The potential of wastewater-based epidemiology as surveillance and early warning of infectious disease outbreaks

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

INTRODUCTION

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

METHOD

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

RESULTS

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

CONCLUSION

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

Evaluating Nanotrap Microbiome Particles as A Wastewater Viral Concentration Method

Wastewater-based surveillance has emerged as a powerful approach to monitoring infectious diseases within a community. Typically, wastewater samples are concentrated before viral analyses to improve sensitivity. Current concentration methods vary in time requirements, costs, and efficiency. Here, we evaluated the concentration efficiency and bias of a novel viral concentration approach, Nanotrap Microbiome Particles (NMP), in wastewater. NMP concentration efficiency was target-specific, with significantly lower concentrations of the bacterial indicator HF183 and viral indicator Carjivirus (formerly crAssphage) relative to direct extraction (1.2 × 105 vs. 3.4 × 105 GC/mL and 2.0 × 105 vs. 1.2 × 105 GC/mL, respectively), but significantly higher concentrations of the viral fecal indicator Pepper Mild Mottle Virus (PMMoV) relative to direct extraction (1.4 × 105 vs. 8.4 × 103 GC/mL). Targeted metagenomic sequencing showed that NMP resulted in significantly more unique species reads per sample than direct extractions (p < 0.001) by detecting species that went undetected by direct extractions. Key viral families identified with high abundances were Adenoviridae, Caliciviridae, Herpesviridae, Papillomaviridae, and Polyomaviridae. NMP showed differential ability for concentrating clinically relevant viral families, suggesting that the technology should be evaluated and optimized for specific viral targets before implementation.

Isothermal nucleic acid amplification for monitoring hand-foot-and-mouth disease: current status and future implications

With the global prevalence of the hand-foot-and-mouth disease (HFMD) epidemic, the development of reliable point-of-care testing (POCT) is crucial for the timely identification and prevention of outbreaks. Isothermal nucleic acid amplification techniques (INAATs) have attracted much attention because of their high efficiency for rapid diagnosis. In this work, we systematically summarize the current status of INAATs for HFMD and discuss advantages and drawbacks of various INAATs for HFMD. The INAATs for HFMD detection mainly include loop-mediated isothermal amplification (LAMP), simultaneous amplification and testing (SAT), and recombinase polymerase amplification (RPA). Among them, LAMP has excelled in several diagnostic metrics and has made significant progress in the field of POCT. SAT has been effective in overcoming the problem of RNA degradation. RPA is suited for on-site testing due to its rapid amplification rate and low reaction temperature. In addition, this study explores the potential of INAATs in lateral flow strips (LFS) test and microfluidic devices for HFMD. LFS is typically used for qualitative analysis and supports multiple detection. Microfluidics can integrate necessary processes of sample pre-processing, amplification, and signal output, enabling high-throughput qualitative or quantitative detection and demonstrating the potential of monitoring HFMD. We hope the current work will provide insights into INAATs for monitoring HFMD and serve as a reference for the implementation of on-site EV detection for public health.

Wastewater-based surveillance of respiratory viruses in Northern Tuscany (Italy): Challenges and added value for public health purposes

During the COVID-19 pandemic, wastewater-based surveillance (WBS) showed great potential as an early warning system and could complement human clinical surveillance. This study aimed to highlight the added value of WBS for respiratory infections alongside different clinical surveillance systems. Sewage collected at the entry of four Wastewater Treatment Plants in Northern Tuscany (Italy) were analyzed for SARS-CoV-2, Human Adenovirus (HAdV), Respiratory Syncytial Virus (RSV) and Influenza Virus (IV), over two years. Clinical data for COVID-19 were available for the study area, while data for other viruses came from national virological surveillance. For SARS-CoV-2, the correlation was highly significant between clinical and hospitalization data (ρ = 0.8460), but not significant between wastewater and clinical or hospitalization data (ρ = 0.1682 and ρ = 0.0569, respectively). SARS-CoV-2 RNA was found in wastewater even in period when clinical cases were not reported, indicating a continuous community circulation. HAdVs were detected in 74.3 % of samples, but most of the sequences identified belonged to enteric species (HAdV-F41), indicating the need of distinguishing the species causing respiratory diseases for the surveillance. RSV were found only in winter 2022-2023, while IV had not been detected in wastewater, probably due to poor test sensitivity. In conclusion, although there may be various challenges in testing different targets, WBS can provide pathogen-specific situational assessment which complements existing surveillance systems.

Machine Learning-Assisted Surface-Enhanced Raman Spectroscopy Detection for Environmental Applications: A Review

Surface-enhanced Raman spectroscopy (SERS) has gained significant attention for its ability to detect environmental contaminants with high sensitivity and specificity. The cost-effectiveness and potential portability of the technique further enhance its appeal for widespread application. However, challenges such as the management of voluminous quantities of high-dimensional data, its capacity to detect low-concentration targets in the presence of environmental interferents, and the navigation of the complex relationships arising from overlapping spectral peaks have emerged. In response, there is a growing trend toward the use of machine learning (ML) approaches that encompass multivariate tools for effective SERS data analysis. This comprehensive review delves into the detailed steps needed to be considered when applying ML techniques for SERS analysis. Additionally, we explored a range of environmental applications where different ML tools were integrated with SERS for the detection of pathogens and (in)organic pollutants in environmental samples. We sought to comprehend the intricate considerations and benefits associated with ML in these contexts. Additionally, the review explores the future potential of synergizing SERS with ML for real-world applications.

Identification of protein biomarkers in wastewater linked to the incidence of COVID-19

Wastewater-based epidemiological (WBE) surveillance is a viable disease surveillance technique capable of monitoring the spread of infectious disease agents in sewershed communities. In addition to detecting viral genomes in wastewater, WBE surveillance can identify other endogenous biomarkers that are significantly elevated and excreted in the saliva, urine, and/or stool of infected individuals. Human protein biomarkers allow the realization of real-time WBE surveillance using highly sensitive biosensors. In this study, we analyzed endogenous protein biomarkers present in wastewater influent through liquid chromatography-tandem mass spectrophotometry and scaffold data-independent acquisition to identify candidate target protein biomarkers for WBE surveillance of SARS-CoV-2. We found that out of the 1382 proteins observed in the wastewater samples, 44 were human proteins associated with infectious diseases. These included immune response substances such as immunoglobulins, cytokine-chemokines, and complements, as well as proteins belonging to antimicrobial and antiviral groups. A significant correlation was observed between the intensity of human infectious disease-related protein biomarkers in wastewater and COVID-19 case numbers. Real-time WBE surveillance using biosensors targeting immune response proteins, such as antibodies or immunoglobulins, in wastewater holds promise for expediting the implementation of relevant policies for the effective prevention of infectious diseases in the near future.

Revealing patterns of SARS-CoV-2 variant emergence and evolution using RBD amplicon sequencing of wastewater

OBJECTIVES

Rapid evolution of SARS-CoV-2 has resulted in the emergence of numerous variants, posing significant challenges to public health surveillance. Clinical genome sequencing, while valuable, has limitations in capturing the full epidemiological dynamics of circulating variants in the general population. This study aimed to monitor the SARS-CoV-2 variant community dynamics and evolution using receptor-binding domain (RBD) amplicon sequencing of wastewater samples.

METHODS

We sequenced wastewater from El Paso, Texas, over 17 months, compared the sequencing data with clinical genome data, and performed biodiversity analysis to reveal SARS-CoV-2 variant dynamics and evolution.

RESULTS

We identified 91 variants and observed waves of dominant variants transitioning from BA.2 to BA.2.12.1, BA.4&5, BQ.1, and XBB.1.5. Comparison with clinical genome sequencing data revealed earlier detection of variants and identification of unreported outbreaks. Our results also showed strong consistency with clinical data for dominant variants at the local, state, and national levels. Alpha diversity analyses revealed significant seasonal variations, with the highest diversity observed in winter. By segmenting the outbreak into lag, growth, stationary, and decline phases, we found higher variant diversity during the lag phase, likely due to lower inter-variant competition preceding outbreak growth.

