epidemiology (WBE) has emerged as a valuable epidemiologic tool to detect the presence of pathogens and track disease trends within a community. WBE overcomes some limitations of traditional clinical disease surveillance as it uses pooled samples from the entire community, irrespective of health-seeking behaviors and symptomatic status of infected individuals. WBE has the potential to estimate the number of infections within a community by using a mass balance equation, however, it has yet to be assessed for accuracy. We hypothesized that the mass balance equation-based approach using measured SARS-CoV-2 wastewater concentrations can generate accurate prevalence estimates of COVID-19 within a community. This study encompassed wastewater sampling over a 53-week period during the COVID-19 pandemic in Gainesville, Florida, to assess the ability of the mass balance equation to generate accurate COVID-19 prevalence estimates. The SARS-CoV-2 wastewater concentration showed a significant linear association (Parameter estimate = 39.43, P value < 0.0001) with clinically reported COVID-19 cases. Overall, the mass balance equation produced accurate COVID-19 prevalence estimates with a median absolute error of 1.28%, as compared to the clinical reference group. Therefore, the mass balance equation applied to WBE is an effective tool for generating accurate community-level prevalence estimates of COVID-19 to improve community surveillance.

Analytical research of pesticide biomarkers in wastewater with application to study spatial differences in human exposure

Wastewater-based epidemiology (WBE) relies on the assessment and interpretation of levels of biomarkers in wastewater originating from a well-defined community. It has provided unique information on spatial and temporal trends of licit and illicit drug consumption, and has also the potential to give complementary information on human exposure to chemicals. Here, we focus on the accurate quantification of pesticide biomarkers (i.e., predominantly urinary metabolites) in influent wastewater at the ng L^-1 level to be used for WBE. In the present study, an advanced analytical methodology has been developed based on ultra-high-pressure liquid chromatography coupled to tandem mass spectrometry (UHPLC-MS/MS), for the simultaneous determination of 11 specific human biomarkers of triazines, urea herbicides, pyrethroids and organophosphates in urban wastewater. The sample treatment consisted of solid-phase extraction using Oasis HLB cartridges. Direct injection of the samples was also tested for all compounds, as a simple and rapid way to determine these compounds without sample manipulation (i.e., minimizing potential analytical errors). However, if extraction recoveries are satisfactory, SPE is the preferred approach that allow reaching lower concertation levels. Six isotopically labelled internal standards were evaluated and used to correct for matrix effects. Due to the difficulties associated with this type of analysis, special emphasis has been placed on the analytical challenges encountered. The satisfactory validated methodology was applied to urban wastewater samples collected from different locations across Europe revealing the presence of 2,6-EA, 3,4-DCA, 3-PBA and 4-HSA i.e, metabolites of metolachlor-s, urea herbicides, pyrethroids and chlorpropham, respectively. Preliminary data reported in this paper illustrate the applicability of this analytical approach for assessing human exposure to pesticides through WBE.

Demonstrating a Comprehensive Wastewater-Based Surveillance Approach That Differentiates Globally Sourced Resistomes

Wastewater-based surveillance (WBS) for disease monitoring is highly promising but requires consistent methodologies that incorporate predetermined objectives, targets, and metrics. Herein, we describe a comprehensive metagenomics-based approach for global surveillance of antibiotic resistance in sewage that enables assessment of 1) which antibiotic resistance genes (ARGs) are shared across regions/communities; 2) which ARGs are discriminatory; and 3) factors associated with overall trends in ARGs, such as antibiotic concentrations. Across an internationally sourced transect of sewage samples collected using a centralized, standardized protocol, ARG relative abundances (16S rRNA gene-normalized) were highest in Hong Kong and India and lowest in Sweden and Switzerland, reflecting national policy, measured antibiotic concentrations, and metal resistance genes. Asian versus European/US resistomes were distinct, with macrolide-lincosamide-streptogramin, phenicol, quinolone, and tetracycline versus multidrug resistance ARGs being discriminatory, respectively. Regional trends in measured antibiotic concentrations differed from trends expected from public sales data. This could reflect unaccounted uses, captured only by the WBS approach. If properly benchmarked, antibiotic WBS might complement public sales and consumption statistics in the future. The WBS approach defined herein demonstrates multisite comparability and sensitivity to local/regional factors.

