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

Efficient concentration of viral nucleic acid in wastewater through surfactant releasing and a two-step magnetic bead extraction and purification

Wastewater-based epidemiology (WBE) is a valuable complement to clinical monitoring, allowing for effective surveillance of viral infections in populations, and tracking the presence and the epidemiological dynamics of various infectious pathogens in communities. However, virus loads are usually low-abundant in wastewater, and current virus concentration methods for WBE are laborious and time-consuming with low recovery efficiency. To address these challenges, we have developed a magnetic bead-based semi-automated method involving extraction and purification to directly concentrate viral nucleic acids from sewage within 55 min. Prior to concentration, 0.5 % LDS was introduced to pretreat wastewater to inactivate viruses and release viral nucleic acids from both liquid and solid fractions to improve recovery. Under optimal conditions, the concentration method combined with reverse transcription-quantitative polymerase chain reaction (RT-qPCR) can detect severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) RNA added exogenously in wastewater as low as 4.9 copies/mL within 2.5 h, with an average recovery rate exceeding 80 %. Testing real sewages proved the applicability of the method to detect multiple viruses in different sewages. Additionally, variants of SARS-CoV-2 were successfully identified by multiplex amplicon sequencing in two samples. In conclusion, the new method could provide a much more efficient way for WBE of pathogenic viruses in various sewages.

Machine learning surveillance of foodborne infectious diseases using wastewater microbiome, crowdsourced, and environmental data

Clostridium perfringens (CP) is a common cause of foodborne infection, leading to significant human health risks and a high economic burden. Thus, effective CP disease surveillance is essential for preventive and therapeutic interventions; however, conventional practices often entail complex, resource-intensive, and costly procedures. This study introduced a data-driven machine learning (ML) modeling framework for CP-related disease surveillance. It leveraged an integrated dataset of municipal wastewater microbiome (e.g., CP abundance), crowdsourced (CP-related web search keywords), and environmental data. Various optimization strategies, including data integration, data normalization, model selection, and hyperparameter tuning, were implemented to improve the ML modeling performance, leading to enhanced predictions of CP cases over time. Explainable artificial intelligence methods identified CP abundance as the most reliable predictor of CP disease cases. Multi-omics subsequently revealed the presence of CP and its genotypes/toxinotypes in wastewater, validating the utility of microbiome-data-enabled ML surveillance for foodborne diseases. This ML-based framework thus exhibits significant potential for complementing and reinforcing existing disease surveillance systems.

Integrative metagenomic dissection of last-resort antibiotic resistance genes and mobile genetic elements in hospital wastewaters

Hospital wastewater is a critical source of antimicrobial resistance (AMR), which facilitates the proliferation and spread of clinically significant antimicrobial resistance genes (ARGs) and pathogenic bacteria. This study utilized metagenomic approaches, including advanced binning techniques, such as MetaBAT2, MaxBin2, and CONCOCT, which offer significant improvements in accuracy and completeness over traditional binning methods. These methods were used to comprehensively assess the dynamics and composition of resistomes and mobilomes in untreated wastewater samples taken from two general hospitals and one cancer hospital. This study revealed a diverse bacterial landscape, largely consisting of Proteobacteria, Firmicutes, Bacteroidetes, and Actinobacteria, with notable variations in microbial composition among hospitals. Analysis of the top 15 genera showed unique microbial pattern distribution in each hospital: Aeromonas was predominant in 1stHWTS (49.39 %), Acidovorax in the CAHWTS at 16.85 %, and Escherichia and Bacteroides in the 2ndHWTS at 11.44 % and 11.33 %, respectively. A total of 114 pathogenic bacteria were identified, with drug-resistant Aeromonas caviae and Escherichia coli being the most prevalent. The study identified 34 types and 1660 subtypes of ARGs, including important last-resort antibiotic resistance genes (LARGs), such as blaNDM, mcr, and tet(X). Using metagenomic binning, this study uncovered distinct patterns of host-resistance associations, particularly with Proteobacteria and Firmicutes. Network analysis highlighted the complex interactions among ARGs, mobile genetic elements (MGEs), and bacterial species, all contributing to the dissemination of AMR. These findings emphasize the intricate nature of AMR in hospital wastewater and the influence of hospital-specific factors on microbial resistance patterns. This study provides support for implementing integrated management strategies, including robust surveillance, advanced wastewater treatment, and strict antibiotic stewardship, to control the dissemination of AMR. Understanding the interplay among bacterial communities, ARGs, and MGEs is important for developing effective public health measures against AMR.

Metagenomic profiling of raw wastewater in Portugal highlights microbiota and resistome signatures of public health interest beyond the usual suspects

In response to the rapid emergence and dissemination of antimicrobial resistant bacteria (ARB) and genes (ARGs), integrated surveillance systems are needed to address antimicrobial resistance (AMR) within the One Health Era. Wastewater analyses enable biomarker monitoring at the sewershed level, offering timely insights into pathogen circulation and ARB/ARGs trends originating from different compartments. During two consecutive epidemic waves of the COVID-19 pandemic in Portugal, taxonomic and functional composition of raw urban wastewater from two wastewater treatment plants (WWTPs) representing one million in equivalent population, located in the main urban areas of the country, were profiled by shotgun metagenomics. Hospital wastewater from two central hospitals located in the WWTPs catchment areas were also sequenced. The resistome and virulome were profiled using metagenomic assemblies without taxonomic constraint, and then specifically characterized for ESKAPE pathogens. Urban and hospital wastewater exhibited specific microbiota signatures, Pseudomonadota dominated in the first and Bacteroidota in the latter. Correlation network analyses highlighted 85 (out of top 100) genera co-occurring across samples. The most frequent ARGs were classified in the multidrug, tetracyclines, and Macrolides, Lincosamides, Streptogramins (MLS) classes. Links established between AMR determinants and bacterial hosts evidenced that the diversity and abundance of ARGs is not restricted to ESKAPE, being also highly predominant among emergent enteropathogens, like Aeromonas and Aliarcobacter, or in the iron (II) oxidizer Acidovorax. The Aliarcobacter genus accumulated high abundance of sulphonamides and polymyxins ARGs, while Acinetobacter and Aeromonas hosted the highest abundance of ARGs against beta-lactams. Other bacteria (e.g. Clostridioides, Francisella, Vibrio cholerae) and genes (e.g. vanA-type vancomycin resistance) of public health interest were detected, with targeted monitoring efforts being needed to establish informative baseline data. Altogether, results highlight that wastewater monitoring is a valuable component of pathogen and AMR surveillance in healthy populations, providing a community-representative snapshot of public health trends beyond priority pathogens.

Piloting wastewater-based surveillance of norovirus in England

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

Long Amplicon Nanopore Sequencing for Dual-Typing RdRp and VP1 Genes of Norovirus Genogroups I and II in Wastewater

Noroviruses (NoVs) are the leading cause of non-bacterial gastroenteritis with societal costs of US$60.3 billion per annum. Development of a long amplicon nanopore-based method for dual-typing the RNA-dependent RNA polymerase (RdRp) and major structural protein (VP1) regions from a single RNA fragment could improve existing norovirus typing methods. Application to wastewater-based epidemiology (WBE) and environmental testing could enable the discovery of novel types and improve outbreak tracking and source apportionment. Here, we have developed such a method with a consensus-based bioinformatics pipeline and optimised reverse transcription (RT) and PCR procedures. Inhibitor removal and LunaScript® RT gave robust amplification of the ≈ 1000 bp RdRP + VP1 amplicon for both the GI and GII PCR assays. Platinum™ Taq polymerase showed good sensitivity and reduced levels non-specific amplification (NSA) when compared to other polymerases. Optimised PCR annealing temperatures significantly reduced NSA (51.3 and 42.4% for GI and GII), increased yield (86.5% for GII) and increased taxa richness (57.7%) for GII. Analysis of three NoV positive faecal samples showed 100% nucleotide similarity with Sanger sequencing. Eight GI genotypes, 11 polymerase types (p-types) and 13 combinations were detected in wastewater along with 4 GII genotypes, 4 p-types and 8 combinations; highlighting the diversity of norovirus taxa present in wastewater in England. The most common genotypes detected in clinical samples were all detected in wastewater while we also frequently detected several GI genotypes not reported in the clinical data. Application of this method into a WBE scheme, therefore, may allow for more accurate measurement of norovirus diversity within the population.

