The spread of antimicrobial resistance (AMR) around the globe, especially in the urban cities with high population, is a major concern. Therefore, the current study aims at identifying antibiotic resistant bacteria, microbial community compositions and the quantification of antimicrobial resistant genes from six sewage treatment plants (STPs) across Pune city in Maharashtra, India. A total of 106 isolates obtained were tested against six antibiotics in which the highest resistance was observed against trimethoprim (24.53 %). The qPCR assays of seven antibiotic resistance genes revealed abundance of blaimp-1 and mecA genes in the summer and monsoon seasons followed by blaNDM-1 gene in the summer and winter seasons. The alpha diversity indices depicted highest microbial diversity of inlet samples during winter, followed by inlet samples during the summer and monsoon seasons. Comparative analysis revealed Bifidobacterium (51 %), Pseudomonas (28.7 %) and Zoogloea (17.6 %) as the most abundant genera in the inlet samples during the summer, monsoon and winter seasons respectively while Acinetobacter (31 %) and Flavobacterium (23 % in winter and 18.2 % in summer) dominated the outlet samples. The co-network analysis revealed positive and negative interactions in the winter and monsoon but only positive interactions in the summer season. Venn diagrams showed higher abundance of ASVs in the outlet samples than the inlet. The top genera correlated exactly opposite with the pH compared to BOD and COD. PICRUSt2-based functional prediction revealed a higher abundance of methicillin resistance, β-lactamase resistance and multidrug resistance genes in inlet samples while chloramphenicol resistance was found higher in outlet samples. Further, we observed that potential pathogens causing infectious disease such as pertussis, shigellosis and tuberculosis were present in all three seasons.

The prolonged viral shedding from the gastrointestinal tract is well documented for numerous pathogens, including SARS-CoV-2. However, the impact of prolonged viral shedding on epidemiological inferences using wastewater data is not yet fully understood.

To gain a better understanding of this phenomenon at the population level, we extended a wastewater-based modeling framework that integrates viral shedding dynamics, viral load data in wastewater, case report data, and an epidemic model.

Our results indicate that as an outbreak progresses, the viral load from recovered individuals gradually becomes predominant, surpassing that from the infectious population. This phenomenon leads to a dynamic relationship between model-inferred and reported daily incidence over the course of an outbreak. Sensitivity analyses on the duration and rate of viral shedding for recovered individuals reveal that accounting for this phenomenon can considerably advance prediction of transmission peak timing. Furthermore, extensive viral shedding from the recovered population toward the conclusion of an epidemic wave may overshadow viral signals from newly infected cases carrying emerging variants, which can delay the rapid recognition of emerging variants based on viral load.

These findings highlight the necessity of integrating post-recovery viral shedding to enhance the accuracy and utility of wastewater-based epidemiological analysis.

Wastewater-based surveillance has been commonly used as a monitoring tool for public health. Also, viruses present in wastewater can pose a health risk. In this study, we screened enterovirus, rhinovirus, norovirus GI and GII, pan-adenovirus, and gastroenteritis-causing adenovirus F40/41 in different wastewater sample types using reverse transcription quantitative polymerase chain reaction and quantitative polymerase chain reaction. We analyzed composite influent samples (N = 22), grab influent samples (N = 20), composite effluent samples (N = 78), grab effluent samples (N = 21), and activated sludge samples (N = 34) collected from six municipal wastewater treatment plants in the Pirkanmaa region of Finland. We detected the viruses in the influent to discover if they had the potential to be monitored using wastewater-based surveillance. In addition, we studied viruses in effluent and activated sludge to detect viruses that persisted in treatment processes. Furthermore, all sample types were compared to discover differences in the viral contents of different wastewater sample types. We detected all the studied viruses in influent, while in activated sludge, we detected enterovirus, pan-adenovirus, and adenovirus F40/41, and in effluent enterovirus, norovirus GI and GII, pan-adenovirus, and adenovirus F40/41 were identified. In addition, the relative amount of all the viruses was the highest in the influent. Our study also showed that composite sampling was a more representative and sensitive method for virus monitoring in wastewater than grab sampling, as the relative amount of the viruses present in composite samples was higher than in grab samples. Since we found abundant viruses in effluent, further studies are required to assess their infectivity and potential health risks as environmental pollutants.