Data modelling recipes for SARS-CoV-2 wastewater-based epidemiology

Wastewater based epidemiology is recognized as one of the monitoring pillars, providing essential information for pandemic management. Central in the methodology are data modelling concepts for both communicating the monitoring results but also for analysis of the signal. It is due to the fast development of the field that a range of modelling concepts are used but without a coherent framework. This paper provides for such a framework, focusing on robust and simple concepts readily applicable, rather than applying latest findings from e.g., machine learning. It is demonstrated that data preprocessing, most important normalization by means of biomarkers and equal temporal spacing of the scattered data, is crucial. In terms of the latter, downsampling to a weekly spaced series is sufficient. Also, data smoothing turned out to be essential, not only for communication of the signal dynamics but likewise for regressions, nowcasting and forecasting. Correlation of the signal with epidemic indicators requires multivariate regression as the signal alone cannot explain the dynamics but - for this case study - multiple linear regression proofed to be a suitable tool when the focus is on understanding and interpretation. It was also demonstrated that short term prediction (7 days) is accurate with simple models (exponential smoothing or autoregressive models) but forecast accuracy deteriorates fast for longer periods.

NC-COVID: A Time-Varying Compartmental Model for Estimating SARS-CoV-2 Infection Dynamics in North Carolina, US

Efforts to track and model SARS-CoV-2 infection dynamics in the population have been complicated by certain aspects of the transmission characteristics, which include a pre-symptomatic infectious phase as well as asymptomatic infectious individuals. Another problem is that many models focus on case count, as there has been (and is) limited data regarding infection status of members of the population, which is the most important aspect for constructing transmission models. This paper describes and explains the parameterization, calibration, and revision of the NC-COVID model, a compartmental model to estimate SARS-CoV-2 infection dynamics for the state of North Carolina, US. The model was developed early in the pandemic to provide rapid, up-to-date state-level estimates of the number of people who were currently infected, were immune from a prior infection, and remained susceptible to infection. As a post modeling exercise, we assessed the veracity of the model by comparing its output to SARS-CoV-2 viral particle concentrations detected in wastewater data and to estimates of people infected using COVID-19 deaths. The NC-COVID model was highly correlated with these independently derived estimates, suggesting that it produced accurate estimates of SARS-CoV-2 infection dynamics in North Carolina.

Optimal allocation and operation of sewer monitoring sites for wastewater-based disease surveillance: A methodological proposal

Wastewater-based epidemiology (WBE) is drawing increasing attention as a promising tool for an early warning of emerging infectious diseases such as COVID-19. This study demonstrated the utility of a spatial bisection method (SBM) and a global optimization algorithm (i.e., genetic algorithm, GA), to support better designing and operating a WBE program for disease surveillance and source identification. The performances of SBM and GA were compared in determining the optimal locations of sewer monitoring manholes to minimize the difference among the effective spatial monitoring scales of the selected manholes. While GA was more flexible in determining the spatial resolution of the monitoring areas, SBM allows stepwise selection of optimal sampling manholes with equiareal subcatchments and lowers computational cost. Upon detecting disease outbreaks at a regular sewer monitoring site, additional manholes within the catchment can be selected and monitored to identify source areas with a required spatial resolution. SBM offered an efficient method for rapidly searching for the optimal locations of additional sampling manholes to identify the source areas. This study provides strategic and technical elements of WBE including sampling site selection with required spatial resolution and a source identification method.

The feasibility of SARS-CoV-2 surveillance using wastewater and environmental sampling in Indonesia

BACKGROUND

Wastewater-based epidemiology (WBE) surveillance as an early warning system (EWS) for monitoring community transmission of SARS-CoV-2 in low- and middle-income country (LMIC) settings, where diagnostic testing capacity is limited, needs further exploration. We explored the feasibility to conduct a WBE surveillance in Indonesia, one of the global epicenters of the COVID-19 pandemic in the middle of 2021, with the fourth largest population in the world where sewer and non-sewered sewage systems are implemented. The feasibility and resource capacity to collect samples on a weekly or fortnightly basis with grab and/or passive sampling methods, as well as to conduct qualitative and quantitative identification of SARS-CoV-2 ribonucleic acid (RNA) using real-time RT-PCR (RT-qPCR) testing of environmental samples were explored.