Wastewater Surveillance Pilot at US Military Installations: Cost Model Analysis

Background

The COVID-19 pandemic highlighted the need for pathogen surveillance systems to augment both early warning and outbreak monitoring/control efforts. Community wastewater samples provide a rapid and accurate source of environmental surveillance data to complement direct patient sampling. Due to its global presence and critical missions, the US military is a leader in global pandemic preparedness efforts. Clinical testing for COVID-19 on US Air Force (USAF) bases (AFBs) was effective but costly with respect to direct monetary costs and indirect costs due to lost time. To remain operating at peak capacity, such bases sought a more passive surveillance option and piloted wastewater surveillance (WWS) at 17 AFBs to demonstrate feasibility, safety, utility, and cost-effectiveness from May 2021 to January 2022.

Objective

We model the costs of a wastewater program for pathogens of public health concern within the specific context of US military installations using assumptions based on the results of the USAF and Joint Program Executive Office for Chemical, Biological, Radiological and Nuclear Defense pilot program. The objective was to determine the cost of deploying WWS to all AFBs relative to clinical swab testing surveillance regimes.

Methods

A WWS cost projection model was built based on subject matter expert input and actual costs incurred during the WWS pilot program at USAF AFBs. Several SARS-CoV-2 circulation scenarios were considered, and the costs of both WWS and clinical swab testing were projected. Analysis was conducted to determine the break-even point and how a reduction in swab testing could unlock funds to enable WWS to occur in parallel.

Results

Our model confirmed that WWS is complementary and highly cost-effective when compared to existing alternative forms of biosurveillance. We found that the cost of WWS was between US $10.5-$18.5 million less expensive annually in direct costs as compared to clinical swab testing surveillance. When the indirect cost of lost work was incorporated, including lost work associated with required clinical swab testing, we estimated that over two-thirds of clinical swab testing could be maintained with no additional costs upon implementation of WWS.

Conclusions

Our results support the adoption of WWS across US military installations as part of a more comprehensive and early warning system that will enable adaptive monitoring during disease outbreaks in a more cost-effective manner than swab testing alone.

Leveraging wastewater-based epidemiology to monitor the spread of neglected tropical diseases in African communities

Neglected tropical diseases continue to cause a significant burden worldwide, with Africa accounting for more than one-third of the global burden. Over the past decade, progress has been made in eliminating, controlling, and eradicating these diseases in Africa. By December 2022, 47 out of 54 African countries had eliminated at least one neglected tropical disease, and more countries were close to achieving this milestone. Between 2020 and 2021, there was an 80 million reduction in people requiring intervention. However, continued efforts are needed to manage neglected tropical diseases and address their social and economic burden, as they deepen marginalisation and stigmatisation. Wastewater-based epidemiology involves analyzing wastewater to detect and quantify biomarkers of disease-causing pathogens. This approach can complement current disease surveillance systems in Africa and provide an additional layer of information for monitoring disease spread and detecting outbreaks. This is particularly important in Africa due to limited traditional surveillance methods. Wastewater-based epidemiology also provides a tsunami-like warning system for neglected tropical disease outbreaks and can facilitate timely intervention and optimised resource allocation, providing an unbiased reflection of the community's health compared to traditional surveillance systems. In this review, we highlight the potential of wastewater-based epidemiology as an innovative approach for monitoring neglected tropical disease transmission within African communities and improving existing surveillance systems. Our analysis shows that wastewater-based epidemiology can enhance surveillance of neglected tropical diseases in Africa, improving early detection and management of Buruli ulcers, hookworm infections, ascariasis, schistosomiasis, dengue, chikungunya, echinococcosis, rabies, and cysticercosis for better disease control.

Definition of a concentration and RNA extraction protocol for optimal whole genome sequencing of SARS-CoV-2 in wastewater (ANRS0160)

Monitoring the presence of RNA from emerging pathogenic viruses, such as SARS-CoV-2, in wastewater (WW) samples requires suitable methods to ensure an effective response. Genome sequencing of WW is one of the crucial methods, but it requires high-quality RNA in sufficient quantities, especially for monitoring emerging variants. Consequently, methods for viral concentration and RNA extraction from WW samples have to be optimized before sequencing. The purpose of this study was to achieve high coverage (≥ 90 %) and sequencing depth (at least ≥200×) even for low initial RNA concentrations (< 105 genome copies (GC)/L) in WW. A further objective was to determine the range of SARS-CoV-2 RNA concentrations that allow high-quality sequencing, and the optimal sample volume for analysis. Ultrafiltration (UF) methods were used to concentrate viral particles from large influent samples (up to 500 mL). An RNA extraction protocol using silica beads, neutral phenol-chloroform treatment, and a PCR inhibitor removal kit was chosen for its effectiveness in extracting RNA and eliminating PCR inhibitors, as well as its adaptability for use with large influent samples. Recovery rates ranged from 24 % to 63 % (N = 17) for SARS-CoV-2 naturally present in WW samples. 200 mL WW samples can be enough for UF concentration, as they showed high quality sequencing analyses with between 5 × 104 GC/L and 6 × 103 GC/L. Below 6 × 103 GC/L, high-quality sequencing was also achieved for ∼40 % of the samples using 500 mL of WW. Sequencing analysis for variant detection was performed on 200 mL WW samples with coverage of >95 % and sequencing depth of >1000×. Analyses revealed the predominance of variant EG.5, known as Eris (66 %-100 %). The use of UF methods in combination with a suitable RNA extraction protocol appear promising for sequencing enveloped viruses in WW in a context of viral emergence.

Antibiotic resistance genes and crAssphage in hospital wastewater and a canal receiving the treatment effluent

Hospital wastewater is a major hotspot for the spread of antimicrobial resistance (AMR) in aquatic ecosystems. This study aimed to investigate the prevalence of antibiotic resistance genes (ARGs) and their correlation with crAssphage in a hospital wastewater treatment plant (HWWTP) and a receiving canal. Water samples were analyzed for 94 ARGs and crAssphage relative to the 16S rRNA using high-throughput quantitative polymerase chain reaction (HT-qPCR). Subsequently, 7 ARGs and crAssphage were selected and quantified using qPCR. The results showed that the detected genes ranged from 79 to 93 out of 95 genes. The raw wastewater (WW) samples had the highest gene diversity compared to the upstream canal, which had less diversity than downstream samples, as determined by HT-qPCR. The blaGES was the most abundant in WW samples, while qacEΔ1, merA, IS6100, tnpA, and IS26 showed high prevalence throughout the treatment processes. The concentrations of intI1, sul1, blaTEM,blaNDM,blaVIM,tetQ, mcr-1, crAssphage, and 16S rRNA, measured using qPCR, were the highest in WW and significantly reduced in treated water samples. Although some water quality parameters, such as total suspended solids and dissolved oxygen, did not significantly differ before and after treatment, removal efficiency ranged from 0.60 to 3.23 log reduction values (LRV). The highest LRV was observed for the tetQ, whereas the mcr-1 had the lowest LRV. Strong positive correlations among the absolute concentrations of ARGs and crAssphage were observed (Spearman's rho = 0.6-1.0), and biochemical oxygen demand correlated with blaTEM and blaVIM (Spearman's rho = 0.6). These results indicate that crAssphage and water quality could reflect the distribution of other ARGs throughout the HWWTP. Further studies are needed to underscore the importance of monitoring ARGs and genetic markers such as crAssphage in HWWTPs and their receiving waters to enhance our understanding of ARG distribution.

High-throughput microfluidic quantitative PCR system for the simultaneous detection of antibiotic resistance genes and bacterial and viral pathogens in wastewater

Comprehensive data on bacterial and viral pathogens of diarrhea and studies applying culture-independent methods for examining antibiotic resistance in wastewater are lacking. This study aimed to simultaneously quantify antibiotic resistance genes (ARGs), class 1 integron-integrase (int1), bacterial and viral pathogens of diarrhea, 16S rRNA, and other indicators using a high-throughput quantitative PCR (HT-qPCR) system. Thirty-six grab wastewater samples from a wastewater treatment plant in Japan, collected three times a month between August 2022 and July 2023, were centrifuged, followed by nucleic acid extraction, reverse transcription, and HT-qPCR. Fourteen targets were included, and HT-qPCR was performed on the Biomark X9™ System (Standard BioTools). For all qPCR assays, R2 was ≥0.978 and the efficiencies ranged from 90.5% to 117.7%, exhibiting high performance. Of the 36 samples, 20 (56%) were positive for Norovirus genogroup II (NoV-GII), whereas Salmonella spp. and Campylobacter jejuni were detected in 24 (67%) and Campylobacter coli in 13 (36%) samples, with mean concentrations ranging from 3.2 ± 0.8 to 4.7 ± 0.3 log10 copies/L. NoV-GII detection ratios and concentrations were higher in winter and spring. None of the pathogens of diarrhea correlated with acute gastroenteritis cases, except for NoV-GII, suggesting the need for data on specific bacterial infections to validate bacterial wastewater-based epidemiology (WBE). All samples tested positive for sul1, int1, and blaCTX-M, irrespective of season. The less explored blaNDM-1 showed a wide prevalence (>83%) and consistent abundance ranging from 4.3 ± 1.0 to 4.9 ± 0.2 log10 copies/L in all seasons. sul1 was the predominant ARG, whereas absolute abundances of 16S rRNA, int1, and blaCTX-M varied seasonally. int1 was significantly correlated with blaCTX-M in autumn and spring, whereas it showed no correlation with blaNDM-1, questioning the applicability of int1 as a sole indicator of overall resistance determinants. This study exhibited that the HT-qPCR system is pivotal for WBE.