CONCLUSIONS

Our findings underscore the importance of low transmission periods in facilitating rapid mutation and variant evolution. Our approach, integrating RBD amplicon sequencing with wastewater surveillance, demonstrates effectiveness in tracking viral evolution and understanding variant emergence, thus enhancing public health preparedness.

Surface-functionalized PAN fiber membranes for the sensitive detection of airborne specific markers

PAN fibers are characterized by having a large surface-to-volume ratio and small pores, which are beneficial for applications in filtration and specific molecular detection systems. Naturally, larger items are filtered, and a lower ratio between specific and nonspecific binding is expected since small pores do not allow larger elements to penetrate through membranes; thus, nonspecific binding is enhanced. We prepared and tested fiber membranes (diameter cca 700 nm) functionalized with a specific antibody to prove that even microscopic systems such as bacteria could be specifically identified. In addition, we established a methodology that enabled the effective binding of bacteria in not only an aqueous environment but also air. Our data clearly prove that even large systems such as bacteria could be specifically identified by fiber membranes surface-functionalized with a specific antibody. This research opens the door to the construction of biosensors for the fast, inexpensive, and sensitive identification of airborne bacterial contaminants and other airborne pollutants.

Tracking epidemic viruses in wastewaters

Classical epidemiology relies on incidence, mortality rates, and clinical data from individual testing, which can be challenging for many countries. Therefore, innovative, flexible, cost-effective, and scalable surveillance techniques are needed. Wastewater-based epidemiology (WBE) has emerged as a highly powerful tool in this regard. WBE analyses substances excreted in human fluids and faeces that enter the sewer system. This approach provides insights into community health status and lifestyle habits. WBE serves as an early warning system for viral surveillance, detecting the emergence of new pathogens, changes in incidence rates, identifying future trends, studying outbreaks, and informing the performance of action plans. While WBE has long been used to study different viruses such as poliovirus and norovirus, its implementation has surged due to the pandemic caused by the Severe Acute Respiratory Syndrome Coronavirus 2. This has led to the establishment of wastewater surveillance programmes at international, national, and community levels, many of which remain operational. Furthermore, WBE is increasingly applied to study other pathogens, including antibiotic resistance bacteria, parasites, fungi, and emerging viruses, with new methodologies being developed. Consequently, the primary focus now is on creating international frameworks to enhance states' preparedness against future health risks. However, there remains considerable work to be done, particularly in integrating the principles of One Health into epidemiological surveillance to acknowledge the interconnectedness of humans, animals, and the environment in pathogen transmission. Thus, a broader approach to analysing the three pillars of One Health must be developed, transitioning from WBE to wastewater and environmental surveillance, and establishing this approach as a routine practice in public health.

The first detection of SARS-CoV-2 RNA in the wastewater of Bucharest, Romania

Wastewater-based epidemiology (WBE) has been previously used as a tool for pathogen identification within communities. After the SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2) outbreak, in 2020, Daughton proposed the implementation of a wastewater surveillance strategy that could determine the incidence of COVID-19 (coronavirus disease 2019) nationally. Individuals in various stages of SARS-CoV-2 infection, including presymptomatic, asymptomatic and symptomatic patients, can be identified as carriers of the virus in their urine, saliva, stool and other bodily secretions. Studies using this method were conducted to monitor the prevalence of the virus in high-density populations, such as cities but also in smaller communities, such as schools and college campuses. The aim of this pilot study was to assess the feasibility and effectiveness of wastewater surveillance in Bucharest, Romania, and wastewater samples were collected weekly from seven locations between July and September 2023. RNA (ribonucleic acid) extraction, followed by dPCR (digital polymerase chain reaction) analysis, was performed to detect viral genetic material. Additionally, NGS (next generation sequencing) technology was used to identify the circulating variants within the wastewater of Bucharest, Romania. Preliminary results indicate the successful detection of SARS-CoV-2 RNA in wastewater, providing valuable insights into the circulation of the virus within the community.

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.

Comparison of Different Reverse Transcriptase-Polymerase Chain Reaction-Based Methods for Wastewater Surveillance of SARS-CoV-2: Exploratory Study

BACKGROUND

Many countries have applied the wastewater surveillance of the COVID-19 pandemic to their national public health monitoring measures. The most used methods for detecting SARS-CoV-2 in wastewater are quantitative reverse transcriptase-polymerase chain reaction (RT-qPCR) and reverse transcriptase-droplet digital polymerase chain reaction (RT-ddPCR). Previous comparison studies have produced conflicting results, thus more research on the subject is required.

OBJECTIVE

This study aims to compare RT-qPCR and RT-ddPCR for detecting SARS-CoV-2 in wastewater. It also aimed to investigate the effect of changes in the analytical pipeline, including the RNA extraction kit, RT-PCR kit, and target gene assay, on the results. Another aim was to find a detection method for low-resource settings.

METHODS

We compared 2 RT-qPCR kits, TaqMan RT-qPCR and QuantiTect RT-qPCR, and RT-ddPCR based on sensitivity, positivity rates, variability, and correlation of SARS-CoV-2 gene copy numbers in wastewater to the incidence of COVID-19. Furthermore, we compared 2 RNA extraction methods, column- and magnetic-bead-based. In addition, we assessed 2 target gene assays for RT-qPCR, N1 and N2, and 2 target gene assays for ddPCR N1 and E. Reverse transcription strand invasion-based amplification (RT-SIBA) was used to detect SARS-CoV-2 from wastewater qualitatively.

RESULTS

Our results indicated that the most sensitive method to detect SARS-CoV-2 in wastewater was RT-ddPCR. It had the highest positivity rate (26/30), and its limit of detection was the lowest (0.06 gene copies/µL). However, we obtained the best correlation between COVID-19 incidence and SARS-CoV-2 gene copy number in wastewater using TaqMan RT-qPCR (correlation coefficient [CC]=0.697, P<.001). We found a significant difference in sensitivity between the TaqMan RT-qPCR kit and the QuantiTect RT-qPCR kit, the first having a significantly lower limit of detection and a higher positivity rate than the latter. Furthermore, the N1 target gene assay was the most sensitive for both RT-qPCR kits, while no significant difference was found between the gene targets using RT-ddPCR. In addition, the use of different RNA extraction kits affected the result when the TaqMan RT-qPCR kit was used. RT-SIBA was able to detect SARS-CoV-2 RNA in wastewater.

CONCLUSIONS

As our study, as well as most of the previous studies, has shown RT-ddPCR to be more sensitive than RT-qPCR, its use in the wastewater surveillance of SARS-CoV-2 should be considered, especially if the amount of SARS-CoV-2 circulating in the population was low. All the analysis steps must be optimized for wastewater surveillance as our study showed that all the analysis steps including the compatibility of the RNA extraction, the RT-PCR kit, and the target gene assay influence the results. In addition, our study showed that RT-SIBA could be used to detect SARS-CoV-2 in wastewater if a qualitative result is sufficient.

A novel method to select time-varying multivariate time series models for the surveillance of infectious diseases

BACKGROUND

Describing the transmission dynamics of infectious diseases across different regions is crucial for effective disease surveillance. The multivariate time series (MTS) model has been widely adopted for constructing cross-regional infectious disease transmission networks due to its strengths in interpretability and predictive performance. Nevertheless, the assumption of constant parameters frequently disregards the dynamic shifts in disease transmission rates, thereby compromising the accuracy of early warnings. This study investigated the applicability of time-varying MTS models in multi-regional infectious disease monitoring and explored strategies for model selection.