MATERIALS AND METHODS

We initiated a routine surveillance of wastewater and environmental sampling at three predetermined districts in Special Region of Yogyakarta Province. Water samples were collected from central and community wastewater treatment plants (WWTPs), including manholes flowing to the central WWTP, and additional soil samples were collected for the near source tracking (NST) locations (i.e., public spaces where people congregate).

RESULTS

We began collecting samples in the Delta wave of the COVID-19 pandemic in Indonesia in July 2021. From a 10-week period, 54% (296/544) of wastewater and environmental samples were positive for SARS-CoV-2 RNA. The sample positivity rate decreased in proportion with the reported incidence of COVID-19 clinical cases in the community. The highest positivity rate of 77% in week 1, was obtained for samples collected in July 2021 and decreased to 25% in week 10 by the end of September 2021.

CONCLUSION

A WBE surveillance system for SARS-CoV-2 in Indonesia is feasible to monitor the community burden of infections. Future studies testing the potential of WBE and EWS for signaling early outbreaks of SARS-CoV-2 transmissions in this setting are required.

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.

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.

Research needs for optimising wastewater-based epidemiology monitoring for public health protection

Wastewater-based epidemiology (WBE) is an unobtrusive method used to observe patterns in illicit drug use, poliovirus, and severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2). The pandemic and need for surveillance measures have led to the rapid acceleration of WBE research and development globally. With the infrastructure available to monitor SARS-CoV-2 from wastewater in 58 countries globally, there is potential to expand targets and applications for public health protection, such as other viral pathogens, antimicrobial resistance (AMR), pharmaceutical consumption, or exposure to chemical pollutants. Some applications have been explored in academic research but are not used to inform public health decision-making. We reflect on the current knowledge of WBE for these applications and identify barriers and opportunities for expanding beyond SARS-CoV-2. This paper critically reviews the applications of WBE for public health and identifies the important research gaps for WBE to be a useful tool in public health. It considers possible uses for pathogenic viruses, AMR, and chemicals. It summarises the current evidence on the following: (1) the presence of markers in stool and urine; (2) environmental factors influencing persistence of markers in wastewater; (3) methods for sample collection and storage; (4) prospective methods for detection and quantification; (5) reducing uncertainties; and (6) further considerations for public health use.

Assessment of community-wide antimicrobials usage in Eastern China using wastewater-based epidemiology

Wastewater-based epidemiology (WBE) has potential to identify the epidemiological links between people, animals, and the environment, as part of antimicrobial resistance (AMR) surveillance. In this study, we investigated six wastewater treatment plants (WWTPs) serving six communities located in two regions in Eastern China: Site A in Zhejiang and site B in Jiangsu province to assess the public use of antimicrobial agents (AA). Fifty antimicrobials and 24 of their metabolites were quantified using ultraperformance liquid chromatography coupled with triple quadrupole tandem mass spectrometry (UPLC-MS/MS). Spatiotemporal trends were established for measured concentrations, daily loads, and population-normalised daily loads. Daily AA mass loads varied between 1.6 g/day and 324.6 g/day reflecting the WWTP scales, with macrolides and β-lactams showing the highest overall environmental burden at 223.7 g/day and 173.7 g/day, respectively. Emissions of antibiotic residues from manufacturing have been observed, with the peak daily load 12-fold higher than the overall load from a community serving a population of over 600,000. Community exposure levels of 225.2 ± 156.2 mg/day/1000 inhabitant and 351.9 ± 133.5 mg/day/1000 inhabitant were recorded in site A and B, respectively. Paired parent-metabolites analysis identified a large proportion (64-78%) of un-metabolised metronidazole and clindamycin at site B, indicating improper disposal of unused drugs either in the community or in livestock production. Consumption levels, calculated via WBE, suggested relatively low antimicrobial usage in Eastern China compared to other areas in China. This first application of WBE in Eastern China to assess the community-wide exposure to AAs has potential to inform regional antimicrobial stewardship.