Advancing Public Health Surveillance: Integrating Modeling and GIS in the Wastewater-Based Epidemiology of Viruses, a Narrative Review

This review article will present a comprehensive examination of the use of modeling, spatial analysis, and geographic information systems (GIS) in the surveillance of viruses in wastewater. With the advent of global health challenges like the COVID-19 pandemic, wastewater surveillance has emerged as a crucial tool for the early detection and management of viral outbreaks. This review will explore the application of various modeling techniques that enable the prediction and understanding of virus concentrations and spread patterns in wastewater systems. It highlights the role of spatial analysis in mapping the geographic distribution of viral loads, providing insights into the dynamics of virus transmission within communities. The integration of GIS in wastewater surveillance will be explored, emphasizing the utility of such systems in visualizing data, enhancing sampling site selection, and ensuring equitable monitoring across diverse populations. The review will also discuss the innovative combination of GIS with remote sensing data and predictive modeling, offering a multi-faceted approach to understand virus spread. Challenges such as data quality, privacy concerns, and the necessity for interdisciplinary collaboration will be addressed. This review concludes by underscoring the transformative potential of these analytical tools in public health, advocating for continued research and innovation to strengthen preparedness and response strategies for future viral threats. This article aims to provide a foundational understanding for researchers and public health officials, fostering advancements in the field of wastewater-based epidemiology.

Surveillance of influenza A and B viruses from community and hospital wastewater treatment plants

Influenza virus is a well-known pathogen that can cause epidemics and pandemics. Several surveillance methods are being followed to monitor the transmission patterns and spread of influenza in the community. Wastewater-based Epidemiology (WBE) can serve as an additional tool to detect the presence of influenza viruses. The current study primarily focuses on surveillance of Influenza A and Influenza B in wastewater treatment plant (WWTP) samples. A total of 100 wastewater samples were collected in July (n = 50) and August (n = 50) 2023 from four different WWTPs in Manipal and Udupi, district of Karnataka, India. The WWTP samples were processed and tested by Real-Time reverse transcriptase PCR (RT-PCR). The data generated was analysed in comparison with the clinical Influenza cases. Of the 100 samples, 18 (18%) tested positive for Influenza A virus and 2 (2%) tested positive for Influenza B virus, with a viral load ranging 1.4 x 102-2.2 x 103 gc/L for influenza A virus and 5.2 x 103-7.7 x 103gc/L for influenza B virus. On correlating the WWTP positivity with clinical case, it was found that influenza clinical cases and virus positivity in wastewater increased simultaneously, emphasizing WBE as a concurrent method for monitoring influenza virus activity.

An early warning indicator trained on stochastic disease-spreading models with different noises

The timely detection of disease outbreaks through reliable early warning signals (EWSs) is indispensable for effective public health mitigation strategies. Nevertheless, the intricate dynamics of real-world disease spread, often influenced by diverse sources of noise and limited data in the early stages of outbreaks, pose a significant challenge in developing reliable EWSs, as the performance of existing indicators varies with extrinsic and intrinsic noises. Here, we address the challenge of modelling disease when the measurements are corrupted by additive white noise, multiplicative environmental noise and demographic noise into a standard epidemic mathematical model. To navigate the complexities introduced by these noise sources, we employ a deep learning algorithm that provides EWS in infectious disease outbreaks by training on noise-induced disease-spreading models. The indicator's effectiveness is demonstrated through its application to real-world COVID-19 cases in Edmonton and simulated time series derived from diverse disease spread models affected by noise. Notably, the indicator captures an impending transition in a time series of disease outbreaks and outperforms existing indicators. This study contributes to advancing early warning capabilities by addressing the intricate dynamics inherent in real-world disease spread, presenting a promising avenue for enhancing public health preparedness and response efforts.

Adjusting for dilution in wastewater using biomarkers: A practical approach

We developed a biomarker-based approach to quantify in-sewer dilution by measuring wastewater quality parameters (ammoniacal-N, orthophosphate, crAssphage). This approach can enhance the environmental management of wastewater treatment works (WWTW) by optimising their operation and providing cost-effective information on the health and behaviour of populations and their interactions with the environment through wastewater-based epidemiology (WBE). Our method relies on site specific baselines calculated for each biomarker. These baselines reflect the sewer conditions without the influence of rainfall-derived inflow and infiltration (RDII). Ammoniacal-N was the best candidate to use as proxy for dilution. We demonstrated that the dilution calculated using biomarkers correlates well with the dilution indicated by measured flow. In some instances, the biomarkers showed much higher dilution than measured flows. These differences were attributed to the loss of flow volume at wastewater treatment works due to the activation of combined sewer overflows (CSOs) and/or storm tanks. Using flow measured directly at the WWTW could therefore result in underestimation of target analyte loads.

A narrative review of wastewater surveillance: pathogens of concern, applications, detection methods, and challenges

Introduction

The emergence and resurgence of pathogens have led to significant global health challenges. Wastewater surveillance has historically been used to track water-borne or fecal-orally transmitted pathogens, providing a sensitive means of monitoring pathogens within a community. This technique offers a comprehensive, real-time, and cost-effective approach to disease surveillance, especially for diseases that are difficult to monitor through individual clinical screenings.

Methods

This narrative review examines the current state of knowledge on wastewater surveillance, emphasizing important findings and techniques used to detect potential pathogens from wastewater. It includes a review of literature on the detection methods, the pathogens of concern, and the challenges faced in the surveillance process.

Results

Wastewater surveillance has proven to be a powerful tool for early warning and timely intervention of infectious diseases. It can detect pathogens shed by asymptomatic and pre-symptomatic individuals, providing an accurate population-level view of disease transmission. The review highlights the applications of wastewater surveillance in tracking key pathogens of concern, such as gastrointestinal pathogens, respiratory pathogens, and viruses like SARS-CoV-2.

Discussion

The review discusses the benefits of wastewater surveillance in public health, particularly its role in enhancing existing systems for infectious disease surveillance. It also addresses the challenges faced, such as the need for improved detection methods and the management of antimicrobial resistance. The potential for wastewater surveillance to inform public health mitigation strategies and outbreak response protocols is emphasized.

Conclusion

Wastewater surveillance is a valuable tool in the fight against infectious diseases. It offers a unique perspective on the spread and evolution of pathogens, aiding in the prevention and control of disease epidemics. This review underscores the importance of continued research and development in this field to overcome current challenges and maximize the potential of wastewater surveillance in public health.

Wastewater and clinical surveillance of respiratory viral pathogens on a university campus

Areas of dense population congregation are prone to experience respiratory virus outbreaks. We monitored wastewater and clinic patients for the presence of respiratory viruses on a large, public university campus. Campus sewer systems were monitored in 16 locations for the presence of viruses using next generation sequencing over 22 weeks in 2023. During this period, we detected a surge in human adenovirus (HAdV) levels in wastewater. Hence, we initiated clinical surveillance at an on-campus clinic from patients presenting with acute respiratory infection. From whole genome sequencing of 123 throat and/or nasal swabs collected, we identified an outbreak of HAdV, specifically of HAdV-E4 and HAdV-B7 genotypes overlapping in time. The temporal dynamics and proportions of HAdV genotypes found in wastewater were corroborated in clinical infections. We tracked specific single nucleotide polymorphisms (SNPs) found in clinical virus sequences and showed that they arose in wastewater signals concordant with the time of clinical presentation, linking community transmission of HAdV to the outbreak. This study demonstrates how wastewater-based epidemiology can be integrated with surveillance at ambulatory healthcare settings to monitor areas prone to respiratory virus outbreaks and provide public health guidance.