METHODS

This study focused on two prominent time-varying MTS models: the time-varying parameter-stochastic volatility-vector autoregression (TVP-SV-VAR) model and the time-varying VAR model using the generalized additive framework (tvvarGAM), and intended to explore and verify their applicable conditions for the surveillance of infectious diseases. For the first time, this study proposed the time delay coefficient and spatial sparsity indicators for model selection. These indicators quantify the temporal lags and spatial distribution of infectious disease data, respectively. Simulation study adopted from real-world infectious disease surveillance was carried out to compare model performances under various scenarios of spatio-temporal variation as well as random volatility. Meanwhile, we illustrated how the modelling process could help the surveillance of infectious diseases with an application to the influenza-like case in Sichuan Province, China.

RESULTS

When the spatio-temporal variation was small (time delay coefficient: 0.1-0.2, spatial sparsity:0.1-0.3), the TVP-SV-VAR model was superior with smaller fitting residuals and standard errors of parameter estimation than those of the tvvarGAM model. In contrast, the tvvarGAM model was preferable when the spatio-temporal variation increased (time delay coefficient: 0.2-0.3, spatial sparsity: 0.6-0.9).

CONCLUSION

This study emphasized the importance of considering spatio-temporal variations when selecting appropriate models for infectious disease surveillance. By incorporating our novel indicators-the time delay coefficient and spatial sparsity-into the model selection process, the study could enhance the accuracy and effectiveness of infectious disease monitoring efforts. This approach was not only valuable in the context of this study, but also has broader implications for improving time-varying MTS analyses in various applications.

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.

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.

RBD amplicon sequencing of wastewater reveals patterns of variant emergence and evolution

Rapid evolution of SARS-CoV-2 has resulted in the emergence of numerous variants, posing significant challenges to public health surveillance. Clinical genome sequencing, while valuable, has limitations in capturing the full epidemiological dynamics of circulating variants in the general population. This study utilized receptor-binding domain (RBD) amplicon sequencing of wastewater samples to monitor the SARS-CoV-2 community dynamics and evolution in El Paso, TX. Over 17 months, we identified 91 variants and observed waves of dominant variants transitioning from BA.2 to BA.2.12.1, BA.4&5, BQ.1, and XBB.1.5. Our findings demonstrated early detection of variants and identification of unreported outbreaks, while showing strong consistency with clinical genome sequencing data at the local, state, and national levels. Alpha diversity analyses revealed significant periodical variations, with the highest diversity observed in winter and the outbreak lag phases, likely due to lower competition among variants before the outbreak growth phase. The data underscores the importance of low transmission periods for rapid mutation and variant evolution. This study highlights the effectiveness of integrating RBD amplicon sequencing with wastewater surveillance in tracking viral evolution, understanding variant emergence, and enhancing public health preparedness.

Developing wastewater-based surveillance schemes for multiple pathogens: The WastPan project in Finland

Wastewater comprises multiple pathogens and offers a potential for wastewater-based surveillance (WBS) to track the prevalence of communicable diseases. The Finnish WastPan project aimed to establish wastewater-based pandemic preparedness for multiple pathogens (viruses, bacteria, parasites, fungi), including antimicrobial resistance (AMR). This article outlines WastPan's experiences in this project, including the criteria for target selection, sampling locations, frequency, analysis methods, and results communication. Target selection relied on epidemiological and microbiological evidence and practical feasibility. Within the WastPan framework, wastewater samples were collected between 2021 and 2023 from 10 wastewater treatment plants (WWTPs) covering 40 % of Finland's population. WWTP selection was validated for reported cases of Extended Spectrum Beta-lactamase-producing bacterial pathogens (Escherichia coli and Klebsiella pneumoniae) from the National Infectious Disease Register. The workflow included 24-h composite influent samples, with one fraction for culture-based analysis (bacteria and fungi) and the rest of the sample was reserved for molecular analysis (viruses, bacteria, antibiotic resistance genes, and parasites). The reproducibility of the monitoring workflow was assessed for SARS-CoV-2 through inter-laboratory comparisons using the N2 and N1 assays. Identical protocols were applied to same-day samples, yielding similar positivity trends in the two laboratories, but the N2 assay achieved a significantly higher detection rate (Laboratory 1: 91.5 %; Laboratory 2: 87.4 %) than the N1 assay (76.6 %) monitored only in Laboratory 2 (McNemar, p < 0.001 Lab 1, = 0.006 Lab 2). This result indicates that the selection of monitoring primers and assays may impact monitoring sensitivity in WBS. Overall, the current study recommends that the selection of sampling frequencies and population coverage of the monitoring should be based on pathogen-specific epidemiological characteristics. For example, pathogens that are stable over time may need less frequent annual sampling, while those that are occurring across regions may require reduced sample coverage. Here, WastPan successfully piloted WBS for monitoring multiple pathogens, highlighting the significance of one-litre community composite wastewater samples for assessing community health. The infrastructure established for COVID-19 WBS is valuable for monitoring various pathogens. The prioritization of the monitoring targets optimizes resource utilization. In the future legislative support in target selection, coverage determination, and sustained funding for WBS is recomended.

Assessment of the surface water quality and primary health risk in urban wastewater and its receiving river Kathajodi, Cuttack of eastern India

Kathajodi, the principal southern distributary of the Mahanadi River, is the vital source of irrigation and domestic water use for densely populated Cuttack city which receives anthropogenic wastes abundantly. This study assesses the contamination level and primary health status of urban wastewater, and its receiving river Kathajodi based on the physicochemical quality indices employing inductively coupled plasma mass spectroscopy and aligning with guidelines from the United States Environmental Protection Agency (USEPA) and WHO. The high WQI, HPI, and HEI in the catchment area (KJ2, KJ3, and KJ4) indicate poor water quality due to the influx of domestic waste through the primary drainage system and effluents of healthcare units. A high BOD (4.33-19.66 mg L-1) in the catchment indicates high organic matter, animal waste, bacteriological contamination, and low DO, resulting in deterioration of water quality. CR values beyond limits (1.00E - 06 to 1.00E - 04) in three locations of catchment due to higher Cd, Pb, and As indicate significant carcinogenic risk, while high Mn, Cu, and Al content is responsible for several non-carcinogenic ailments and arsenic-induced physiological disorders. The elevated heavy metals Cd, Cu, Fe, Mn, Ni, and Zn, in Kathajodi, could be due to heavy coal combustion, vehicle exhaust, and industrial waste. On the other hand, Cu, Fe, K, and Al could be from agricultural practices, weathered rocks, and crustal materials. Positive significant (p ≤ 0.05) Pearson correlations between physicochemical parameters indicate their common anthropogenic origin and similar chemical characteristics. A strong correlation of PCA between elements and physiological parameters indicates their role in water quality deterioration. Assessing the surface water quality and heavy metal contents from this study will offer critical data to policymakers for monitoring and managing public health concerns.

Highly Multiplexed Reverse-Transcription Loop-Mediated Isothermal Amplification and Nanopore Sequencing (LAMPore) for Wastewater-Based Surveillance

Wastewater-based surveillance (WBS) has gained attention as a strategy to monitor and provide an early warning for disease outbreaks. Here, we applied an isothermal gene amplification technique, reverse-transcription loop-mediated isothermal amplification (RT-LAMP), coupled with nanopore sequencing (LAMPore) as a means to detect SARS-CoV-2. Specifically, we combined barcoding using both an RT-LAMP primer and the nanopore rapid barcoding kit to achieve highly multiplexed detection of SARS-CoV-2 in wastewater. RT-LAMP targeting the SARS-CoV-2 N region was conducted on 96 reactions including wastewater RNA extracts and positive and no-target controls. The resulting amplicons were pooled and subjected to nanopore sequencing, followed by demultiplexing based on barcodes that differentiate the source of each SARS-CoV-2 N amplicon derived from the 96 RT-LAMP products. The criteria developed and applied to establish whether SARS-CoV-2 was detected by the LAMPore assay indicated high consistency with polymerase chain reaction-based detection of the SARS-CoV-2 N gene, with a sensitivity of 89% and a specificity of 83%. We further profiled sequence variations on the SARS-CoV-2 N amplicons, revealing a number of mutations on a sample collected after viral variants had emerged. The results demonstrate the potential of the LAMPore assay to facilitate WBS for SARS-CoV-2 and the emergence of viral variants in wastewater.