QuaID: Enabling Earlier Detection of Recently Emerged SARS-CoV-2 Variants of Concern in Wastewater

As clinical testing declines, wastewater monitoring can provide crucial surveillance on the emergence of SARS-CoV-2 variants of concern (VoC) in communities. Multiple recent studies support that wastewater-based SARS-CoV-2 detection of circulating VoC can precede clinical cases by up to two weeks. Furthermore, wastewater based epidemiology enables wide population-based screening and study of viral evolutionary dynamics. However, highly sensitive detection of emerging variants remains a complex task due to the pooled nature of environmental samples and genetic material degradation. In this paper we propose quasi-unique mutations for VoC identification, implemented in a novel bioinformatics tool (QuaID) for VoC detection based on quasi-unique mutations. The benefits of QuaID are three-fold: (i) provides up to 3 week earlier VoC detection compared to existing approaches, (ii) enables more sensitive VoC detection, which is shown to be tolerant of >50% mutation drop-out, and (iii) leverages all mutational signatures, including insertions & deletions.

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.

Consumption trends of pharmaceuticals and psychoactive drugs in Latvia determined by the analysis of wastewater

Wastewater-based epidemiology (WBE) was applied to evaluate seasonal variations of the consumption of pharmaceuticals (i.e. antibiotics, NSAIDs, antiepileptics, antihypertensives and others), caffeine, alcohol and nicotine in Latvia throughout 2021. In addition, weekly variation of caffeine, nicotine and alcohol consumption was investigated. Pronounced seasonality was observed in the consumption of antibiotics and decongestants, as well as caffeine, nicotine and alcohol. Correlation with COVID-19 statistics was observed in the case of macrolide antibiotics and antiasthmatic salbutamol. Comparison of the estimated consumption values obtained using the WBE approach and the statistics revealed that the majority of compounds data are in good agreement except angiotensin II receptor blocker group antihypertensives where the most overestimated consumption values were observed.

Lead time of early warning by wastewater surveillance for COVID-19: Geographical variations and impacting factors

The global data on the temporal tracking of the COVID-19 through wastewater surveillance needs to be comparatively evaluated to generate a proper and precise understanding of the robustness, advantages, and sensitivity of the wastewater-based epidemiological (WBE) approach. We reviewed the current state of knowledge based on several scientific articles pertaining to temporal variations in COVID-19 cases captured via viral RNA predictions in wastewater. This paper primarily focuses on analyzing the WBE-based temporal variation reported globally to check if the reported early warning lead-time generated through environmental surveillance is pragmatic or latent. We have compiled the geographical variations reported as lead time in various WBE reports to strike a precise correlation between COVID-19 cases and genome copies detected through wastewater surveillance, with respect to the sampling dates, separately for WASH and non-WASH countries. We highlighted sampling methods, climatic and weather conditions that significantly affected the concentration of viral SARS-CoV-2 RNA detected in wastewater, and thus the lead time reported from the various climatic zones with diverse WASH situations were different. Our major findings are: i) WBE reports around the world are not comparable, especially in terms of gene copies detected, lag-time gained between monitored RNA peak and outbreak/peak of reported case, as well as per capita RNA concentrations; ii) Varying sanitation facility and climatic conditions that impact virus degradation rate are two major interfering features limiting the comparability of WBE results, and iii) WBE is better applicable to WASH countries having well-connected sewerage system.

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

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

Full-Length 16S rRNA Gene Sequences from Raw Sewage Samples Spanning Geographic and Seasonal Gradients in Conveyance Systems across the United States

Wastewater microbiome research often relies on sequencing of hypervariable regions of 16S rRNA genes, which are difficult to classify at refined taxonomic levels. Here, we introduce a data set of near-full-length 16S rRNA genes from samples designed to capture known geographic and seasonal variations in municipal wastewater microbial communities.