Current state and future perspectives on de facto population markers for normalization in wastewater-based epidemiology: A systematic literature review

Wastewater-based epidemiology (WBE) and wastewater surveillance have become a valuable complementary data source to collect information on community-wide exposure through the measurement of human biomarkers in influent wastewater (IWW). In WBE, normalization of data with the de facto population that corresponds to a wastewater sample is crucial for a correct interpretation of spatio-temporal trends in exposure and consumption patterns. However, knowledge gaps remain in identifying and validating suitable de facto population biomarkers (PBs) for refinement of WBE back-estimations. WBE studies that apply de facto PBs (including hydrochemical parameters, utility consumption data sources, endo- and exogenous chemicals, biological biomarkers and signalling records) for relative trend analysis and absolute population size estimation were systematically reviewed from three databases (PubMed, Web of Science, SCOPUS) according to the PRISMA guidelines. We included in this review 81 publications that accounted for daily variations in population sizes by applying de facto population normalization. To date, a wide range of PBs have been proposed for de facto population normalization, complicating the comparability of normalized measurements across WBE studies. Additionally, the validation of potential PBs is complicated by the absence of an ideal external validator, magnifying the overall uncertainty for population normalization in WBE. Therefore, this review proposes a conceptual tier-based cross-validation approach for identifying and validating de facto PBs to guide their integration for i) relative trend analysis, and ii) absolute population size estimation. Furthermore, this review also provides a detailed evaluation of the uncertainty observed when comparing different de jure and de facto population estimation approaches. This study shows that their percentual differences can range up to ±200 %, with some exceptions showing even larger variations. This review underscores the need for collaboration among WBE researchers to further streamline the application of de facto population normalization and to evaluate the robustness of different PBs in different socio-demographic communities.

Wastewater surveillance for viral pathogens: A tool for public health

A focus on water quality has intensified globally, considering its critical role in sustaining life and ecosystems. Wastewater, reflecting societal development, profoundly impacts public health. Wastewater-based epidemiology (WBE) has emerged as a surveillance tool for detecting outbreaks early, monitoring infectious disease trends, and providing real-time insights, particularly in vulnerable communities. WBE aids in tracking pathogens, including viruses, in sewage, offering a comprehensive understanding of community health and lifestyle habits. With the rise in global COVID-19 cases, WBE has gained prominence, aiding in monitoring SARS-CoV-2 levels worldwide. Despite advancements in water treatment, poorly treated wastewater discharge remains a threat, amplifying the spread of water-, sanitation-, and hygiene (WaSH)-related diseases. WBE, serving as complementary surveillance, is pivotal for monitoring community-level viral infections. However, there is untapped potential for WBE to expand its role in public health surveillance. This review emphasizes the importance of WBE in understanding the link between viral surveillance in wastewater and public health, highlighting the need for its further integration into public health management.

Tricks and tracks of prevalence, occurrences, treatment technologies, and challenges of mixtures of emerging contaminants in the environment: With special emphasis on microplastic

This paper aims to emphasize the occurrence of various emerging contaminant (EC) mixtures in natural ecosystems and highlights the primary concern arising from the unregulated release into soil and water, along with their impacts on human health. Emerging contaminant mixtures, including pharmaceuticals, personal care products, dioxins, polychlorinated biphenyls, pesticides, antibiotics, biocides, surfactants, phthalates, enteric viruses, and microplastics (MPs), are considered toxic contaminants with grave implications. MPs play a crucial role in transporting pollutants to aquatic and terrestrial ecosystems as they interact with the various components of the soil and water environments. This review summarizes that major emerging contaminants (ECs), like trimethoprim, diclofenac, sulfamethoxazole, and 17α-Ethinylestradiol, pose serious threats to public health and contribute to antimicrobial resistance. In addressing human health concerns and remediation techniques, this review critically evaluates conventional methods for removing ECs from complex matrices. The diverse physiochemical properties of surrounding environments facilitate the partitioning of ECs into sediments and other organic phases, resulting in carcinogenic, teratogenic, and estrogenic effects through active catalytic interactions and mechanisms mediated by aryl hydrocarbon receptors. The proactive toxicity of ECs mixture complexation and, in part, the yet-to-be-identified environmental mixtures of ECs represent a blind spot in current literature, necessitating conceptual frameworks for assessing the toxicity and risks with individual components and mixtures. Lastly, this review concludes with an in-depth exploration of future scopes, knowledge gaps, and challenges, emphasizing the need for a concerted effort in managing ECs and other organic pollutants.

Global WWTP Microbiome-based Integrative Information Platform: From experience to intelligence

Domestic and industrial wastewater treatment plants (WWTPs) are facing formidable challenges in effectively eliminating emerging pollutants and conventional nutrients. In microbiome engineering, two approaches have been developed: a top-down method focusing on domesticating seed microbiomes into engineered ones, and a bottom-up strategy that synthesizes engineered microbiomes from microbial isolates. However, these approaches face substantial hurdles that limit their real-world applicability in wastewater treatment engineering. Addressing this gap, we propose the creation of a Global WWTP Microbiome-based Integrative Information Platform, inspired by the untapped microbiome and engineering data from WWTPs and advancements in artificial intelligence (AI). This open platform integrates microbiome and engineering information globally and utilizes AI-driven tools for identifying seed microbiomes for new plants, providing technical upgrades for existing facilities, and deploying microbiomes for accidental pollution remediation. Beyond its practical applications, this platform has significant scientific and social value, supporting multidisciplinary research, documenting microbial evolution, advancing Wastewater-Based Epidemiology, and enhancing global resource sharing. Overall, the platform is expected to enhance WWTPs' performance in pollution control, safeguarding a harmonious and healthy future for human society and the natural environment.

Evaluation of different molecular systems for detection and quantification of SARS-CoV-2 RNA from wastewater samples

Wastewater-based epidemiology has proved to be a suitable approach for tracking the spread of epidemic agents including SARS-CoV-2 RNA. Different protocols have been developed for quantitative detection of SARS-CoV-2 RNA from wastewater samples, but little is known on their performance. In this study we compared three protocols based on Reverse Transcription Real Time-PCR (RT-PCR) and one based on Droplet Digital PCR (ddPCR) for SARS-CoV-2 RNA detection from 35 wastewater samples. Overall, SARS-CoV-2 RNA was detected by at least one method in 85.7 % of samples, while 51.4 %, 22.8 % and 8.6 % resulted positive with two, three or all four methods, respectively. Protocols based on commercial RT-PCR assays and on Droplet Digital PCR showed an overall higher sensitivity vs. an in-house assay. The use of more than one system, targeting different genes, could be helpful to increase detection sensitivity.

Antimicrobials and antimicrobial resistance genes in the shadow of COVID-19 pandemic: A wastewater-based epidemiology perspective

Higher usage of antimicrobial agents in both healthcare facilities and the communities has resulted in an increased spread of resistant bacteria. However, the improved infection prevention and control practices may also contribute to decreasing antimicrobial resistance (AMR). In the present study, wastewater-based epidemiology (WBE) approach was applied to explore the link between COVID-19 and the community usage of antimicrobials, as well as the prevalence of resistance genes. Longitudinal study has been conducted to monitor the levels of 50 antimicrobial agents (AAs), 24 metabolites, 5 antibiotic resistance genes (ARGs) and class 1 integrons (intI 1) in wastewater influents in 4 towns/cities over two years (April 2020 - March 2022) in the South-West of England (a total of 1,180 samples collected with 87,320 individual AA measurements and 8,148 ARG measurements). Results suggested higher loads of AAs and ARGs in 2021-22 than 2020-21, with beta-lactams, quinolones, macrolides and most ARGs showing statistical differences. In particular, the intI 1 gene (a proxy of environmental ARG pollution) showed a significant increase after the ease of the third national lockdown in England. Positive correlations for all quantifiable parent AAs and metabolites were observed, and consumption vs direct disposal of unused AAs has been identified via WBE. This work can help establish baselines for AMR status in communities, providing community-wide surveillance and evidence for informing public health interventions. Overall, studies focused on AMR from the start of the pandemic to the present, especially in the context of environmental settings, are of great importance to further understand the long-term impact of the pandemic on AMR.

Assessment of restriction measures implemented during COVID pandemics on community lifestyle choices via wastewater-based epidemiology

SARS-CoV-2 pandemic affected lifestyle habits, and the mental health and wellbeing of people around the world. In this manuscript, two towns (Paulton and Radstock) and two cities (Bath and Bristol) in Southwest England (> 1 million people) were monitored for two years using Wastewater-Based Epidemiology to assess impacts of COVID (including management measures such as lockdowns and movement restrictions) on community lifestyle choices: illicit drugs, legal stimulants, abused pharmaceuticals and pain pharma usage. Results were triangulated with key dates captured during the pandemic (national lockdowns, restrictions and social distancing measures, etc.). This highlighted a reduction in cocaine intake (as benzoylecgonine) (community average: -36 %) during the first lockdown and an increase in illicit drugs usage after the 3rd national lockdown (community averages for amphetamine: +8 %, cocaine/benzoylecgonine: +39 %, ketamine:+70 %) when restrictions were removed, and social interaction and recreational activities increased. There was a reduction in the intake of caffeine (as 1,7-dimethylxanthine) (community average:-39 %) after 3rd national lockdown coinciding with pubs reopening while nicotine intake (via cotinine) remained stable indicating lack of impact of COVID on smoking habits. Pain pharma often used in pain management resulting from injuries linked with sport activities (naproxen and diclofenac) showed decrease in usage due to lockdown restrictions in physical exercise and access to gyms/sport facilities.