Advances in wastewater analysis revealing the co-circulating viral trends of noroviruses and Omicron subvariants

Co-presence of enveloped and non-enveloped viruses is common both in community circulation and in wastewater. Community surveillance of infections requires robust methods enabling simultaneous quantification of multiple viruses in wastewater. Using enveloped SARS-CoV-2 Omicron subvariants and non-enveloped norovirus (NoV) as examples, this study reports a robust method that integrates electronegative membrane (EM) concentration, viral inactivation, and RNA preservation (VIP) with efficient capture and enrichment of the viral RNA on magnetic (Mag) beads, and direct detection of RNA on the beads. This method provided improved viral recoveries of 80 ± 4 % for SARS-CoV-2 and 72 ± 5 % for Murine NoV. Duplex reverse transcription quantitative polymerase chain reaction (RT-qPCR) assays with newly designed degenerate primer-probe sets offered high PCR efficiencies (90-91 %) for NoV (GI and GII) targets and were able to detect as few as 15 copies of the viral RNA per PCR reaction. This technique, combined with duplex detection of NoV and multiplex detection of Omicron, successfully quantified NoV (GI and GII) and Omicron variants in the same sets of 94 influent wastewater samples collected from two large wastewater systems between July 2022 and June 2023. The wastewater viral RNA results showed temporal changes of both NoV and Omicron variants in the same wastewater systems and revealed an inverse relationship of their emergence. This study demonstrated the importance of a robust analytical platform for simultaneous surveillance of enveloped and non-enveloped viruses in wastewater. The ability to sensitively determine multiple viral pathogens in wastewater will advance applications of wastewater surveillance as a complementary public health tool.

Correspondence of SARS-CoV-2 genomic sequences obtained from wastewater samples and COVID-19 patient at long-term care facilities

Wastewater-based epidemiology (WBE) has been in the spotlight because of applicability of early detection of virus outbreak and new variants at the catchment area. However, there has been a notable absence of research directly confirming the association between SARS-CoV-2 in wastewater and patient specimens. In this study, we performed a quantitative and qualitative investigation with a genetic-level comparison of SARS-CoV-2 between COVID-19 patients and SARS-CoV-2 positive wastewater samples at long-term care facilities. Wastewater samples were collected via passive sampling from manholes, and SARS-CoV-2 load in wastewater was determined by qPCR. We performed correlation analysis between SARS-CoV-2 load and COVID-19 case number, which suggested that SARS-CoV-2 was detected from wastewater earlier than ascertainment of COVID-19 case. Six and six RNA samples from COVID-19 positive cases and wastewater, respectively, from two facilities were then applied for amplicon sequencing analysis. Mutation analysis revealed high sequence similarity of SARS-CoV-2 variants between wastewater and patient samples (>99 %). To the best of our knowledge, this is the first study demonstrating that WBE is also effective in predicting predominant SARS-CoV-2 variant at facility-level, which is helpful to develop early-warning system for outbreak occurrence with predominant variant.

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

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

A scoping review of human pathogens detected in untreated human wastewater and sludge

Wastewater monitoring is an approach to identify the presence or abundance of pathogens within a population. The objective of this scoping review (ScR) was to identify and characterize research on human pathogens and antimicrobial resistance detected in untreated human wastewater and sludge. A search was conducted up to March 2023 and standard ScR methodology was followed. This ScR included 1,722 articles, of which 56.5% were published after the emergence of COVID-19. Viruses and bacteria were commonly investigated, while research on protozoa, helminths, and fungi was infrequent. Articles prior to 2019 were dominated by research on pathogens transmitted through fecal-oral or waterborne pathways, whereas more recent articles have explored the detection of pathogens transmitted through other pathways such as respiratory and vector-borne. There was variation in sampling, samples, and sample processing across studies. The current evidence suggests that wastewater monitoring could be applied to a range of pathogens as a public health tool to detect an emerging pathogen and understand the burden and spread of disease to inform decision-making. Further development and refinement of the methods to identify and interpret wastewater signals for different prioritized pathogens are needed to develop standards on when, why, and how to monitor effectively.

An interpretative review of the wastewater-based surveillance of the SARS-CoV-2: where do we stand on its presence and concern?

Wastewater-based epidemiology (WBE) has been used for monitoring infectious diseases like polio, hepatitis, etc. since the 1940s. It is also being used for tracking the SARS-CoV-2 at the population level. This article aims to compile and assess the information for the qualitative and quantitative detection of the SARS-CoV-2 in wastewater. Based on the globally published studies, we highlight the importance of monitoring SARS-CoV-2 presence/detection in the wastewater and concurrently emphasize the development of early surveillance techniques. SARS-CoV-2 RNA sheds in the human feces, saliva, sputum and mucus that ultimately reaches to the wastewater and brings viral RNA into it. For the detection of the virus in the wastewater, different detection techniques have been optimized and are in use. These are based on serological, biosensor, targeted PCR, and next generation sequencing for whole genome sequencing or targeted amplicon sequencing. The presence of the SARS-CoV-2 RNA in wastewater could be used as a potential tool for early detection and devising the strategies for eradication of the virus before it is spread in the community. Additionally, with the right and timely understanding of viral behavior in the environment, an accurate and instructive model that leverages WBE-derived data may be created. This might help with the creation of technological tools and doable plans of action to lessen the negative effects of current viral epidemics or future potential outbreaks on public health and the economy. Further work toward whether presence of viral load correlates with its ability to induce infection, still needs evidence. The current increasing incidences of JN.1 variant is a case in point for continued early detection and surveillance, including wastewater.

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

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

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

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

Quantitative analysis of SARS-CoV-2 RNA in wastewater and evaluation of sampling frequency during the downward period of a COVID-19 wave in Japan

Wastewater-based epidemiology (WBE) is a practical approach for detecting the presence of SARS-CoV-2 infections and assessing the epidemic trend of the coronavirus disease 2019 (COVID-19). The purpose of this study was to evaluate the minimum sampling frequency required to properly identify the COVID-19 trend during the downward epidemic period when using a highly sensitive RNA detection method. WBE was conducted using the Efficient and Practical virus Identification System with ENhanced Sensitivity for Solids (EPISENS-S), a highly sensitive SARS-CoV-2 RNA detection method, at nine neighboring wastewater treatment plants (WWTPs). These WWTPs were in the same prefecture in Japan, and they had different sewer types, sampling methods, and sampling frequencies. The overall detection rate of SARS-CoV-2 RNA was 97.8 % during the entire study period when the geometric means of new COVID-19 cases per 100,000 inhabitants were between 3.3 and 7.7 in each WWTP. The maximum SARS-CoV-2 RNA concentration in wastewater was 2.14 × 104 copies/L, which corresponded to pepper mild mottle virus (PMMoV)-normalized concentrations of 6.54 × 10-3. We evaluated the effect of sampling frequencies on the probability of a significant correlation with the number of newly reported COVID-19 cases by hypothetically reducing the sampling frequency in the same dataset. When the wastewater sampling frequency occurred 5, 3, 2, and 1 times per week, these results exhibited significant correlations of 100 % (5/5), 89 % (8/9), 85 % (23/27), and 48 % (13/27), respectively. To achieve significant correlation with a high probability of over 85 %, a minimum sampling frequency of twice per week is required, even if sampling methods and sewer types are different. WBE using the EPISENS-S method and a sampling frequency of more than twice a week can be used to properly monitor COVID-19 wave epidemic trends, even during downward periods.