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

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

Acquired insights from the long-term surveillance of SARS-CoV-2 RNA for COVID-19 monitoring: The case of Monterrey Metropolitan Area (Mexico)

Wastewater-based epidemiology offers a time- and cost-effective way to monitor SARS-CoV-2 spread in communities and therefore represents a complement to clinical testing. WBE applicability has been demonstrated in a number of cases over short-term periods as a method for tracking the prevalence of SARS-CoV-2 and an early-warning tool for predicting outbreaks in the population. This study reports SARS-CoV-2 viral loads from wastewater treatment plants (WWTPs) and hospitals over a 6-month period (June to December 2020). Results show that the overall range of viral load in positive tested samples was between 1.2 × 10³ and 3.5 × 10⁶ gene copies/l, unveiling that secondary-treated wastewaters mirrored the viral load of influents. The interpretation suggests that the viral titers found in three out of four WWTPs were associated to clinical COVID-19 surveillance indicators preceding 2-7 days the rise of reported clinical cases. The median wastewater detection rate of SARS-CoV-2 was one out of 14,300 reported new cases. Preliminary model estimates of prevalence ranged from 0.02 to 4.6% for the studied period. This comprehensive statistical and epidemiological analysis demonstrates that the applied wastewater-based approach to COVID-19 surveillance is in general consistent and feasible, although there is room for improvements.

Evaluating the impact of sample storage, handling, and technical ability on the decay and recovery of SARS-CoV-2 in wastewater

Wastewater based epidemiology (WBE) is useful for tracking and monitoring the level of disease prevalence in a community and has been used extensively to complement clinical testing during the current COVID-19 pandemic. Despite the numerous benefits, sources of variability in sample storage, handling, and processing methods can make WBE data difficult to generalize. We performed an experiment to determine sources of variability in WBE data including the impact of storage time, handling, and processing techniques on the concentration of SARS-CoV-2 in wastewater influent from three wastewater treatment plants (WWTP) in North Carolina over 19 days. The SARS-CoV-2 concentration in influent samples held at 4°C did not degrade significantly over the 19-day experiment. Heat pasteurization did not significantly impact the concentration of SARS-CoV-2 at two of the three WWTP but did reduce viral recovery at the WWTP with the smallest population size served. On each processing date, one filter from each sample was processed immediately while a replicate filter was frozen at -80°C. Once processed, filters previously frozen were found to contain slightly higher concentrations (<0.2 log copies/L) than their immediately processed counterparts, indicating freezing filters is a viable method for delayed quantification and may even improve recovery at WWTP with low viral concentrations. Investigation of factors contributing to variability during sample processing indicated that analyst experience level contributed significantly (p<0.001) to accepted droplet generation while extraction efficiency and reverse transcription efficiency contributed significantly (p<0.05) to day-to-day SARS-CoV-2 variability. This study provides valuable practical information for minimizing decay and/or loss of SARS CoV-2 in wastewater influent while adhering to safety procedures, promoting efficient laboratory workflows, and accounting for sources of variability.

Centralized and decentralized wastewater-based epidemiology to infer COVID-19 transmission - A brief review

Wastewater-based epidemiology has shown to be a promising and innovative approach to measure a wide variety of illicit drugs that are consumed in the communities. In the same way as for illicit drugs, wastewater-based epidemiology is a promising approach to understand the prevalence of viruses in a community-level.

The ongoing coronavirus disease 2019 (COVID-19) pandemic created an unprecedented burden on public health and diagnostic laboratories all over the world because of the need for massive laboratory testing. Many studies have shown the applicability of a centralized wastewater-based epidemiology (WBE) approach, where samples are collected at WWTPs. A more recent concept is a decentralized approach for WBE where samples are collected at different points of the sewer system and at polluted water bodies. The second being particularly important in countries where there are insufficient connections from houses to municipal sewage pipelines and thus untreated wastewater is discharged directly in environmental waters.

A decentralized approach can be used to focus the value of diagnostic tests in what we call targeted-WBE, by monitoring wastewater in parts of the population where an outbreak is likely to happen, such as student dorms, retirement homes and hospitals. A combination of centralized and decentralized WBE should be considered for an affordable, sustainable, and successful WBE implementation in high-, middle- and low-income countries.

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.