Tobamovirus fructirugosum (ToBRFV) and Crinivirus tomatichlorosis (ToCV) are emerging viral threats to tomato production worldwide, with expanding global distribution. Both viruses exhibit distinct biological characteristics and transmission mechanisms that influence their spread. This study aimed to reconstruct the complete genomes of ToBRFV and ToCV from infected tomato plants and wastewater samples in Argentina to explore their global evolutionary dynamics. Additionally, it compared the genetic diversity of ToBRFV in plant tissue and sewage samples. Using metagenomic analysis, the complete genome sequences of two ToBRFV isolates and two ToCV isolates from co-infected tomatoes, along with four ToBRFV isolates from sewage, were obtained. The analysis showed that ToBRFV exhibited higher genetic diversity in environmental samples than in plant samples. Phylodynamic analysis indicated that both viruses had a recent, single introduction in Argentina but predicted different times for ancestral diversification. The evolutionary analysis estimated that ToBRFV began its global diversification in June 2013 in Israel, with rapid diversification and exponential growth until 2020, after which the effective population size declined. Moreover, ToCV's global expansion was characterized by exponential growth from 1979 to 2010, with Turkey identified as the most probable location with the current data available. This study highlights how sequencing and monitoring plant viruses can enhance our understanding of their global spread and impact on agriculture.

During COVID-19, surveillance of SARS-CoV-2 in wastewater has been a promising tool for tracking viral infection at the community level. However, in addition to the shedding rates within the community, SARS-CoV-2 concentrations in raw wastewater are influenced by several environmental factors. This study investigated the effects of wastewater characteristics on the viral quantification of SARS-CoV-2 and pepper mild mottle virus (PMMoV) for a large wastewater system with combined sewers. Principal component analysis illustrated that water temperature negatively correlates with SARS-CoV-2 and PMMoV in wastewater, but flow rate and EC are highly correlated with SARS-CoV-2 in spring and winter. The normalization using EC enhanced the correlation with clinical data compared to normalization using pH, flow rate, and raw SARS-CoV-2. The normalization using PMMoV reduced the correlation with clinical data. Multiple linear and random forest (RF) applied to predict the concentrations of SARS-CoV-2 in wastewater, given the confirmed cases and physicochemical parameters. RF regression was the best model to predict SARS-CoV-2 in wastewater (R2=0.8), with the most important variables being the confirmed cases followed by water temperature. RF model is a potent predictor of the presence of SARS-CoV-2 in wastewater. This enhances the degree of reliability between community outbreaks and SARS-CoV-2 monitoring.

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

Wastewater-based epidemiology has emerged as a complementary tool for the monitoring of COVID-19 pandemic waves and for the circulation of viral variants. The selection, standardization, and dynamics of different SARS-CoV-2 RNA targets in wastewater requires further investigation. In the present study, 106 wastewater samples were collected over a 24-month period from the wastewater treatment plant of Sondrio, north Italy, and were analyzed for the presence of SARS-CoV-2 RNA through the quantification of ORF1b, N1, and N3 gene targets via one-step real-time qPCR. In general, the three RNA targets demonstrated different performances and dynamics over the studied time period, underlying the usefulness of multiple viral targets in the surveillance of SARS-CoV-2 in wastewater. During the first 12 months, the quantification of the selected SARS-CoV-2 viral targets also correlated with the reported clinical cases in the same geographical area; however, from the overall data analysis this did not appear to significantly anticipate the epidemic waves. In conclusion, this study further supports the use of wastewater surveillance as a real time indicator of the human circulation of SARS-CoV-2. Moreover, the use of multiple viral gene targets has been shown to improve the reliability of SARS-CoV-2 surveillance in wastewater over time.

Since the start of the coronavirus-19 pandemic, the use of wastewater-based epidemiology (WBE) for disease surveillance has increased throughout the world. Because wastewater measurements are affected by external factors, processing WBE data typically includes a normalization step in order to adjust wastewater measurements (e.g., viral ribonucleic acid (RNA) concentrations) to account for variation due to dynamic population changes, sewer travel effects, or laboratory methods. Pepper mild mottle virus (PMMoV), a plant RNA virus abundant in human feces and wastewater, has been used as a fecal contamination indicator and has been used to normalize wastewater measurements extensively. However, there has been little work to characterize the spatiotemporal variability of PMMoV in wastewater, which may influence the effectiveness of PMMoV for adjusting or normalizing WBE measurements. Here, we investigate its variability across space and time using data collected over a two-year period from sewage treatment plants across the United States. We find that most variation in PMMoV measurements can be attributed to longitude and latitude followed by site-specific variables. Further research into cross-geographical and -temporal comparability of PMMoV-normalized pathogen concentrations would strengthen the utility of PMMoV in WBE.