Comparison of RT-ddPCR and RT-qPCR platforms for SARS-CoV-2 detection: Implications for future outbreaks of infectious diseases

The increased frequency of human infectious disease outbreaks caused by RNA viruses worldwide in recent years calls for enhanced public health surveillance for better future preparedness. Wastewater-based epidemiology (WBE) is emerging as a valuable epidemiological tool for providing timely population-wide surveillance for disease prevention and response complementary to the current clinical surveillance system. Here, we compared the analytical performance and practical applications between predominant molecular detection methods of RT-qPCR and RT-ddPCR on SARS-CoV-2 detection in wastewater surveillance. When pure viral RNA was tested, RT-ddPCR exhibited superior quantification accuracy at higher concentration levels and achieved more sensitive detection with reduced variation at low concentration levels. Furthermore, RT-ddPCR consistently demonstrated more robust and accurate measurement either in the background of the wastewater matrix or with the presence of mismatches in the target regions of the consensus assay. Additionally, by detecting mock variant RNA samples, we found that RT-ddPCR outperformed RT-qPCR in virus genotyping by targeting specific loci with signature mutations in allele-specific (AS) assays, especially at low levels of allele frequencies and concentrations, which increased the possibility for sensitive low-prevalence variant detection in the population. Our study provides insights for detection method selection in the WBE applications for future infectious disease outbreaks.

Developing scientific literacy with a cyclic independent study assisted CURE detecting SARS-CoV-2 in wastewater

The COVID-19 pandemic has exposed a high level of scientific illiteracy and mistrust that pervades the scientific and medical communities. This finding has proven the necessity of updating current methods used to expose undergraduates to research. The research in traditional course-based undergraduate research experiences (CUREs) is limited by undergraduate time constraints, skill level, and course structure, and consequently it does not attain the learning objectives or the high-impact, relevant studies achieved in graduate-level laboratories using a cyclic trainee/trainer model. Although undergraduate independent study (ISY) research more closely matches the structure and learning objectives of graduate-level research, they are uncommon as professors and universities typically view them as a significant time and resource burden with limited return. Cyclic independent study-assisted CUREs (CIS-CUREs) combine many positive aspects of ISY graduate-level research, and CUREs by pre-training ISY research lead to facilitate CURE proposal and project semesters in a cyclic model. The CIS-CURE approach allowed undergraduate students at Stetson University to perform and disseminate more rigorous, involved, long-term, and challenging research projects, such as the surveillance of SARS-CoV-2 in wastewater. In doing so, all students would have the opportunity to participate in a high-impact research project and consequently gain a more comprehensive training, reach higher levels of research dissemination, and increase their competitiveness after graduating. Together, CIS-CUREs generate graduates with higher scientific literacy and thus combat scientific mistrust in communities.

Time series modelling for wastewater-based epidemiology of COVID-19: A nationwide study in 40 wastewater treatment plants of Belgium, February 2021 to June 2022

BACKGROUND

Wastewater-based epidemiology (WBE) has been implemented to monitor surges of COVID-19. Yet, multiple factors impede the usefulness of WBE and quantitative adjustment may be required.

AIM

We aimed to model the relationship between WBE data and incident COVID-19 cases, while adjusting for confounders and autocorrelation.

METHODS

This nationwide WBE study includes data from 40 wastewater treatment plants (WWTPs) in Belgium (02/2021-06/2022). We applied ARIMA-based modelling to assess the effect of daily flow rate, pepper mild mottle virus (PMMoV) concentration, a measure of human faeces in wastewater, and variants (alpha, delta, and omicron strains) on SARS-CoV-2 RNA levels in wastewater. Secondly, adjusted WBE metrics at different lag times were used to predict incident COVID-19 cases. Model selection was based on AICc minimization.

RESULTS

In 33/40 WWTPs, RNA levels were best explained by incident cases, flow rate, and PMMoV. Flow rate and PMMoV were associated with -13.0 % (95 % prediction interval: -26.1 to +0.2 %) and +13.0 % (95 % prediction interval: +5.1 to +21.0 %) change in RNA levels per SD increase, respectively. In 38/40 WWTPs, variants did not explain variability in RNA levels independent of cases. Furthermore, our study shows that RNA levels can lead incident cases by at least one week in 15/40 WWTPs. The median population size of leading WWTPs was 85.1 % larger than that of non‑leading WWTPs. In 17/40 WWTPs, however, RNA levels did not lead or explain incident cases in addition to autocorrelation.

CONCLUSION

This study provides quantitative insights into key determinants of WBE, including the effects of wastewater flow rate, PMMoV, and variants. Substantial inter-WWTP variability was observed in terms of explaining incident cases. These findings are of practical importance to WBE practitioners and show that the early-warning potential of WBE is WWTP-specific and needs validation.

Biosensors for waterborne virus detection: Challenges and strategies

Waterborne viruses that can be harmful to human health pose significant challenges globally, affecting health care systems and the economy. Identifying these waterborne pathogens is essential for preventing diseases and protecting public health. However, handling complex samples such as human and wastewater can be challenging due to their dynamic and complex composition and the ultralow concentration of target analytes. This review presents a comprehensive overview of the latest breakthroughs in waterborne virus biosensors. It begins by highlighting several promising strategies that enhance the sensing performance of optical and electrochemical biosensors in human samples. These strategies include optimizing bioreceptor selection, transduction elements, signal amplification, and integrated sensing systems. Furthermore, the insights gained from biosensing waterborne viruses in human samples are applied to improve biosensing in wastewater, with a particular focus on sampling and sample pretreatment due to the dispersion characteristics of waterborne viruses in wastewater. This review suggests that implementing a comprehensive system that integrates the entire waterborne virus detection process with high-accuracy analysis could enhance virus monitoring. These findings provide valuable insights for improving the effectiveness of waterborne virus detection, which could have significant implications for public health and environmental management.

Systematic review and meta-analysis of human health-related protein markers for realizing real-time wastewater-based epidemiology

For effective implementation of the wastewater-based epidemiology (WBE) approach, real-time quantification of markers in wastewater is critical for data acquisition before data interpretation, dissemination, and decision-making. This can be achieved by using biosensor technology, but whether the quantification/detection limits of different types of biosensors comply with the concentration of WBE markers in wastewater is unclear. In the present study, we identified promising protein markers with relatively high concentrations in wastewater samples and analyzed biosensor technologies that are potentially available for real-time WBE. The concentrations of potential protein markers in stool and urine samples were obtained through systematic review and meta-analysis. We examined 231 peer-review papers to collect information regarding potential protein markers that can enable us to achieve real-time monitoring using biosensor technology. Fourteen markers in stool samples were identified at the ng/g level, presumably equivalent to ng/L of wastewater after dilution. Moreover, relatively high average concentrations of fecal inflammatory proteins were observed, e.g., fecal calprotectin, clusterin, and lactoferrin. Fecal calprotectin exhibited the highest average log concentration among the markers identified in stool samples with its mean value being 5.24 [95 % CI: 5.05, 5.42] ng/g. We identified 50 protein markers in urine samples at the ng/mL level. Uromodulin (4.48 [95 % CI: 4.20, 4.76] ng/mL) and plasmin (4.18 [95 % CI: 3.15, 5.21] ng/mL) had the top two highest log concentrations in urine samples. Furthermore, the quantification limit of some electrochemical- and optical-based biosensors was found to be around the femtogram/mL level, which is sufficiently low to detect protein markers in wastewater even after dilution in sewer pipes.