Sheet, Surveillance, Strategy, Salvage and Shield in global biodefense system to protect the public health and tackle the incoming pandemics

The pandemic of COVID-19 challenges the global health system and raises our concerns on the next waves of other emerging infectious diseases. Considering the lessons from the failure of world's pandemic warning system against COVID-19, many scientists and politicians have mentioned different strategies to improve global biodefense system, among which Sheet, Surveillance, Strategy, Salvage and Shield (5S) are frequently discussed. Nevertheless, the current focus is mainly on the optimization and management of individual strategy, and there are limited attempts to combine the five strategies as an integral global biodefense system. Sheet represents the biosafety datasheet for biohazards in natural environment and human society, which helps our deeper understanding on the geographical pattern, transmission routes and infection mechanism of pathogens. Online surveillance and prognostication network is an environmental Surveillance tool for monitoring the outbreak of pandemic diseases and alarming the risks to take emergency actions, targeting aerosols, waters, soils and animals. Strategy is policies and legislations for social distancing, lockdown and personal protective equipment to block the spread of infectious diseases in communities. Clinical measures are Salvage on patients by innovating appropriate medicines and therapies. The ultimate defensive Shield is vaccine development to protect healthy crowds from infection. Fighting against COVID-19 and other emerging infectious diseases is a long rocky journey, requiring the common endeavors of scientists and politicians from all countries around the world. 5S in global biodefense system bring a ray of light to the current darkest and future road from environmental and geographical perspectives.

Detection and quantification of SARS-CoV-2 RNA in wastewater influent in relation to reported COVID-19 incidence in Finland

Wastewater-based surveillance is a cost-effective concept for monitoring COVID-19 pandemics at a population level. Here, SARS-CoV-2 RNA was monitored from a total of 693 wastewater (WW) influent samples from 28 wastewater treatment plants (WWTP, N = 21-42 samples per WWTP) in Finland from August 2020 to May 2021, covering WW of ca. 3.3 million inhabitants (∼ 60% of the Finnish population). Quantity of SARS-CoV-2 RNA fragments in 24 h-composite samples was determined by using the ultrafiltration method followed by nucleic acid extraction and CDC N2 RT-qPCR assay. SARS-CoV-2 RNA signals at each WWTP were compared over time to the numbers of confirmed COVID-19 cases (14-day case incidence rate) in the sewer network area. Over the 10-month surveillance period with an extensive total number of samples, the detection rate of SARS-CoV-2 RNA in WW was 79% (including 6% uncertain results, i.e., amplified only in one out of four, two original and two ten-fold diluted replicates), while only 24% of all samples exhibited gene copy numbers above the quantification limit. The range of the SARS-CoV-2 detection rate in WW varied from 33% (including 10% uncertain results) in Pietarsaari to 100% in Espoo. Only six out of 693 WW samples were positive with SARS-COV-2 RNA when the reported COVID-19 case number from the preceding 14 days was zero. Overall, the 14-day COVID-19 incidence was 7.0, 18, and 36 cases per 100 000 persons within the sewer network area when the probability to detect SARS-CoV-2 RNA in wastewater samples was 50%, 75% and 95%, respectively. The quantification of SARS-CoV-2 RNA required significantly more COVID-19 cases: the quantification rate was 50%, 75%, and 95% when the 14-day incidence was 110, 152, and 223 COVID-19 cases, respectively, per 100 000 persons. Multiple linear regression confirmed the relationship between the COVID-19 incidence and the SARS-CoV-2 RNA quantified in WW at 15 out of 28 WWTPs (overall R² = 0.36, p < 0.001). At four of the 13 WWTPs where a significant relationship was not found, the SARS-CoV-2 RNA remained below the quantification limit during the whole study period. In the five other WWTPs, the sewer coverage was less than 80% of the total population in the area and thus the COVID-19 cases may have been inhabitants from the areas not covered. Based on the results obtained, WW-based surveillance of SARS-CoV-2 could be used as an indicator for local and national COVID-19 incidence trends. Importantly, the determination of SARS-CoV-2 RNA fragments from WW is a powerful and non-invasive public health surveillance measure, independent of possible changes in the clinical testing strategies or in the willingness of individuals to be tested for COVID-19.

Emergence of SARS-CoV-2 Alpha lineage and its correlation with quantitative wastewater-based epidemiology data