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.

Optimisation of analytical methods for tuberculosis drug detection in wastewater: A multinational study

Wastewater-based epidemiology (WBE) is a robust tool for disease surveillance and monitoring of pharmaceutical consumption. However, monitoring tuberculosis (TB) drug consumption faces challenges due to limited data availability. This study aimed to optimise methods for detecting TB drugs in treated and untreated wastewater from four African countries: South Africa, Nigeria, Kenya, and Cameroon. The limit of detection (LOD) for these drugs ranged from a minimum of 2.20 (±1.02) for rifampicin to a maximum of 2.95 (±0.79) for pyrazinamide. A parallel trend was observed concerning the limit of quantification (LOQ), with rifampicin reporting the lowest average LOQ of 7.33 (±3.44) and pyrazinamide showing the highest average LOQ of 9.81 (±2.64). The variance in LOD and LOQ values could be attributed to factors such as drug polarity. Erythromycin and rifampicin exhibited moderately polar LogP values (2.6 and 2.95), indicating higher lipid affinity and lower water affinity. Conversely, ethambutol, pyrazinamide, and isoniazid displayed polar LogP values (-0.059, -0.6, and -0.7), suggesting lower lipid affinity and greater water affinity. The study revealed that storing wastewater samples for up to 5 days did not result in significant drug concentration loss, with concentration reduction remaining below 1 log throughout the storage period. Application of the optimised method for drug detection and quantification in both treated and untreated wastewater unveiled varied results. Detection frequencies varied among drugs, with ethambutol consistently most detected, while pyrazinamide and isoniazid were least detected in wastewater from only two countries. Most untreated wastewater samples had undetectable drug concentrations, ranging from 1.21 ng/mL for erythromycin to 54.61 ng/mL for isoniazid. This variability may suggest differences in drug consumption within connected communities. In treated wastewater samples, detectable drug concentrations ranged from 1.27 ng/mL for isoniazid to 10.20 ng/mL for ethambutol. Wastewater treatment plants exhibited variable removal efficiencies for different drugs, emphasising the need for further optimisation. Detecting these drugs in treated wastewater suggests potential surface water contamination and subsequent risks of human exposure, underscoring continued research's importance.

The National Wastewater Surveillance System (NWSS): From inception to widespread coverage, 2020-2022, United States

Wastewater surveillance is a valuable tool that can be used to track infectious diseases in a community. In September 2020, the Centers for Disease Control and Prevention (CDC) established the National Wastewater Surveillance System (NWSS) to coordinate and build the nation's capacity to detect and quantify concentrations of SARS-CoV-2 RNA in U.S. wastewater. This is the first surveillance summary of NWSS, covering September 1, 2020 to December 31, 2022. Through partnerships with state, tribal, local, and territorial health departments, NWSS became a national surveillance platform that can be readily expanded and adapted to meet changing public health needs. Beginning with 209 sampling sites in September 2020, NWSS rapidly expanded to >1500 sites by December 2022, covering ≈47 % of the U.S. population. As of December 2022, >152,000 unique wastewater samples have been collected by NWSS partners, primarily from wastewater treatment plants (WWTPs). WWTPs participating in NWSS tend to be larger than the average U.S. WWTP and serve more populated communities. In December 2022, ≈8 % of the nearly 16,000 U.S. WWTPs were participating in NWSS. NWSS partners used a variety of methods for sampling and testing wastewater samples; however, progress is being made to standardize these methods. In July 2021, NWSS partners started submitting SARS-CoV-2 genome sequencing data to NWSS. In October 2022, NWSS expanded to monkeypox virus testing, with plans to include additional infectious disease targets in the future. Through the rapid implementation and expansion of NWSS, important lessons have been learned. Wastewater surveillance programs should consider both surge and long-term capacities when developing an implementation plan, and early standardization of sampling and testing methods is important to facilitate data comparisons across sites. NWSS has proven to be a flexible and sustainable surveillance system that will continue to be a useful complement to case-based surveillance for guiding public health action.

Campus source to sink wastewater surveillance of severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2)

Wastewater-based surveillance (WBS) offers an aggregate, and cost-effective approach for tracking infectious disease outbreak prevalence within communities, that provides data on community health complementary to individual clinical testing. This study reports on a 16-month WBS initiative on a university campus in England, UK, assessing the presence of SARS-CoV-2 in sewers from large buildings, downstream sewer locations, raw wastewater, partially treated and treated effluents. Key findings include the detection of the Alpha (B.1.1.7) variant in wastewater, with 70 % of confirmed campus cases correlating with positive wastewater samples. Notably, ammonium nitrogen (NH4-N) levels showed a positive correlation (ρ = 0.543, p < 0.01) with virus levels at the large building scale, a relationship not observed at the sewer or wastewater treatment works (WWTW) levels due to dilution. The WWTW was compliant to wastewater standards, but the secondary treatment processes were not efficient for virus removal as SARS-CoV-2 was consistently detected in treated discharges. Tools developed through WBS can also be used to enhance traditional environmental monitoring of aquatic systems. This study provides a detailed source-to-sink evaluation, emphasizing the critical need for the widespread application and improvement of WBS. It showcases WBS utility and reinforces the ongoing challenges posed by viruses to receiving water quality.

Tracking SARS-CoV-2 variants of concern in wastewater: an assessment of nine computational tools using simulated genomic data

Wastewater-based surveillance (WBS) is an important epidemiological and public health tool for tracking pathogens across the scale of a building, neighbourhood, city, or region. WBS gained widespread adoption globally during the SARS-CoV-2 pandemic for estimating community infection levels by qPCR. Sequencing pathogen genes or genomes from wastewater adds information about pathogen genetic diversity, which can be used to identify viral lineages (including variants of concern) that are circulating in a local population. Capturing the genetic diversity by WBS sequencing is not trivial, as wastewater samples often contain a diverse mixture of viral lineages with real mutations and sequencing errors, which must be deconvoluted computationally from short sequencing reads. In this study we assess nine different computational tools that have recently been developed to address this challenge. We simulated 100 wastewater sequence samples consisting of SARS-CoV-2 BA.1, BA.2, and Delta lineages, in various mixtures, as well as a Delta-Omicron recombinant and a synthetic 'novel' lineage. Most tools performed well in identifying the true lineages present and estimating their relative abundances and were generally robust to variation in sequencing depth and read length. While many tools identified lineages present down to 1 % frequency, results were more reliable above a 5 % threshold. The presence of an unknown synthetic lineage, which represents an unclassified SARS-CoV-2 lineage, increases the error in relative abundance estimates of other lineages, but the magnitude of this effect was small for most tools. The tools also varied in how they labelled novel synthetic lineages and recombinants. While our simulated dataset represents just one of many possible use cases for these methods, we hope it helps users understand potential sources of error or bias in wastewater sequencing analysis and to appreciate the commonalities and differences across methods.

Monitoring of over-the-counter (OTC) and COVID-19 treatment drugs complement wastewater surveillance of SARS-CoV-2

BACKGROUND

The application of wastewater-based epidemiology to track the outbreak and prevalence of coronavirus disease (COVID-19) in communities has been tested and validated by several researchers across the globe. However, the RNA-based surveillance has its inherent limitations and uncertainties.

OBJECTIVE

This study aims to complement the ongoing wastewater surveillance efforts by analyzing other chemical biomarkers in wastewater to help assess community response (hospitalization and treatment) during the pandemic (2020-2021).

METHODS

Wastewater samples (n = 183) were collected from the largest wastewater treatment facility in Suffolk County, NY, USA and analyzed for COVID-19 treatment drugs (remdesivir, chloroquine, and hydroxychloroquine (HCQ)) and their human metabolites. We additionally monitored 26 pharmaceuticals including common over-the-counter (OTC) drugs. Lastly, we developed a Bayesian model that uses viral RNA, COVID-19 treatment drugs, and pharmaceuticals data to predict the confirmed COVID-19 cases within the catchment area.

RESULTS

The viral RNA levels in wastewater tracked the actual COVID-19 case numbers well as expected. COVID-19 treatment drugs were detected with varying frequency (9-100%) partly due to their instability in wastewater. We observed a significant correlation (R = 0.30, p < 0.01) between the SARS-CoV-2 genes and desethylhydroxychloroquine (DHCQ, metabolite of HCQ). Remdesivir levels peaked immediately after the Emergency Use Authorization approved by the FDA. Although, 13 out of 26 pharmaceuticals assessed were consistently detected (DF = 100%, n = 111), only acetaminophen was significantly correlated with viral loads, especially when the Omicron variant was dominant. The Bayesian models were capable of reproducing the temporal trend of the confirmed cases.