The genomic monitoring of SARS-CoV-2 variants of concern (VOCs) in riverine and sewage water has been widely used as an epidemiological tool worldwide. But its utility for epidemiological assessments still needs to be evaluated in some areas. Our study encompassed thirteen Brazilian rivers spanning a vast urban expanse across the states of Rio de Janeiro, São Paulo, and Paraná. The sampled rivers in Rio de Janeiro are heavily contaminated with sewage. Meanwhile, the Indian samples were all wastewater before joining the water bodies from urban regions (Andhra Pradesh and Telangana). The viral copies were quantified using quantitative polymerase chain reaction (qPCR) in all examined samples (N = 91). The abundance of viral particles varied from 567 to 85,700,000 copies/ml. Subsequently, Illumina CovidSeq was applied to identify the major variants. In Brazil, while a single SARS-CoV-2 VOC was identified for just a few samples (6/50, 12 %), most samples harbored multiple VOCs (44/50, 88 %). In India only one probed sample had a single variant identified. Gamma (2021) and Omicron (2021 and 2022) were the most abundant variants. Delta and Omicron genetic material were detected in Rio de Janeiro city rivers before Brazil's first cases of these variants. Several negative samples in the Real-Time RT-PCR (qPCR) turned out to have SARS-CoV-2 sequences suggesting CovidSeq was more sensitive than RT-PCR for virus detection in environmental samples. Sewage surveillance holds promise for early detection of emerging variants driving pandemic waves, exemplified by the Delta and Omicron variants, potentially offering a preemptive advantage over clinical sample reports.

Wastewater has emerged as a crucial tool for infectious disease surveillance, offering a valuable means to bridge the equity gap between underserved communities and larger urban municipalities. However, using wastewater surveillance in a predictive manner remains a challenge. In this study, we tested if detecting SARS-CoV-2 in wastewater can forecast outbreaks in rural communities. Under the CDC National Wastewater Surveillance program, we monitored the SARS-CoV-2 in the wastewater of five rural communities and a small city in Idaho (USA). We then used a particle filter method coupled with a stochastic susceptible-exposed-infectious-recovered (SEIR) model to infer active case numbers using quantities of SARS-CoV-2 in wastewater. Our findings revealed that while high daily variations in wastewater viral load made real-time interpretation difficult, the SEIR model successfully factored out this noise, enabling accurate forecasts of the Omicron outbreak in five of the six towns shortly after initial increases in SARS-CoV-2 concentrations were detected in wastewater. The model predicted outbreaks with a lead time of 0 to 11 days (average of 6 days +/- 4) before the surge in reported clinical cases. This study not only underscores the viability of wastewater-based epidemiology (WBE) in rural communities-a demographic often overlooked in WBE research-but also demonstrates the potential of advanced epidemiological modeling to enhance the predictive power of wastewater data. Our work paves the way for more reliable and timely public health guidance, addressing a critical gap in the surveillance of infectious diseases in rural populations.

Wastewater-based epidemiology (WBE) is a recognized, dynamic approach to monitoring the transmission of pathogens in communities through urban wastewater. This study aimed to detect and quantify influenza A and B viruses in Italian wastewater during the 2022-2023 season (October 2022 to April 2023). A total of 298 wastewater samples were collected from 67 wastewater treatment plants (WTPs) across the country. These samples were analyzed for influenza A and B viruses (IAV, IBV) using primers originally developed by the Centers for Disease Control and Prevention (CDC) for real-time PCR and adapted for digital PCR. The overall detection rates of IAV and IBV across the entire study period were 19.1% and 16.8%, respectively. The prevalence of IAV in wastewater showed a gradual increase from October to December 2022, peaking at 61% in December. In contrast, IBV peaked at 36% in February 2023. This temporal discrepancy in peak concentrations suggests different seasonal patterns for the two influenza types. These trends mirrored human surveillance data, which showed influenza A cases peaking at 46% in late December and declining to around 2% by April 2023, and influenza B cases starting to increase significantly in January 2023 and peaking at about 14% in March. IAV concentrations ranged from 9.80 × 102 to 1.94 × 105 g.c./L, while IBV concentrations ranged from 1.07 × 103 to 1.43 × 104 g.c./L. Overall, the environmental data were consistent with the human surveillance trends observed during the study period in the country. These results demonstrate the value of WBE in tracking epidemiological patterns and highlight its potential as a complementary tool to infectious diseases surveillance systems.