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) gave rise to an international public health emergency in 3 months after its emergence in Wuhan, China. Typically for an RNA virus, random mutations occur constantly leading to new lineages, incidental with a higher transmissibility. The highly infective alpha lineage, firstly discovered in the UK, led to elevated mortality and morbidity rates as a consequence of Covid-19, worldwide. Wastewater surveillance proved to be a powerful tool for early detection and subsequent monitoring of the dynamics of SARS-CoV-2 and its variants in a defined catchment. Using a combination of sequencing and RT-qPCR approaches, we investigated the total SARS-CoV-2 concentration and the emergence of the alpha lineage in wastewater samples in Vienna, Austria linking it to clinical data. Based on a non-linear regression model and occurrence of signature mutations, we conclude that the alpha variant was present in Vienna sewage samples already in December 2020, even one month before the first clinical case was officially confirmed and reported by the health authorities. This provides evidence that a well-designed wastewater monitoring approach can provide a fast snapshot and may detect the circulating lineages in wastewater weeks before they are detectable in the clinical samples. Furthermore, declining 14 days prevalence data with simultaneously increasing SARS-CoV-2 total concentration in wastewater indicate a different shedding behavior for the alpha variant. Overall, our results support wastewater surveillance to be a suitable approach to spot early circulating SARS-CoV-2 lineages based on whole genome sequencing and signature mutations analysis.

Comparison of high-frequency in-pipe SARS-CoV-2 wastewater-based surveillance to concurrent COVID-19 random clinical testing on a public U.S. university campus

Wastewater-based epidemiology (WBE) is utilized globally as a tool for quantifying the amount of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) within communities, yet the efficacy of community-level wastewater monitoring has yet to be directly compared to random Coronavirus Disease of 2019 (COVID-19) clinical testing; the best-supported method of virus surveillance within a single population. This study evaluated the relationship between SARS-CoV-2 RNA in raw wastewater and random COVID-19 clinical testing on a large university campus in the Southwestern United States during the Fall 2020 semester. Daily composites of wastewater (24-hour samples) were collected three times per week at two campus locations from 16 August 2020 to 1 January 2021 (n = 95) and analyzed by reverse transcriptase-quantitative polymerase chain reaction (RT-qPCR) targeting the SARS-CoV-2 E gene. Campus populations were estimated using campus resident information and anonymized, unique user Wi-Fi connections. Resultant trends of SARS-CoV-2 RNA levels in wastewater were consistent with local and nationwide pandemic trends showing peaks in infections at the start of the Fall semester in mid-August 2020 and mid-to-late December 2020. A strong positive correlation (r = 0.71 (p < 0.01); n = 15) was identified between random COVID-19 clinical testing and WBE surveillance methods, suggesting that wastewater surveillance has a predictive power similar to that of random clinical testing. Additionally, a comparative cost analysis between wastewater and clinical methods conducted here show that WBE was more cost effective, providing data at 1.7% of the total cost of clinical testing ($6042 versus $338,000, respectively). We conclude that wastewater monitoring of SARS-CoV-2 performed in tandem with random clinical testing can strengthen campus health surveillance, and its economic advantages are maximized when performed routinely as a primary surveillance method, with random clinical testing reserved for an active outbreak situation.

Improved methods for the detection and quantification of SARS-CoV-2 RNA in wastewater

Since the start of the COVID-19 pandemic, different methods have been used to detect the presence of genetic material of SARS-CoV-2 in wastewater. The use of wastewater for SARS-CoV-2 RNA detection and quantification showed different problems, associated to the complexity of the matrix and the lack of standard methods used to analyze the presence of an enveloped virus, such as coronavirus. Different strategies for the concentration process were selected to carry out the detection and quantification of SARS-CoV-2 RNA in wastewater: (a) aluminum hydroxide adsorption-precipitation, (b) pre-treatment with glycine buffer and precipitation with polyethylene-glycol (PEG) and (c) ultrafiltration (Centricon). Our results showed that the reduction of organic matter, using the pre-treatment with glycine buffer before the concentration with Centricon or aluminum hydroxide adsorption-precipitation, improved the recovery percentage of the control virus, Mengovirus (MgV) (8.37% ± 5.88 n = 43; 6.97% ± 6.51 n = 20, respectively), and the detection of SARS-CoV-2 in comparison with the same methodology without a pre-treatment. For the concentration with Centricon, the use of 100 mL of wastewater, instead of 200 mL, increased the MgV recovery, and allowed a positive detection of SARS-CoV-2 with N1 and N2 targets. The quantity of SARS-CoV-2 RNA detected in wastewater did not show a direct correlation with the number of confirmed cases, but the study of its upwards or downwards trend over time enabled the detection of an increase of epidemiological data produced in September 2020, January 2021 and April 2021.