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

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

Surveillance of multidrug-resistant tuberculosis in sub-Saharan Africa through wastewater-based epidemiology

The spread of multidrug-resistant tuberculosis (MDR-TB) is a serious public health issue, particularly in developing nations. The current methods of monitoring drug-resistant TB (DR-TB) using clinical diagnoses and hospital records are insufficient due to limited healthcare access and underreporting. This study proposes using Wastewater-Based Epidemiology (WBE) to monitor DR-TB in six African countries (Ghana, Nigeria, Kenya, Uganda, Cameroon, and South Africa) and examines the impact of treated wastewater on the spread of TB drug-resistant genes in the environment. Using droplet-digital polymerase chain reaction (ddPCR), the study evaluated untreated and treated wastewater samples in selected African countries for TB surveillance. There was a statistically significant difference in concentrations of genes conferring resistance to TB drugs in wastewater samples from the selected countries (p-value<0.05); South African samples exhibited the highest concentrations of 4.3(±2,77), 4.8(±2.96), 4.4(±3,10) and 4.7(±3,39) log copies/ml for genes conferring resistance to first-line TB drugs (katG, rpoB, embB and pncA respectively) in untreated wastewater. This may be attributed to the higher prevalence of TB/MDR-TB in SA compared to other African countries. Interestingly, genes conferring resistance to second-line TB drugs such as delamanid (ddn gene) and bedaquiline (atpE gene) were detected in relatively high concentrations (4.8(±3,67 and 3.2(±2,31 log copies/ml for ddn and atpE respectively) in countries, such as Cameroon, where these drugs are not part of the MDR-TB treatment regimens, perhaps due to migration or the unapproved use of these drugs in the country. The gene encoding resistance to streptomycin (rrs gene) was abundant in all countries, perhaps due to the common use of this antibiotic for infections other than TB. These results highlight the need for additional surveillance and monitoring, such as WBE, to gather data at a community level. Combining WBE with the One Health strategy and current TB surveillance systems can help prevent the spread of DR-TB in populations.

20-Month monitoring of SARS-CoV-2 in wastewater of Curitiba, in Southern Brazil

The COVID-19 pandemic resulted in the collapse of healthcare systems and led to the development and application of several approaches of wastewater-based epidemiology to monitor infected populations. The main objective of this study was to carry out a SARS-CoV-2 wastewater based surveillance in Curitiba, Southern Brazil Sewage samples were collected weekly for 20 months at the entrance of five treatment plants representing the entire city and quantified by qPCR using the N1 marker. The viral loads were correlated with epidemiological data. The correlation by sampling points showed that the relationship between the viral loads and the number of reported cases was best described by a cross-correlation function, indicating a lag between 7 and 14 days amidst the variables, whereas the data for the entire city presented a higher correlation (0.84) with the number of positive tests at lag 0 (sampling day). The results also suggest that the Omicron VOC resulted in higher titers than the Delta VOC. Overall, our results showed that the approach used was robust as an early warning system, even with the use of different epidemiological indicators or changes in the virus variants in circulation. Therefore, it can contribute to public decision-makers and health interventions, especially in vulnerable and low-income regions with limited clinical testing capacity. Looking toward the future, this approach will contribute to a new look at environmental sanitation and should even induce an increase in sewage coverage rates in emerging countries.

RNA-Seq of untreated wastewater to assess COVID-19 and emerging and endemic viruses for public health surveillance

Background

The COVID-19 pandemic showcased the power of genomic sequencing to tackle the emergence and spread of infectious diseases. However, metagenomic sequencing of total microbial RNAs in wastewater has the potential to assess multiple infectious diseases simultaneously and has yet to be explored.

Methods

A retrospective RNA-Seq epidemiological survey of 140 untreated composite wastewater samples was performed across urban (n = 112) and rural (n = 28) areas of Nagpur, Central India. Composite wastewater samples were prepared by pooling 422 individual grab samples collected prospectively from sewer lines of urban municipality zones and open drains of rural areas from 3rd February to 3rd April 2021, during the second COVID-19 wave in India. Samples were pre-processed and total RNA was extracted prior to genomic sequencing.

Findings

This is the first study that has utilised culture and/or probe-independent unbiased RNA-Seq to examine Indian wastewater samples. Our findings reveal the detection of zoonotic viruses including chikungunya, Jingmen tick and rabies viruses, which have not previously been reported in wastewater. SARS-CoV-2 was detectable in 83 locations (59%), with stark abundance variations observed between sampling sites. Hepatitis C virus was the most frequently detected infectious virus, identified in 113 locations and co-occurring 77 times with SARS-CoV-2; and both were more abundantly detected in rural areas than urban zones. Concurrent identification of segmented virus genomic fragments of influenza A virus, norovirus, and rotavirus was observed. Geographical differences were also observed for astrovirus, saffold virus, husavirus, and aichi virus that were more prevalent in urban samples, while the zoonotic viruses chikungunya and rabies, were more abundant in rural environments.

Interpretation

RNA-Seq can effectively detect multiple infectious diseases simultaneously, facilitating geographical and epidemiological surveys of endemic viruses that could help direct healthcare interventions against emergent and pre-existent infectious diseases as well as cost-effectively and qualitatively characterising the health status of the population over time.

Funding

UK Research and Innovation (UKRI) Global Challenges Research Fund (GCRF) grant number H54810, as supported by Research England.

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

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

Assessment of virus concentration methods for detecting SARS-CoV-2 IN wastewater

Wastewater-based epidemiology has been described as a valuable tool for monitoring the spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in a community. However, there is no consensus on the best concentration method to allow reliable detection of SARS-CoV-2 in this matrix, considering different laboratory facilities. This study compares two viral concentration methods, ultracentrifugation (ULT) and skimmed-milk flocculation (SMF), for detecting SARS-CoV-2 in wastewater samples. The analytical sensitivity (limits of detection and quantification [LoD/LoQ]) of both methods was evaluated using a bovine respiratory syncytial virus (BRSV) as a surrogate. Three different approaches were conducted to establish LoD of each method based on the assays on the standard curve (ALoDsc), on the dilution of internal control (ALoDiC), and the processing steps (PLoD). For PLoD, ULT method had the lowest value (1.86 × 103 genome copy/microliter [GC/µL]) when compared to the SMF method (1.26 × 107 GC/µL). The LoQ determination showed a mean value of 1.55 × 105 GC/µL and 3.56 × 108 GC/µL to ULT and SMF, respectively. The detection of SARSCoV-2 in naturally contaminated wastewater revealed 100% (12/12) and 25% (3/12) of detection using ULT and SMF with quantification ranging from 5.2 to 7.2 log10 genome copy/liter (GC/L) and 5.06 to 5.46 log10 GC/L, respectively. The detection success rate of BRSV used as an internal control process was 100% (12/12) for ULT and 67% (8/12) for SMF, with an efficiency recovery rate ranging from 12 to 38% and 0.1 to 5%, respectively. Our data consolidates the importance of assessing the methods used; however, further analysis should be carried out to improve low-cost concentration methodologies, essential for use in low-income and developing countries.

Wastewater-Based Epidemiology as a Tool to Detect SARS-CoV-2 Circulation at the Community Level: Findings from a One-Year Wastewater Investigation Conducted in Sicily, Italy

Wastewater-based epidemiology is a well-established tool for detecting and monitoring the spread of enteric pathogens and the use of illegal drugs in communities in real time. Since only a few studies in Italy have investigated the correlation between SARS-CoV-2 in wastewater and the prevalence of COVID-19 cases from clinical testing, we conducted a one-year wastewater surveillance study in Sicily to correlate the load of SARS-CoV-2 RNA in wastewater and the reported cumulative prevalence of COVID-19 in 14 cities from October 2021 to September 2022. Furthermore, we investigated the role of SARS-CoV-2 variants and subvariants in the increase in the number of SARS-CoV-2 infections. Our findings showed a significant correlation between SARS-CoV-2 RNA load in wastewater and the number of active cases reported by syndromic surveillance in the population. Moreover, the correlation between SARS-CoV-2 in wastewater and the active cases remained high when a lag of 7 or 14 days was considered. Finally, we attributed the epidemic waves observed to the rapid emergence of the Omicron variant and the BA.4 and BA.5 subvariants. We confirmed the effectiveness of wastewater monitoring as a powerful epidemiological proxy for viral variant spread and an efficient complementary method for surveillance.