The emergence of new SARS-CoV-2 variants poses challenges to global surveillance efforts, necessitating swift actions in their detection, evaluation, and management. Among the most recent variants, Omicron BA.2.86 and its sub-lineages have gained attention due to their potential immune evasion properties. This study describes the development of a digital PCR assay for the rapid detection of BA.2.86 and its descendant lineages, in wastewater samples. By using this assay, we analyzed wastewater samples collected in Italy from September 2023 to January 2024. Our analysis revealed the presence of BA.2.86 lineages already in October 2023 with a minimal detection rate of 2% which then rapidly increased, becoming dominant by January 2024, accounting for a prevalence of 62%. The findings emphasize the significance of wastewater-based surveillance in tracking emerging variants and underscore the efficacy of targeted digital PCR assays for environmental monitoring.

Emerging contaminants can be natural or synthetic materials, as well as materials of a chemical, or biological origin; these materials are typically not controlled or monitored in the environment. Canada is home to nearly 7 % of the world's renewable water supply and a wide range of different kinds of water systems, including the Great Lake, rivers, canals, gulfs, and estuaries. Although the majority of these pollutants are present in trace amounts (μg/L - ng/L concentrations), several studies have reported their detrimental impact on both human health and the biota. In Canadian aquatic environments, concentrations of pharmaceuticals (as high as 115 μg/L), pesticides (as high as 1.95 μg/L), bioavailable heavy metals like dissolved mercury (as high as 135 ng/L), and hydrocarbon/crude oil spills (as high as 4.5 million liters) have been documented. Biological threats such as genetic materials of the contagious SARS-CoV-2 virus have been reported in the provinces of Québec, Ontario, Saskatchewan and Manitoba provinces, as well as in the Nunavut territory, with a need for more holistic research. These toxins and emerging pollutants are associated with nefarious short and long-term health effects, with the potential for bioaccumulation in the environment. Hence, this Canadian-focused report provides the footprints for water and environmental sustainability, in light of this emerging threat to the environment and society. Several remediation pathways/tools that have been explored by Canadian researchers, existing challenges and prospects are also discussed. The review concludes with preventive measures and strategies for managing the inventory of emerging contaminants in the environment.

Background & objectives The global impact of COVID-19, with over 45 million cases and 533,300 deaths in India alone, necessitates effective surveillance methods. Traditional approaches face challenges in detecting pre-symptomatic and asymptomatic cases, prompting the exploration of wastewater-based epidemiology (WBE). This study focuses on Mumbai's vulnerable slums, aiming to assess the potential of WBE as an alternative surveillance method. Methods Genomic surveillance of SARS-CoV-2 was conducted in Mumbai's vulnerable settings (slums) for 11 months (August 2022 to June 2023). Wastewater samples from open drains and sewage treatment plants were correlated with reported COVID-19 cases in the city. Early detection of emerging viral variants and seasonal variations in viral load were explored. Results Correlations were identified between wastewater samples and reported COVID-19 cases in Mumbai's vulnerable slums, with early detection occurring three weeks before clinical diagnoses, underscoring the potential utility of WBE. Genomic sequencing provided insights into the viral variants, identifying shifts in predominant variants. Seasonal variations showed higher viral concentrations in summer and monsoon, potentially associated with accelerated droplet evaporation in early summer and droplet-based transmission during mid-summer and monsoon. Interpretation & conclusions Wastewater-based epidemiology emerges as a cost-effective and rapid early warning system, providing crucial insights into virus behaviour and evolution. Particularly significant for countries like India, WBE aids in outbreak monitoring and targeted interventions. The global integration of wastewater surveillance emphasizes its importance in comprehensive pandemic monitoring, establishing it as an integral component of public health strategies worldwide.

Substantial uncertainties pose challenges to the accuracy of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) quantification in wastewater. We conducted a comprehensive evaluation of two concentration methods, three nucleic acid extraction methods, and the amplification performance of eight primer-probe sets. Our results showed that the two concentration methods exhibited similar recovery rates. Specifically, using a 30 kDa cut-off ultrafilter and a centrifugal force of 2500 g achieved the highest virus recovery rates (27.32 ± 8.06 % and 26.37 ± 7.77 %, respectively), with lower corresponding quantification uncertainties of 29.51 % and 29.47 % in ultrafiltration methods. Similarly, a 15 % PEG concentration with 1.5 M NaCl markedly improved virus recovery (26.76 ± 5.92 % and 28.47 ± 6.74 %, respectively), and reducing variation to 22.16 % and 23.66 % in the PEG precipitation method. Additionally, employing a vigorous bead-beating approach at 6 m/s during viral RNA extraction significantly increased RNA yield, with an efficiency reaching up to 82.18 %. Among the evaluated eight primer-probe sets, the E_Sarbeco primer-probe set provided the most stable and consistent quantitative results across various sample matrices. These findings are crucial for establishing robust viral quantification protocols and enhancing methodological precision for effective wastewater surveillance, enabling sensitive and precise detection of SARS-CoV-2.