IMPACT

In this study, for the first time, we measured COVID-19 treatment and pharmaceutical drugs and their metabolites in wastewater to complement ongoing COVID-19 viral RNA surveillance efforts. Our results highlighted that, although the COVID-19 treatment drugs were not very stable in wastewater, their detection matched with usage trends in the community. Acetaminophen, an OTC drug, was significantly correlated with viral loads and confirmed cases, especially when the Omicron variant was dominant. A Bayesian model was developed which could predict COVID-19 cases more accurately when incorporating other drugs data along with viral RNA levels in wastewater.

A systematic review on the incidence of influenza viruses in wastewater matrices: Implications for public health

Influenza viruses pose a significant public health threat, necessitating comprehensive surveillance strategies to enhance early detection and preventive measures. This systematic review investigates the incidence of influenza viruses in wastewater matrices, aiming to elucidate the potential implications for public health. The study synthesizes existing literature, employing rigorous inclusion criteria to identify relevant studies conducted globally. The essence of the problem lies in the gaps of traditional surveillance methods, which often rely on clinical data and may underestimate the true prevalence of influenza within communities. Wastewater-based epidemiology offers a novel approach to supplementing these conventional methods, providing a broader and more representative assessment of viral circulation. This review systematically examines the methodologies employed in the selected studies, including virus concentration techniques and molecular detection methods, to establish a standardized framework for future research. Our findings reveal a consistent presence of influenza viruses in diverse wastewater matrices across different geographic locations and seasons. Recommendations for future research include the standardization of sampling protocols, improvement of virus concentration methods, and the integration of wastewater surveillance into existing public health frameworks. In conclusion, this systematic review contributes to the understanding of influenza dynamics in wastewater matrices, offering valuable insights for public health practitioners and policymakers. Implementation of wastewater surveillance alongside traditional methods can enhance the resilience of public health systems and better prepare communities for the challenges posed by influenza outbreaks.

Water-Based Epidemiological Investigation of Hepatitis E Virus in South Africa

Hepatitis E virus (HEV) is an emerging zoonotic pathogen that exhibits great host diversity. The primary means of transmission of the virus in low- and middle-income countries is contaminated water, often due to a lack of access to proper sanitation, which leads to faecal contamination of water sources. Environmental surveillance is an important tool that can be used to monitor virus circulation and as an early warning system for outbreaks. This study was conducted to determine the prevalence and genetic diversity of HEV in wastewater, surface water (rivers and standpipe/ablution water), and effluent from a piggery in South Africa. A total of 536 water samples were screened for HEV using real-time reverse transcription-polymerase chain reaction. Overall, 21.8% (117/536) of the wastewater, river, and ablution water samples tested positive for HEV, whereas 74.4% (29/39) of the samples from the piggery tested positive. Genotyping revealed sequences belonging to HEV genotypes 3 (98%, 53/54) and 4 (2%, 1/54), with subtypes 3c, 3f, and 4b being identified.

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

BACKGROUND

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

METHODS

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

RESULTS

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

CONCLUSION

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

Identifying outbreaks in sewer networks: An adaptive sampling scheme under network's uncertainty

Motivated by the implementation of a SARS-Cov-2 sewer surveillance system in Chile during the COVID-19 pandemic, we propose a set of mathematical and algorithmic tools that aim to identify the location of an outbreak under uncertainty in the network structure. Given an upper bound on the number of samples we can take on any given day, our framework allows us to detect an unknown infected node by adaptively sampling different network nodes on different days. Crucially, despite the uncertainty of the network, the method allows univocal detection of the infected node, albeit at an extra cost in time. This framework relies on a specific and well-chosen strategy that defines new nodes to test sequentially, with a heuristic that balances the granularity of the information obtained from the samples. We extensively tested our model in real and synthetic networks, showing that the uncertainty of the underlying graph only incurs a limited increase in the number of iterations, indicating that the methodology is applicable in practice.

Seasonal sensitivity of groundwater dissolved organic matter in recognition of chronic kidney disease of unknown etiology: Optical and molecular perspectives

Chronic kidney disease of unknown etiology (CKDu) has aroused a great concern due to its widespread prevalence in many developing countries. Dissolved organic matter (DOM) has been proved to be associated with CKDu in groundwater. However, the responses of their association to abiotic influencing factors like seasonal variation are not carefully disclosed. Herein, we revealed the seasonal variation of DOM in CKDu related groundwater (CKDu groundwater) and control group (non-CKDu groundwater) collected from Sri Lanka during the dry and wet seasons by excitation-emission matrix spectroscopy and Fourier transform ion cyclotron resonance mass spectrometry. In both CKDu and non-CKDu groundwaters, the input of exogenous DOM during wet season improved the degree of humification and molecular weight of DOM, while oxidative processes during the dry season increased the ratios of oxygen to carbon (O/C). Furthermore, compared with non-CKDu groundwater, more DOM with high O/C enriched in CKDu groundwater during the dry season, indicating stronger oxidative processes in CKDu groundwater. It may result in the enrichment of carboxyl group and induce the enhanced leaching of CKDu-related Si and F-. The receiver operating characteristic (ROC) analysis showed that the CKDu-recognition ability of most optical and molecular indicators was susceptible to seasonal factors and their recognition abilities were stronger in the wet season. The linkage between DOM and CKDu was affected by seasonal factors through the occurrence, mobility, degradation, and toxicity of typical organic molecules (e.g., C17H18O10S). The study provides a new insight into screening pathogenic factors of other endemic diseases related to organic molecules.

Polyethylenimine mediated recovery of SARS-CoV-2 and total viral RNA: Impact of aqueous conditions on behaviour and recovery

Wastewater-based epidemiology (WBE) is an emerging, practical surveillance tool for monitoring community levels of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2, SC2). However, a paucity of data exists regarding SARS-CoV-2 and viral biomarker behaviour in aqueous and wastewater environments. Therefore, there is a pressing need to develop efficient and robust methods that both improve method sensitivity and reduce time and cost. We present a novel method for SARS-CoV-2, Human Coronavirus 229E (229E), and Pepper Mild Mottle Virus (PMMoV) recovery utilizing surface charge-based attraction via the branched cationic polymer, polyethylenimine (PEI). Initially, dose-optimization experiments demonstrated that low concentrations of PEI (0.001% w/v) proved most effective at flocculating suspended viruses and viral material, including additional unbound SC2 viral fragments and/or RNA from raw wastewater. A design-of-experiments (DOE) approach was used to optimize virus and/or viral material aggregation behaviour and recovery across varying aqueous conditions, revealing pH as a major influence on recoverability in this system, combinatorially due to both a reduction in viral material surface charge and increased protonation of PEI-bound amine groups. Overall, this method has shown great promise in significantly improving quantitative viral recovery, providing a straightforward and effective augmentation to standard centrifugation techniques.

Human adenovirus outbreak at a university campus monitored by wastewater and clinical surveillance

Areas of dense population congregation are prone to experience respiratory virus outbreaks. We monitored wastewater and clinic patients for the presence of respiratory viruses on a large, public university campus. Campus sewer systems were monitored in 16 locations for the presence of viruses using next generation sequencing over 22 weeks in 2023. During this period, we detected a surge in human adenovirus (HAdV) levels in wastewater. Hence, we initiated clinical surveillance at an on-campus clinic from patients presenting with acute respiratory infection. From whole genome sequencing of 123 throat and/or nasal swabs collected, we identified an outbreak of HAdV, specifically of HAdV-E4 and HAdV-B7 genotypes overlapping in time. The temporal dynamics and proportions of HAdV genotypes found in wastewater were corroborated in clinical infections. We tracked specific single nucleotide polymorphisms (SNPs) found in clinical virus sequences and showed that they arose in wastewater signals concordant with the time of clinical presentation, linking community transmission of HAdV to the outbreak. This study demonstrates how wastewater-based epidemiology can be integrated with surveillance at ambulatory healthcare settings to monitor areas prone to respiratory virus outbreaks and provide public health guidance.