An innovative approach for predicting pandemic hotspots in complex wastewater networks using graph theory coupled with fuzzy logic

Early prediction of COVID-19 infected communities (potential hotspots) is essential to limit the spread of virus. Diagnostic testing has limitations in big populations because it cannot deliver information at a fast enough rate to stop the spread in its early phases. Wastewater based epidemiology (WBE) experiments showed promising results for brisk detection of 'SARS CoV-2' RNA in urban wastewater. However, a systematic and targeted approach to track COVID-19 virus in the complex wastewater networks at a community level is lacking. This research combines graph network (GN) theory with fuzzy logic to determine the chances of a specific community being a COVID-19 hotspot in a wastewater network. To detect 'SARS-CoV-2' RNA, GN divides wastewater network into communities and fuzzy logic-based inference system is used to identify targeted communities. For the propose of tracking, 4000 sample cases from Minnesota (USA) were tested based on various contributing factors. With a probability score of greater than 0.8, 42% of cases were likely to be designated as COVID-19 hotspots based on multiple demographic characteristics. The research enhances the conventional WBE approach through two novel aspects, viz. (1) by integrating graph theory with fuzzy logic for quick prediction of potential hotspot along with its likelihood percentage in a wastewater network, and (2) incorporating the uncertainty associated with COVID-19 contributing factors using fuzzy membership functions. The targeted approach allows for rapid testing and implementation of vaccination campaigns in potential hotspots. Consequently, governmental bodies can be well prepared to check future pandemics and variant spreading in a more planned manner.

Supplementary Information

The online version contains supplementary material available at 10.1007/s00477-023-02468-3.

High resolution mass spectrometry (HRMS) determination of drugs in wastewater and wastewater based epidemiology in Cadiz Bay (Spain)

Multi-residue methods for the determination of the myriad of compounds of emerging concern (CECs) entering in the environment are key elements for further assessment on their distribution and fate. Here, we have developed an analytical protocol for the simultaneous analysis of 195 prescription, over-the-counter, and illicit drugs by using a combination of solid phase extraction (SPE) and determination by liquid chromatography coupled to high resolution mass spectrometry (LC-HRMS). The method was applied to the analysis of influent sewage samples from 3 wastewater treatment plants (WWTPs) from Cadiz Bay (SW Spain), enabling the quantification of more than 100 pharmaceuticals, 19 of them at average concentrations higher than 1 μg L^-1, including caffeine (92 μg L^-1), paracetamol (72 μg L^-1), and ibuprofen (56 μg L^-1), as well as several illicit drugs (e.g., cocaine). Wastewater based epidemiology (WBE) was applied for 27 of the detected compounds to establish their consumption in the sampling area, which has been never attempted before. Caffeine, naproxen, and salicylic acid stood out because of their high consumption (638, 51, and 20 g d^-1·1000pop^-1, respectively). Regarding illicit drugs, cocaine showed the highest frequency of detection and we estimated an average consumption of 3683 mg d^-1·1000pop^-1 in Cadiz Bay. The combination of new HRMS methods, capable of discriminating thousands of chemicals, and WBE will allow for a more comprehensive characterization of chemical substances and their consumption in urban environments in the near future.

Optimised protocol for monitoring SARS-CoV-2 in wastewater using reverse complement PCR-based whole-genome sequencing

Monitoring the spread of viral pathogens in the population during epidemics is crucial for mounting an effective public health response. Understanding the viral lineages that constitute the infections in a population can uncover the origins and transmission patterns of outbreaks and detect the emergence of novel variants that may impact the course of an epidemic. Population-level surveillance of viruses through genomic sequencing of wastewater captures unbiased lineage data, including cryptic asymptomatic and undiagnosed infections, and has been shown to detect infection outbreaks and novel variant emergence before detection in clinical samples. Here, we present an optimised protocol for quantification and sequencing of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in influent wastewater, used for high-throughput genomic surveillance in England during the COVID-19 pandemic. This protocol utilises reverse compliment PCR for library preparation, enabling tiled amplification across the whole viral genome and sequencing adapter addition in a single step to enhance efficiency. Sequencing of synthetic SARS-CoV-2 RNA provided evidence validating the efficacy of this protocol, while data from high-throughput sequencing of wastewater samples demonstrated the sensitivity of this method. We also provided guidance on the quality control steps required during library preparation and data analysis. Overall, this represents an effective method for high-throughput sequencing of SARS-CoV-2 in wastewater which can be applied to other viruses and pathogens of humans and animals.

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

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

Analysis of metatranscriptomic methods to enable wastewater-based biosurveillance of all infectious diseases

Introduction

Wastewater-based surveillance emerged during the COVID-19 pandemic as an efficient way to quickly screen large populations, monitor infectious disease transmission over time, and identify whether more virulent strains are becoming more prevalent in the region without burdening the health care system with individualized testing. Ohio was one of the first states to implement wastewater monitoring through its Ohio Coronavirus Wastewater Monitoring Network (OCWMN), originally tracking the prevalence of COVID-19 by quantitative qPCR from over 67 sites across the state. The OCWMN evolved along with the pandemic to include sequencing the SARS-CoV-2 genome to assess variants of concern circulating within the population. As the pandemic wanes, networks such as OCWMN can be expanded to monitor other infectious diseases and outbreaks of interest to the health department to reduce the burden of communicable diseases. However, most surveillance still utilizes qPCR based diagnostic tests for individual pathogens, which is hard to scale for surveillance of multiple pathogens.

Methods

Here we have tested several genomic methods, both targeted and untargeted, for wastewater-based biosurveillance to find the most efficient procedure to detect and track trends in reportable infectious diseases and outbreaks of known pathogens as well as potentially novel pathogens or variants on the rise in our communities. RNA extracts from the OCWMN were provided weekly from 10 sites for 6 weeks. Total RNA was sequenced from the samples on the Illumina NextSeq and on the MinION to identify pathogens present. The MinION long read platform was also used to sequence SARS-CoV-2 with the goal of reducing the complexity of variant calling in mixed populations as occurs with short Illumina reads. Finally, a targeted hybridization approach was tested for compatibility with wastewater RNA samples.

Results and discussion

The data analyzed here provides a baseline assessment that demonstrates that wastewater is a rich resource for infectious disease epidemiology and identifies technology gaps and potential solutions to enable this resource to be used by public health laboratories to monitor the infectious disease landscape of the regions they serve.

Changing molecular epidemiology of Hepatitis A virus in Santiago, Chile from 2010 to 2021

OBJECTIVES

Hepatitis A (HAV) virus causes asymptomatic to life-treating fulminant hepatitis. During infection, patients show large viral excretion in their stools. Resistance of HAV to environmental conditions, allows us to recover viral nucleotide sequences from wastewater and trace its evolutionary history.

METHODS

We characterize twelve years of HAV circulation in wastewater from Santiago, Chile, and conducted phylogenetic analyses to decipher the dynamics of circulating lineages.

RESULTS

We observed the exclusive circulation of the HAV IA genotype. The molecular epidemiologic analyses showed a steady circulation of a dominant lineage with low genetic diversity (d = 0,007) between 2010 and 2017. An outbreak of Hepatitis A associated with men who have sex with men, in 2017 was associated with the irruption of a new lineage. Remarkably, a dramatic change in the dynamic of HAV circulation was observed in the period post-outbreak; between 2017 and 2021 when 4 different lineages were transiently detected. Exhaustive phylogenetic analyses indicate that these lineages were introduced and possibly derived from isolates from other Latin American countries.