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

Wastewater-based surveillance (WBS) is increasingly used for monitoring disease targets in wastewaters around the world. This study, performed in Ottawa, Canada, identifies a decrease in SARS-CoV-2 wastewater measurements during snowmelt-induced sewer flushing events. Observations first revealed a correlation between suppressed viral measurements and periods of increased sewage flowrates, air temperatures above 0 °C during winter months, and solids mass flux increases. These correlations suggest that high sewage flowrates from snowmelt events or intense precipitation events lead to the scouring of previously settled solids in sewers and the subsequent entrainment of these solids into the transported wastewaters. Collection of WBS samples during flushing events hence contains a heterogeneous mixture of solids, including resuspended solids with varying degrees of decay. Therefore flushing events can present a challenge for accurately measuring disease target viral signals when using solids-based analytical methods. This study demonstrates that resuspended solids entrained in the wastewaters during flushing events retain PMMoV signal while the SARS-CoV-2 signal is significantly reduced due to the slower decay rate of pepper mild mottle virus (PMMoV) compared to SARS-CoV-2 within wastewaters. Hence current normalization methods using PMMoV are shown to be ineffective in correcting for flushing events and the associated resuspension of settled solids, as the PMMoV signal of settled solids within sewers does not account for the differential decay rates experiences by SARS-CoV-2 signal in settled solids. Instead, this study identifies RNA to PMMoV correction factor as an effective approach to correct for flushing events and to realign SARS-CoV-2 signal with COVID-19 hospital admission rates within communities. As such, the study highlights the key physicochemical parameters necessary to identify flushing events that affect SARS-CoV-2 WBS measurements and introduces a novel RNA to PMMoV correction factor approach for solids-based analysis of SARS-CoV-2 during flushing events, enhancing the accuracy of WBS data for public health decision-making.

Severe acute respiratory syndrome coronaviruses 2 (SARS-CoV-2) causing coronavirus disease 2019 (COVID-19) affected the health of human beings and the global economy. The patients with SARS-CoV-2 infection had viral RNA or live infectious viruses in feces. Thus, the possible transmission of SARS-CoV-2 through wastewater received great attentions. Moreover, SARS-CoV-2 in wastewater can serve as an early indicator of the infection within communities. We summarized the preconcentration and detection technology of SARS-CoV-2 in wastewater aiming at the complex matrices of wastewater and low virus concentration and compared their performance characteristics. We described the emerging tests that would be possible to realize the rapid detection of SARS-CoV-2 in fields and encourage academics to advance their technologies beyond conception. We concluded with a brief discussion on the outlook for integrating preconcentration and the detection of SARS-CoV-2 with emerging technologies.

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.

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.

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.

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.

During the recent COVID-19 pandemic, wastewater-based epidemiological (WBE) surveillance played a crucial role in evaluating infection rates, analyzing variants, and identifying hot spots in a community. This expanded the possibilities for using wastewater to monitor the prevalence of infectious diseases. The full potential of WBE remains hindered by several factors, such as a lack of information on the survival of pathogens in sewage, heterogenicity of wastewater matrices, inconsistent sampling practices, lack of standard test methods, and variable sensitivity of analytical techniques. In this study, we review the aforementioned challenges, cost implications, process automation, and prospects of WBE for full-fledged wastewater-based community health screening. A comprehensive literature survey was conducted using relevant keywords, and peer reviewed articles pertinent to our research focus were selected for this review with the aim of serving as a reference for research related to wastewater monitoring for early epidemic detection.

The COVID-19 pandemic underscored the significance of omics technology and Wastewater-Based Epidemiology for epidemic preparedness. This study investigates the virosphere in wastewater samples from Natal (Brazil), aiming to understand its structure, relationships, and potential. Metaviromic analysis was used on DNA and RNA from weekly samples collected over a year (June/2021 to May/2022) from three wastewater treatment plants. The virosphere showed stability, particularly in viruses infecting microorganisms and plants. However, an alternation of representatives of viruses that infect animals has been observed. Among the most abundant viruses infecting microorganisms are genera associated with the bacterial genera Escherichia, Pseudomonas, and Caulobacte. Regarding the viruses infecting plants, Sobemovirus and Tobamovirus are the most abundant genera. Odontoglossum ringspot virus was identified as a possible RNA virus biomarker. Among DNA viruses infecting animals, genera Bocaparvovirus and Mastadenovirus are the most prevalent. Intriguingly, some Poxviridae family members were observed in the samples. Co-occurrence network analysis identified potential biomarkers like Volepox virus, Anatid herpesvirus 1, and Caviid herpesvirus 2. Among RNA viruses affecting animals, Mamastrovirus, Rotavirus, and Norovirus genera were the most abundant pathogens. Furthermore, members of the Coronaviridae family exhibited a high degree of centrality values in the co-occurrence network, even connecting with unclassified viruses. The study emphasizes the importance of research in understanding the roles of unclassified viruses. In addition, we observed an association between Coronaviridae reads, rainfall, and the number of reported COVID-19 cases. Our study highlights the diversity and complexity of the viral community in wastewater and the need for research to understand better the ecological roles unclassified viruses play. Such advances will significantly contribute to our preparedness and response to future viral threats. Furthermore, our study contributes to knowledge of virosphere dynamics, offering insights that can contribute to the direction of future public health policies and interventions.