Adapted methods for monitoring influenza virus and respiratory syncytial virus in sludge and wastewater

Wastewater-based surveillance constitutes a valuable methodology for the continuous monitoring of viral circulation, with the capacity to function as an early warning system. It holds particular significance in scenarios where respiratory viruses exhibit overlapping clinical presentations, as occurs with SARS-CoV-2, influenza virus (IV), and respiratory syncytial virus (RSV), and allows seasonal virus outbreaks to be distinguished from COVID-19 peaks. Furthermore, sewage sludge, given it harbors concentrated human waste from a large population, serves as a substantial reservoir for pathogen detection. To effectively integrate wastewater-based epidemiology into infectious disease surveillance, the detection methods employed in wastewater samples must be adapted to the distinct characteristics of sludge matrices. In this study, we adapted and applied protocols for the detection of IV and RSV in sewage sludge, comparing their performance with the results obtained in wastewater. To assess the efficiency of these protocols, sludge and wastewater samples were spiked with IV and RSV RNA, either free or incorporated in lentiviral particles. Samples were concentrated using the aluminum hydroxide adsorption-precipitation method before viral RNA extraction. Absolute virus quantification was carried out by RT-qPCR, including an internal control to monitor potential inhibitory factors. Recovery efficiencies for both free IV and RSV RNA were 60 % in sludge, and 75 % and 71 % respectively in wastewater, whereas the values for IV and RSV RNA in lentiviral particles were 16 % and 10 % in sludge and 21 % and 17 % in wastewater respectively. Additionally, the protocol enabled the quantification of naturally occurring IV and RSV in wastewater and sludge samples collected from two wastewater treatment plants during the winter months, thus affirming the efficacy of the employed methodologies.

Multidrug-Resistant Staphylococcus sp. and Enterococcus sp. in Municipal and Hospital Wastewater: A Longitudinal Study

The objective of the study was to detect multidrug-resistant Staphylococcus sp. and Enterococcus sp. isolates in municipal and hospital wastewater and to determine their elimination or persistence after wastewater treatment. Between August 2021 and September 2022, raw and treated wastewater samples were collected at two hospital and two community wastewater treatment plants (WWTPs). In each season of the year, two treated and two raw wastewater samples were collected in duplicate at each of the WWTPs studied. Screening and presumptive identification of staphylococci and enterococci was performed using chromoagars, and identification was performed with the Matrix Assisted Laser Desorption Ionization Time of Flight mass spectrometry (MALDI-TOF MS®). Antimicrobial susceptibility was performed using VITEK 2® automated system. There were 56 wastewater samples obtained during the study period. A total of 182 Staphylococcus sp. and 248 Enterococcus sp. were identified. The highest frequency of Staphylococcus sp. isolation was in spring and summer (n = 129, 70.8%), and for Enterococcus sp. it was in autumn and winter (n = 143, 57.7%). Sixteen isolates of Staphylococcus sp. and sixty-three of Enterococcus sp. persisted during WWTP treatments. Thirteen species of staphylococci and seven species of enterococci were identified. Thirty-one isolates of Staphylococcus sp. and ninety-four of Enterococcus sp. were multidrug-resistant. Resistance to vancomycin (1.1%), linezolid (2.7%), and daptomycin (8.2%/10.9%%), and a lower susceptibility to tigecycline (2.7%), was observed. This study evidences the presence of Staphylococcus sp. and Enterococcus sp. resistant to antibiotics of last choice of clinical treatment, in community and hospital wastewater and their ability to survive WWTP treatment systems.

Amplidiff: an optimized amplicon sequencing approach to estimating lineage abundances in viral metagenomes

BACKGROUND

Metagenomic profiling algorithms commonly rely on genomic differences between lineages, strains, or species to infer the relative abundances of sequences present in a sample. This observation plays an important role in the analysis of diverse microbial communities, where targeted sequencing of 16S and 18S rRNA, both well-known hypervariable genomic regions, have led to insights into microbial diversity and the discovery of novel organisms. However, the variable nature of discriminatory regions can also act as a double-edged sword, as the sought-after variability can make it difficult to design primers for their amplification through PCR. Moreover, the most variable regions are not necessarily the most informative regions for the purpose of differentiation; one should focus on regions that maximize the number of lineages that can be distinguished.

RESULTS

Here we present AmpliDiff, a computational tool that simultaneously finds highly discriminatory genomic regions in viral genomes of a single species, as well as primers allowing for the amplification of these regions. We show that regions and primers found by AmpliDiff can be used to accurately estimate relative abundances of SARS-CoV-2 lineages, for example in wastewater sequencing data. We obtain errors that are comparable with using whole genome information to estimate relative abundances. Furthermore, our results show that AmpliDiff is robust against incomplete input data and that primers designed by AmpliDiff also bind to genomes sampled months after the primers were selected.

CONCLUSIONS

With AmpliDiff we provide an effective, cost-efficient alternative to whole genome sequencing for estimating lineage abundances in viral metagenomes.

Estimating actual SARS-CoV-2 infections from secondary data

Eminent in pandemic management is accurate information on infection dynamics to plan for timely installation of control measures and vaccination campaigns. Despite huge efforts in diagnostic testing of individuals, the underestimation of the actual number of SARS-CoV-2 infections remains significant due to the large number of undocumented cases. In this paper we demonstrate and compare three methods to estimate the dynamics of true infections based on secondary data i.e., (a) test positivity, (b) infection fatality and (c) wastewater monitoring. The concept is tested with Austrian data on a national basis for the period of April 2020 to December 2022. Further, we use the results of prevalence studies from the same period to generate (upper and lower bounds of) credible intervals for true infections for four data points. Model parameters are subsequently estimated by applying Approximate Bayesian Computation-rejection sampling and Genetic Algorithms. The method is then validated for the case study Vienna. We find that all three methods yield fairly similar results for estimating the true number of infections, which supports the idea that all three datasets contain similar baseline information. None of them is considered superior, as their advantages and shortcomings depend on the specific case study at hand.

Wastewater-based epidemiology for COVID-19 using dynamic artificial neural networks

Global efforts in vaccination have led to a decrease in COVID-19 mortality but a high circulation of SARS-CoV-2 is still observed in several countries, resulting in some cases of severe lockdowns. In this sense, wastewater-based epidemiology remains a powerful tool for supporting regional health administrations in assessing risk levels and acting accordingly. In this work, a dynamic artificial neural network (DANN) has been developed for predicting the number of COVID-19 hospitalized patients in hospitals in Valladolid (Spain). This model takes as inputs a wastewater epidemiology indicator for COVID-19 (concentration of RNA from SARS-CoV-2 N1 gene reported from Valladolid Wastewater Treatment Plant), vaccination coverage, and past data of hospitalizations. The model considered both the instantaneous values of these variables and their historical evolution. Two study periods were selected (from May 2021 until September 2022 and from September 2022 to July 2023). During the first period, accurate predictions of hospitalizations (with an overall range between 6 and 171) were favored by the correlation of this indicator with N1 concentrations in wastewater (r = 0.43, p < 0.05), showing accurate forecasting for 1 day ahead and 5 days ahead. The second period's retraining strategy maintained the overall accuracy of the model despite lower hospitalizations. Furthermore, risk levels were assigned to each 1 day ahead prediction during the first and second periods, showing agreement with the level measured and reported by regional health authorities in 95 % and 93 % of cases, respectively. These results evidenced the potential of this novel DANN model for predicting COVID-19 hospitalizations based on SARS-CoV-2 wastewater concentrations at a regional scale. The model architecture herein developed can support regional health authorities in COVID-19 risk management based on wastewater-based epidemiology.

Performance evaluation for the inactivation of multidrug-resistant bacteria in wastewater effluent by different disinfection technologies

The escalating presence of antibiotic-resistant bacteria (ARB) in aquatic ecosystems underscores the critical role of wastewater treatment plants (WWTPs) in mitigating antibiotic resistance. Disinfection is the final, pivotal step in WWTPs, and it is essential to control the dissemination of ARB before water discharge. This study utilized both phenotypic analysis and transcriptome (RNA-seq) approach to investigate the efficiency and mechanisms of disinfection using chlorination, ultraviolet (UV), and peracetic acid (PAA) on multidrug-resistant bacteria (MRB). Our results demonstrated that the use of 100 mg min L-1 of chlorine, 8.19 mJ cm-2 of UV irradiation or 50 min mg L-1 of PAA significantly reduced the abundance of MRB. Intriguingly, RNA-seq clarified distinct mechanisms of chlorination and UV disinfection. UV radiation triggered the SOS response to cope with DNA damage, induced the expression of multi-drug resistance genes by increasing the expression of efflux pump transporters. UV radiation also promoted the absorption of iron through chelation and transportation to participate in various cell life processes. Chlorination, on the other hand, significantly up-regulated osmotic response elements, including the synthesis of glycine betaine, iron-sulfur clusters, and related transporters. Both chlorination and UV significantly down-regulated key metabolic pathways (P < 0.05), inhibiting the process of amino acid synthesis and energy metabolism. Imbalance in energy homeostasis was the most important factor leading to cytotoxicity. These results provide useful insights into optimizing the wastewater disinfection process in order to prevent the dissemination of ARB in aquatic environment.