CONCLUSION

The HAV circulation in recent years in Chile is rapidly changing and suggests that this phenomenon could be a consequence of massive population migrations in Latin America caused by political instability and natural disasters.

Sewershed surveillance as a tool for smart management of a pandemic in threshold countries. Case study: Tracking SARS-CoV-2 during COVID-19 pandemic in a major urban metropolis in northwestern Argentina

Wastewater-based epidemiology is an economical and effective tool for monitoring the COVID-19 pandemic. In this study we proposed sampling campaigns that addressed spatial-temporal trends within a metropolitan area. This is a local study of detection and quantification of SARS-CoV-2 in wastewater during the onset, rise, and decline of COVID-19 cases in Salta city (Argentina) over the course of a twenty-one-week period (13 Aug to 30 Dec) in 2020. Wastewater samples were gathered from 13 sewer manholes specific to each sewershed catchment, prior to convergence or mixing with other sewer lines, resulting in samples specific to individual catchments with defined areas. The 13 sewershed catchments selected comprise 118,832 connections to the network throughout the city, representing 84.7 % (534,747 individuals) of the total population. The number of COVID19-related exposure and symptoms cases in each area were registered using an application developed for smartphones by the provincial government. Geographical coordinates provided by the devices were recorded, and consequently, it was possible to geolocalise all app-cases and track them down to which of the 13 sampling catchments belonged. RNA fragments of SARS-CoV-2 were detected in every site since the beginning of the monitoring, anticipating viral circulation in the population. Over the course of the 21-week study, the concentrations of SARS-CoV-2 ranged between 1.77 × 104 and 4.35 × 107 genome copies/L. There was a correspondence with the highest viral load in wastewater and the peak number of cases reported by the app for each catchment. The associations were evaluated with correlation analysis. The viral loads of SARS-CoV-2 in wastewater were a feasible means to describe the trends of COVID-19 infections. Surveillance at sewershed scale, provided reliable and strategic information that could be used by local health stakeholders to manage the COVID-19 pandemic.

Wastewater surveillance of SARS-CoV-2 and influenza in preK-12 schools shows school, community, and citywide infections

Wastewater surveillance is a passive and efficient way to monitor the spread of infectious diseases in large populations and high transmission areas such as preK-12 schools. Infections caused by respiratory viruses in school-aged children are likely underreported, particularly because many children may be asymptomatic or mildly symptomatic. Wastewater monitoring of SARS-CoV-2 has been studied extensively and primarily by sampling at centralized wastewater treatment plants, and there are limited studies on SARS-CoV-2 in preK-12 school wastewater. Similarly, wastewater detections of influenza have only been reported in wastewater treatment plant and university manhole samples. Here, we present the results of a 17-month wastewater monitoring program for SARS-CoV-2 (n = 2176 samples) and influenza A and B (n = 1217 samples) in 51 preK-12 schools. We show that school wastewater concentrations of SARS-CoV-2 RNA were strongly associated with COVID-19 cases in schools and community positivity rates, and that influenza detections in school wastewater were significantly associated with citywide influenza diagnosis rates. Results were communicated back to schools and local communities to enable mitigation strategies to stop the spread, and direct resources such as testing and vaccination clinics. This study demonstrates that school wastewater surveillance is reflective of local infections at several population levels and plays a crucial role in the detection and mitigation of outbreaks.

Contribution of wastewater-based epidemiology to SARS-CoV-2 screening in Brazil and the United States

Wastewater-based epidemiology (WBE) is a valuable tool for investigating the existence, prevalence, and spread of pathogens, such as SARS-CoV-2, in a given population. WBE, proposed as part of the SARS-CoV-2 surveillance strategy for monitoring virus circulation, may complement clinical data and contribute to reducing the spread of the disease through early detection. In developing countries such as Brazil, where clinical data are scarce, information obtained from wastewater monitoring can be crucial for designing public health interventions. In the United States, the country with the largest number of confirmed SARS-CoV-2 cases worldwide, WBE programs have begun to be carried out to investigate correlations with coronavirus disease 2019 (COVID-19) clinical data and support health agencies in decision-making to prevent the spread of the disease. This systematic review aimed to assess the contribution of WBE to SARS-CoV-2 screening in Brazil and the United States and compare studies conducted in a developed and developing country. Studies in Brazil and the United States showed WBE to be an important epidemiological surveillance strategy in the context of the COVID-19 pandemic. WBE approaches are useful for early detection of COVID-19 outbreaks, estimation of clinical cases, and assessment of the effectiveness of vaccination program.

High-throughput sequencing as a tool for monitoring prokaryote communities in a wastewater treatment plant

In this study, the DNA metabarcoding technique was used to explore the prokaryote diversity and community structure in wastewater collected in spring and winter 2020-2021 as well as the efficiency of the treatment in a wastewater treatment plant (WWTP) in Ría de Vigo (NW Spain). The samplings included raw wastewater from the inlet stream (M1), the discharge water after the disinfection treatment (M3) and mussels used as bioindicators of possible contamination of the marine environment. Significant differences were discovered in the microbiome of each type of sample (M1, M3 and mussels), with 92 %, 45 % and 44 % of exclusive OTUs found in mussel, M3 and M1 samples respectively. Seasonal differences were also detected in wastewater samples, with which abiotic parameters (temperature, pH) could be strongly involved. Bacteria present in raw wastewater (M1) were associated with the human gut microbiome, and therefore, potential pathogens that could be circulating in the population in specific periods were detected (e.g., Arcobacter sp. and Clostridium sp.). A considerable decrease in putative pathogenic organisms from the M1 to M3 wastewater fractions and the scarce presence in mussels (<0.5 % total reads) confirmed the effectiveness of pathogen removal in the wastewater treatment plant. Our results showed the potential of the DNA metabarcoding technique for monitoring studies and confirmed its application in wastewater-based epidemiology (WBE) and environmental contamination studies. Although this technique cannot determine if the infective pathogens are present, it can characterize the microbial communities and the putative pathogens that are circulating through the population (microbiome of M1) and also confirm the efficacy of depuration treatment, which can directly affect the aquaculture sector and even human and veterinary health.

Wastewater-based epidemiology approach: The learning lessons from COVID-19 pandemic and the development of novel guidelines for future pandemics

Wastewater-based epidemiology (WBE) provides a comprehensive real-time framework of population attitude and health status. This approach is attracting the interest of medical community and health authorities to monitor the prevalence of a virus (such as the severe acute respiratory syndrome coronavirus 2, SARS-CoV-2) among a community. Indeed, WBE is currently fine-tuning as environmental surveillance tool for coronavirus disease 2019 (COVID-19) pandemic. After a bibliometric analysis conducted to discover the research trends in WBE field, this work aimed to side-by-side compare the conventional method based on clinical testing with WBE approach. Furthermore, novel guidelines were developed to apply the WBE approach to a pandemic. The growing interest on WBE approach for COVID-19 pandemic is demonstrated by looking at the sharp increase in scientific papers published in the last years and at the ongoing studies on viral quantification methods and analytical procedures. The side-by-side comparison highlighted the ability of WBE to identify the hot-spot areas faster than the conventional approach, reducing the costs (e.g., rational use of available resources) and the gatherings at medical centers. Contrary to clinical testing, WBE has the surveillance capacity for preventing the virus resurgence, including asymptomatic contribution, and ensuring the preservation of medical staff health by avoiding the exposure to the virus infection during clinical testing. As extensively reported, the time in collecting epidemiological data is crucial for establishing the prevention and mitigation measures that are essential for curbing a pandemic. The developed guidelines can help to build a WBE system useful to control any future pandemic.