During the COVID-19 pandemic, wastewater surveillance was used to monitor community transmission of SARS-CoV-2. As new genetic variants emerged, the need for timely identification of these variants in wastewater became an important focus. In response to increased reports of Omicron transmission across the United States, the Oklahoma Wastewater Surveillance team utilized allele-specific RT-qPCR assays to detect and differentiate variants, such as Omicron, from other variants found in wastewater in Oklahoma. The PCR assays showed presence of the Omicron variant in Oklahoma on average two weeks before official reports, which was confirmed through genomic sequencing of selected wastewater samples. Through continued surveillance from November 2021 to January 2022, we also demonstrated the transition from prevalence of the Delta variant to prevalence of the Omicron variant in local communities. We further assessed how this transition correlated with certain demographic factors characterizing each community. Our results highlight RT-qPCR assays as a rapid, simple, and cost-effective method for monitoring the community spread of SARS-CoV-2 genetic variants in wastewater. Additionally, they demonstrate that specific demographic factors such as ethnic composition and household income can correlate with the timing of SARS-CoV-2 variant introduction and spread.

Wastewater surveillance is crucial for the epidemiological monitoring of SARS-CoV-2. Various concentration techniques, such as skimmed milk flocculation (SMF) and polyethylene glycol (PEG) precipitation, are employed to isolate the virus effectively. This study aims to compare these two methods and determine the one with the superior recovery rates. From February to December 2021, 24-h wastewater samples were collected from the Ioannina Wastewater Treatment Plant's inlet and processed using both techniques. Subsequent viral genome isolation and a real-time RT-qPCR detection of SARS-CoV-2 were performed. The quantitative analysis demonstrated a higher detection sensitivity with a PEG-based concentration than SMF. Moreover, when the samples were positive by both methods, PEG consistently yielded higher viral loads. These findings underscore the need for further research into concentration methodologies and the development of precise protocols to enhance epidemiological surveillance through wastewater analysis.

The global SARS-CoV-2 monitoring effort has been extensive, resulting in many states and countries establishing wastewater-based epidemiology programs to address the spread of the virus during the pandemic. Challenges for programs include concurrently optimizing methods, training new laboratories, and implementing successful surveillance programs that can rapidly translate results for public health, and policy making. Surveillance in Michigan early in the pandemic in 2020 highlights the importance of quality-controlled data and explores correlations with wastewater and clinical case data aggregated at the state level. The lessons learned and potential measures to improve public utilization of results are discussed. The Michigan Network for Environmental Health and Technology (MiNET) established a network of laboratories that partnered with local health departments, universities, wastewater treatment plants (WWTPs) and other stakeholders to monitor SARS-CoV-2 in wastewater at 214 sites in Michigan. MiNET consisted of nineteen laboratories, twenty-nine local health departments, 6 Native American tribes, and 60 WWTPs monitoring sites representing 45 % of Michigan's population from April 6 and December 29, 2020. Three result datasets were created based on quality control criteria. Wastewater results that met all quality assurance criteria (Dataset Mp) produced strongest correlations with reported clinical cases at 16 days lag (rho = 0.866, p < 0.05). The project demonstrated the ability to successfully track SARS-CoV-2 on a large, state-wide scale, particularly data that met the outlined quality criteria and provided an early warning of increasing COVID-19 cases. MiNET is currently poised to leverage its competency to complement public health surveillance networks through environmental monitoring for new and emerging pathogens of concern and provides a valuable resource to state and federal agencies to support future responses.