Wastewater-based surveillance as a tool for public health action: SARS-CoV-2 and beyond

Wastewater-based surveillance (WBS) has undergone dramatic advancement in the context of the coronavirus disease 2019 (COVID-19) pandemic. The power and potential of this platform technology were rapidly realized when it became evident that not only did WBS-measured SARS-CoV-2 RNA correlate strongly with COVID-19 clinical disease within monitored populations but also, in fact, it functioned as a leading indicator. Teams from across the globe rapidly innovated novel approaches by which wastewater could be collected from diverse sewersheds ranging from wastewater treatment plants (enabling community-level surveillance) to more granular locations including individual neighborhoods and high-risk buildings such as long-term care facilities (LTCF). Efficient processes enabled SARS-CoV-2 RNA extraction and concentration from the highly dilute wastewater matrix. Molecular and genomic tools to identify, quantify, and characterize SARS-CoV-2 and its various variants were adapted from clinical programs and applied to these mixed environmental systems. Novel data-sharing tools allowed this information to be mobilized and made immediately available to public health and government decision-makers and even the public, enabling evidence-informed decision-making based on local disease dynamics. WBS has since been recognized as a tool of transformative potential, providing near-real-time cost-effective, objective, comprehensive, and inclusive data on the changing prevalence of measured analytes across space and time in populations. However, as a consequence of rapid innovation from hundreds of teams simultaneously, tremendous heterogeneity currently exists in the SARS-CoV-2 WBS literature. This manuscript provides a state-of-the-art review of WBS as established with SARS-CoV-2 and details the current work underway expanding its scope to other infectious disease targets.

Integrating wastewater and randomised prevalence survey data for national COVID surveillance

During the COVID-19 pandemic, studies in a number of countries have shown how wastewater can be used as an efficient surveillance tool to detect outbreaks at much lower cost than traditional prevalence surveys. In this study, we consider the utilisation of wastewater data in the post-pandemic setting, in which collection of health data via national randomised prevalence surveys will likely be run at a reduced scale; hence an affordable ongoing surveillance system will need to combine sparse prevalence data with non-traditional disease metrics such as wastewater measurements in order to estimate disease progression in a cost-effective manner. Here, we use data collected during the pandemic to model the dynamic relationship between spatially granular wastewater viral load and disease prevalence. We then use this relationship to nowcast local disease prevalence under the scenario that (i) spatially granular wastewater data continue to be collected; (ii) direct measurements of prevalence are only available at a coarser spatial resolution, for example at national or regional scale. The results from our cross-validation study demonstrate the added value of wastewater data in improving nowcast accuracy and reducing nowcast uncertainty. Our results also highlight the importance of incorporating prevalence data at a coarser spatial scale when nowcasting prevalence at fine spatial resolution, calling for the need to maintain some form of reduced-scale national prevalence surveys in non-epidemic periods. The model framework is disease-agnostic and could therefore be adapted to different diseases and incorporated into a multiplex surveillance system for early detection of emerging local outbreaks.

Emerging trends in wastewater treatment: Addressing microorganic pollutants and environmental impacts

This review focuses on the challenges and advances associated with the treatment and management of microorganic pollutants, encompassing pesticides, industrial chemicals, and persistent organic pollutants (POPs) in the environment. The translocation of these contaminants across multiple media, particularly through atmospheric transport, emphasizes their pervasive nature and the subsequent ecological risks. The urgency to develop cost-effective remediation strategies for emerging organic contaminants is paramount. As such, wastewater-based epidemiology and the increasing concern over estrogenicity are explored. By incorporating conventional and innovative wastewater treatment techniques, this article highlights the integration of environmental management strategies, analytical methodologies, and the importance of renewable energy in waste treatment. The primary objective is to provide a comprehensive perspective on the current scenario, imminent threats, and future directions in mitigating the effects of these pollutants on the environment. Furthermore, the review underscores the need for international collaboration in developing standardized guidelines and policies for monitoring and controlling these microorganic pollutants. It advocates for increased investment in research and development of advanced materials and technologies that can efficiently remove or neutralize these contaminants, thereby safeguarding environmental health and promoting sustainable practice.

Simple SARS-CoV-2 concentration methods for wastewater surveillance in low resource settings

Wastewater-based epidemiology (WBE) measures pathogens in wastewater to monitor infectious disease prevalence in communities. Due to the high dilution of pathogens in sewage, a concentration method is often required to achieve reliable biomarker signals. However, most of the current concentration methods rely on expensive equipment and labor-intensive processes, which limits the application of WBE in low-resource settings. Here, we compared the performance of four inexpensive and simple concentration methods to detect SARS-CoV-2 in wastewater samples: Solid Fraction, Porcine Gastric Mucin-conjugated Magnetic Beads, Calcium Flocculation-Citrate Dissolution (CFCD), and Nanotrap® Magnetic Beads (NMBs). The NMBs and CFCD methods yielded the highest concentration performance for SARS-CoV-2 (∼16-fold concentration and ∼ 41 % recovery) and require <45 min processing time. CFCD has a relatively low consumable cost (<$2 per four sample replicates). All methods can be performed with basic laboratory equipment and minimal electricity usage which enables further application of WBE in remote areas and low resource settings.

Emergence of Carbapenemase Genes in Gram-Negative Bacteria Isolated from the Wastewater Treatment Plant in A Coruña, Spain

Wastewater treatment plants (WWTPs) are recognized as important niches of antibiotic-resistant bacteria that can be easily spread to the environment. In this study, we collected wastewater samples from the WWTP of A Coruña (NW Spain) from April 2020 to February 2022 to evaluate the presence of Gram-negative bacteria harboring carbapenemase genes. Bacteria isolated from wastewater were classified and their antimicrobial profiles were determined. In total, 252 Gram-negative bacteria carrying various carbapenemase genes were described. Whole-genome sequencing was conducted on 55 selected carbapenemase producing isolates using Oxford Nanopore technology. This study revealed the presence of a significant population of bacteria carrying carbapenemase genes in WWTP, which constitutes a public health problem due to their risk of dissemination to the environment. This emphasizes the usefulness of WWTP monitoring for combating antibiotic resistance. Data revealed the presence of different types of sequences harboring carbapenemase genes, such as blaKPC-2, blaGES-5, blaGES-6, blaIMP-11, blaIMP-28, blaOXA-24, blaOXA-48, blaOXA-58, blaOXA-217, and blaVIM-2. Importantly, the presence of the blaKPC-2 gene in wastewater, several months before any clinical case was detected in University Hospital of A Coruña, suggests that wastewater-based epidemiology can be used as an early warning system for the surveillance of antibiotic-resistant bacteria.

Making waves: The benefits and challenges of responsibly implementing wastewater-based surveillance for rural communities

The sampling and analysis of sewage for pathogens and other biomarkers offers a powerful tool for monitoring and understanding community health trends and potentially predicting disease outbreaks. Since the early months of the COVID-19 pandemic, the use of wastewater-based testing for public health surveillance has increased markedly. However, these efforts have focused on urban and peri‑urban areas. In most rural regions of the world, healthcare service access is more limited than in urban areas, and rural public health agencies typically have less disease outcome surveillance data than their urban counterparts. The potential public health benefits of wastewater-based surveillance for rural communities are therefore substantial - though so too are the methodological and ethical challenges. For many rural communities, population dynamics and insufficient, aging, and inadequately maintained wastewater collection and treatment infrastructure present obstacles to the reliable and responsible implementation of wastewater-based surveillance. Practitioner observations and research findings indicate that for many rural systems, typical implementation approaches for wastewater-based surveillance will not yield sufficiently reliable or actionable results. We discuss key challenges and potential strategies to address them. However, to support and expand the implementation of responsible, reliable, and ethical wastewater-based surveillance for rural communities, best practice guidelines and standards are needed.

Urine biomarkers for Alzheimer's disease: A new opportunity for wastewater-based epidemiology?

While Alzheimer's disease (AD) diagnosis, management, and care have become priorities for healthcare providers and researcher's worldwide due to rapid population aging, epidemiologic surveillance efforts are currently limited by costly, invasive diagnostic procedures, particularly in low to middle income countries (LMIC). In recent years, wastewater-based epidemiology (WBE) has emerged as a promising tool for public health assessment through detection and quantification of specific biomarkers in wastewater, but applications for non-infectious diseases such as AD remain limited. This early review seeks to summarize AD-related biomarkers and urine and other peripheral biofluids and discuss their potential integration to WBE platforms to guide the first prospective efforts in the field. Promising results have been reported in clinical settings, indicating the potential of amyloid β, tau, neural thread protein, long non-coding RNAs, oxidative stress markers and other dysregulated metabolites for AD diagnosis, but questions regarding their concentration and stability in wastewater and the correlation between clinical levels and sewage circulation must be addressed in future studies before comprehensive WBE systems can be developed.