The future of the COVID pandemic and its public health and societal impact will be determined by the profile and spread of emerging variants and the timely identification and response to them. Wastewater surveillance of SARS-CoV-2 has been widely adopted in many countries across the globe and has played an important role in tracking infection levels and providing useful epidemiological information that cannot be adequately captured by clinical testing alone. However, novel variants can emerge rapidly, spread globally, and markedly alter the trajectory of the pandemic, as exemplified by the Delta and Omicron variants. Most mutations linked to the emergence of new SARS-CoV-2 variants are found within variable regions of the SARS-CoV-2 Spike protein. We have developed a duplex hemi-nested PCR method that, coupled with short amplicon sequencing, allows simultaneous typing of two of the most highly variable and informative regions of the Spike gene: the N-terminal domain and the receptor binding motif. Using this method in an operationalized public health program, we identified the first known incursion of Omicron BA.1 into Victoria, Australia and demonstrated how sensitive amplicon sequencing methods can be combined with wastewater surveillance as a relatively low-cost solution for early warning of variant incursion and spread.IMPORTANCEThis study offers a rapid, cost-effective, and sensitive approach for monitoring SARS-CoV-2 variants in wastewater. The method's flexibility permits timely modifications, enabling the integration of emerging variants and adaptations to evolving SARS-CoV-2 genetics. Of particular significance for low- and middle-income regions with limited surveillance capabilities, this technique can potentially be utilized to study a range of pathogens or viruses that possess diverse genetic sequences, similar to influenza.

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.

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.

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.

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.

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.

There are significant differences in the transmission rate and mortality rate of COVID-19 under environmental conditions such as seasons and climates. However, the impact of environmental factors on the role of the COVID-19 pandemic and the transmission mechanism of the SARS-CoV-2 is unclear. Therefore, a comprehensive understanding of the impact of environmental factors on COVID-19 can provide innovative insights for global epidemic prevention and control policies and COVID-19 related research. This review summarizes the evidence of the impact of different natural and social environmental factors on the transmission of COVID-19 through a comprehensive analysis of epidemiology and mechanism research. This will provide innovative inspiration for global epidemic prevention and control policies and provide reference for similar infectious diseases that may emerge in the future.

Evidence reveals mechanisms by which natural environmental factors influence the transmission of COVID-19, including (i) virus survival and transport, (ii) immune system damage, (iii) inflammation, oxidative stress, and cell death, and (iiii) increasing risk of complications. All of these measures appear to be effective in controlling the spread or mortality of COVID-19: (1) reducing air pollution levels, (2) rational use of ozone disinfection and medical ozone therapy, (3) rational exposure to sunlight, (4) scientific ventilation and maintenance of indoor temperature and humidity, (5) control of population density, and (6) control of population movement. Our review indicates that with the continuous mutation of SARS-CoV-2, high temperature, high humidity, low air pollution levels, and low population density more likely to slow down the spread of the virus.

Wastewater-based environmental surveillance (WBES) has been proven as proxy tool for monitoring nucleic acids of pathogens shed by infected population before clinical outcomes. The poor sewershed network of low to middle-income countries (LMICs) leads to most of the wastewater flow through open drains. We studied the effectiveness of WBES using open drain samples to monitor the emergence of the SARS-CoV-2 variants in 2 megacities of India having dense population through zonation approach. Samples from 28 locations spanned into 5 zones of Pune region, Maharashtra, India, were collected on a weekly basis during October 2021 to July 2022. Out of 1115 total processed samples, 303 (~ 27%) tested positive for SARS-CoV-2. The periodical rise and fall in the percentage positivity of the samples was found to be in sync with the abundance of SARS-CoV-2 RNA and the reported COVID-19 active cases for Pune city. Sequencing of the RNA obtained from wastewater samples confirmed the presence of SARS-CoV-2. Of 337 sequences, lineage identification for 242 samples revealed 265 distinct SARS-CoV-2 variants including 10 highly transmissible ones. Importantly, transition from Delta to Omicron variant could be detected in wastewater samples 2 weeks prior to any clinically reported Omicron cases in India. Thus, this study demonstrates the usefulness of open drain samples for real-time monitoring of a viral pathogen's evolutionary dynamics and could be implemented in LMICs.

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

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

How to address public health priorities after COVID-19 is becoming a critical task. To this end, we conducted wastewater surveillance for six leading pathogens, namely, SARS-CoV-2, norovirus, rotavirus, influenza A virus (IAV), enteroviruses and respiratory syncytial virus (RSV), in Nanchang city from January to April 2023. Metaviromic sequencing was conducted at the 1st, 4th, 7th, 9th, 12th and 14th weeks to reveal the dynamics of viral pathogens that were not covered by qPCR. Amplicon sequencing of the conserved region of norovirus GI and GII and the rotavirus and region encoding nonstructural protein of RSV was also conducted weekly. The results showed that after a rapid decrease in SARS-CoV-2 sewage concentrations occurred in January 2023, surges of norovirus, rotavirus, IAV and RSV started at the 6th, 7th, 8th and 11th weeks, respectively. The dynamics of the sewage concentrations of norovirus, rotavirus, IAV and RSV were consistent with the off-season resurgence of the above infectious diseases. Notably, peak sewage concentrations of norovirus GI, GII, rotavirus, IAV and RSV were found at the 6th, 3rd, 7th, 7th and 8th weeks, respectively. Astroviruses also resurge after the 7th week, as revealed by metaviromic data, suggesting that wastewater surveillance together with metaviromic data provides an essential early warning tool for revealing patterns of infectious disease resurgence.

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.

As the threat of COVID-19 recedes, wastewater surveillance - unlike other pandemic-era public health surveillance methods - seems here to stay. Concerns have been raised, however, about the potential risks that wastewater surveillance might pose towards group privacy. Existing scholarship has focused upon using ethics- or human rights-based frameworks as a means of balancing the public health objectives of wastewater surveillance and the potential risks it might pose to group privacy. However, such frameworks greatly lack enforceability. In order to further the strong foundation laid by such frameworks - while addressing their lack of enforceability - this paper proposes the idea of the 'obligation' as an alternative way to regulate wastewater surveillance systems. The legal codification of said obligations provides a method of ensuring that wastewater surveillance systems can be deployed effectively and equitably. Our paper proposes that legal obligations for wastewater surveillance can be created and enforced through transparent and purposeful legislation (which would include limits on power and grant institutions substantial oversight) as well as paying heed to non-legislative legal means of enforcement, such as through courts or contracts. Introducing legal obligations for wastewater surveillance could therefore be highly useful to researchers, policymakers, corporate technologists, and government agencies working in this field.

PMMoV has been widely used to normalize the concentration of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) RNA, influenza, and respiratory syncytial virus (RSV) to account for variations in the fecal content of wastewater. PMMoV is also used as an internal RNA recovery control for wastewater-based epidemiology (WBE) tests. While potentially useful for the interpretation of WBE data, previous studies have suggested that PMMoV concentration can be affected by various physico-chemical characteristics of wastewater. There is also the possibility that laboratory methods, particularly the variability in centrifugation steps to remove supernatant from pellets can cause PMMoV variability. The goal of this study is to improve our understanding of the main drivers of PMMoV variability by assessing the relationship between PMMoV concentration, the physico-chemical characteristics of wastewater, and the methodological approach for concentrating wastewater samples. We analyzed 24-hour composite wastewater samples collected from the influent stream of three wastewater treatment plants (WWTPs) located in the City of Toronto, Ontario, Canada. Samples were collected 3 to 5 times per week starting from the beginning of March 2021 to mid-July 2023. The influent flow rate was used to partition the data into wet and dry weather conditions. Physico-chemical characteristics (e.g., total suspended solids (TSS), biological oxygen demand (BOD), alkalinity, electrical conductivity (EC), and ammonia (NH3)) of the raw wastewater were measured, and PMMoV was quantified. Spatial and temporal variability of PMMoV was observed throughout the study period. PMMoV concentration was significantly higher during dry weather conditions. Multiple linear regression analysis demonstrates that the number and type of physico-chemical parameters that drive PMMoV variability are site-specific, but overall BOD and alkalinity were the most important predictors. Differences in PMMoV concentration for a single WWTP between two different laboratory methods, along with a weak correlation between pellet mass and TSS using one method may indicate that differences in sample concentration and subjective subsampling bias could alter viral recovery and introduce variability to the data.

Fungal diseases are often linked to poverty, which is associated with poor hygiene and sanitation conditions that have been severely worsened by the COVID-19 pandemic. Moreover, COVID-19 patients are treated with Dexamethasone, a corticosteroid that promotes an immunosuppressive profile, making patients more susceptible to opportunistic fungal infections, such as those caused by Candida species. In this study, we analyzed the prevalence of Candida yeasts in wastewater samples collected to track viral genetic material during the COVID-19 pandemic and identified the yeasts using polyphasic taxonomy. Furthermore, we investigated the production of biofilm and hydrolytic enzymes, which are known virulence factors. Our findings revealed that all Candida species could form biofilms and exhibited moderate hydrolytic enzyme activity. We also proposed a workflow for monitoring wastewater using Colony PCR instead of conventional PCR, as this technique is fast, cost-effective, and reliable. This approach enhances the accurate taxonomic identification of yeasts in environmental samples, contributing to environmental monitoring as part of the One Health approach, which preconizes the monitoring of possible emergent pathogenic microorganisms, including fungi.