Wastewater-based epidemiology has expanded as a tool for collecting COVID-19 surveillance data, but there is limited information on the feasibility of this form of surveillance within decentralized wastewater systems (e.g., septic systems). This study assessed SARS-CoV-2 RNA concentrations in wastewater samples from a septic system servicing a mobile home park (66 households) and from two pumping stations serving a similarly sized (71 households) and a larger (1,000 households) neighborhood within a nearby sewershed over 35 weeks in 2020. Also, raw wastewater from a hospital in the same sewershed was sampled. The mobile home park samples had the highest detection frequency (39/39 days) and mean concentration of SARS-CoV-2 RNA (2.7 × 107 gene copies/person/day for the N1) among the four sampling sites. N1 gene and N2 gene copies were highly correlated across mobile home park samples (Pearson\'s r = 0.93, p < 0.0001). In the larger neighborhood, new COVID-19 cases were reported every week during the sampling period; however, we detected SARS-CoV-2 RNA in 12% of the corresponding wastewater samples. The results of this study suggest that sampling from decentralized wastewater infrastructure can be used for continuous monitoring of SARS-CoV-2 infections.

The study investigated the application of Wastewater-Based Epidemiology (WBE) as a tool for monitoring the SARS-CoV-2 prevalence in a city in northern Italy from October 2021 to May 2023. Based on a previously used deterministic model, this study proposed a variation to account for the population characteristics and virus biodegradation in the sewer network. The model calculated virus loads and corresponding COVID-19 cases over time in different areas of the city and was validated using healthcare data while considering viral mutations, vaccinations, and testing variability. The correlation between the predicted and reported cases was high across the three waves that occurred during the period considered, demonstrating the ability of the model to predict the relevant fluctuations in the number of cases. The population characteristics did not substantially influence the predicted and reported infection rates. Conversely, biodegradation significantly reduced the virus load reaching the wastewater treatment plant, resulting in a 30% reduction in the total virus load produced in the study area. This approach can be applied to compare the virus load values across cities with different population demographics and sewer network structures, improving the comparability of the WBE data for effective surveillance and intervention strategies.

Wastewater based epidemiology has become a widely used tool for monitoring trends of concentrations of different pathogens, most notably and widespread of SARS-CoV-2. Therefore, in 2022, also in Rhineland-Palatinate, the Ministry of Science and Health has included 16 wastewater treatment sites in a surveillance program providing biweekly samples. However, the mere viral load data is subject to strong fluctuations and has limited value for political deciders on its own. Therefore, the state of Rhineland-Palatinate has commissioned the University Medical Center at Johannes Gutenberg University Mainz to conduct a representative cohort study called SentiSurv, in which an increasing number of up to 12,000 participants have been using sensitive antigen self-tests once or twice a week to test themselves for SARS-CoV-2 and report their status. This puts the state of Rhineland-Palatinate in the fortunate position of having time series of both, the viral load in wastewater and the prevalence of SARS-CoV-2 in the population. Our main contribution is a calibration study based on the data from 2023-01-08 until 2023-10-01 where we identified a scaling factor ( 0.208 ± 0.031 ) and a delay ( 5.07 ± 2.30 days) between the virus load in wastewater, normalized by the pepper mild mottle virus (PMMoV), and the prevalence recorded in the SentiSurv study. The relation is established by fitting an epidemiological model to both time series. We show how that can be used to estimate the prevalence when the cohort data is no longer available and how to use it as a forecasting instrument several weeks ahead of time. We show that the calibration and forecasting quality and the resulting factors depend strongly on how wastewater samples are normalized.

The \'recreational use\' of selected over-the-counter (OTC) medicines is an unofficial activity. The traditional surveys assessing the use of drugs are affected by the bias of underreporting and are thus unreliable. The development of analytical techniques helps to monitor the substances at trace levels, such as in wastewater, and might be applied to estimate the consumption of an analyte of interest and ensure additional, evidence-based information complementary to population surveys. We reviewed studies focused on evaluating the estimated consumption of drugs as a reliable and unbiased source of evidence-based information (called wastewater-based epidemiology, WBE) to monitor the scale of this phenomenon. We found there is a need to test not only narcotics in the environment but also medicines that may be abused or recreationally used. The reviewed studies show methods that might provide reliable information about consumption of drugs, narcotics, and OTC medications for proposing targeted, preventive actions. Moreover, as all the selected studies were based on mass spectrometry, there is a potential to include the dextromethorphan and/or related compounds as part of the screening for narcotics and OTC drugs that can be socially harmful, overused, or misused. This article reviews the analytical methods for detecting dextromethorphan and/or its transformation products in environmental water samples.

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The determination of illicit drugs in urban influent wastewater (IWW) enables the monitoring of spatial and temporal drug usage trends and assessment of community lifestyle habits. The increasing number of wastewater surveillance studies has emphasized the necessity for the development of rapid, high-throughput methods that maintain high quality data. This work evaluates the use of a dilute-and-shoot methodology, based on direct injection (DI) of centrifuged samples, as an alternative approach to the widely applied sample pre-treatment based on solid-phase extraction, for the liquid chromatography-tandem mass spectrometry determination of seven widely consumed illicit drugs and their metabolites in IWW (amphetamine; cocaine metabolite, benzoylecgonine; ketamine; 3,4-methylenedioxymethamphetamine (MDMA); methamphetamine; cannabis metabolite, 11-nor-9-carboxy-delta-9-tetrahydrocannabinol (THCCOOH); heroin metabolite, 6-acetylmorphine (6-MAM)). Comparison of both approaches in terms of matrix effects, sensitivity and accuracy, demonstrates the DI method suitability to correctly quantify these analytes in IWW, with a limit of quantification lower than 30 ng L-1 for most compounds. After validation of the method and participation in an interlaboratory exercise, the DI method was applied to the analysis of 54 IWW samples collected from different Spanish wastewater treatment plants. Additionally, quality controls were incorporated in each analysis batch to support the DI method applicability and robustness. The use of a 10 μL-DI reduces time-consuming sample preparation, analysis time and measurement uncertainty. Moreover, it supports green chemistry by reducing the consumption of organic solvents and it facilitates logistics by collecting, transporting, and storing less sample volume. The methodology is therefore especially appropriate for monitoring illicit drugs in large wastewater-based epidemiology sampling campaigns or when fast near real-time results are needed.

Wastewater surveillance has emerged as a crucial public health tool for population-level pathogen surveillance. Supported by funding from the American Rescue Plan Act of 2021, the FDA\'s genomic epidemiology program, GenomeTrakr, was leveraged to sequence SARS-CoV-2 from wastewater sites across the United States. This initiative required the evaluation, optimization, development, and publication of new methods and analytical tools spanning sample collection through variant analyses. Version-controlled protocols for each step of the process were developed and published on protocols.io. A custom data analysis tool and a publicly accessible dashboard were built to facilitate real-time visualization of the collected data, focusing on the relative abundance of SARS-CoV-2 variants and sub-lineages across different samples and sites throughout the project. From September 2021 through June 2023, a total of 3,389 wastewater samples were collected, with 2,517 undergoing sequencing and submission to NCBI under the umbrella BioProject, PRJNA757291. Sequence data were released with explicit quality control (QC) tags on all sequence records, communicating our confidence in the quality of data. Variant analysis revealed wide circulation of Delta in the fall of 2021 and captured the sweep of Omicron and subsequent diversification of this lineage through the end of the sampling period. This project successfully achieved two important goals for the FDA\'s GenomeTrakr program: first, contributing timely genomic data for the SARS-CoV-2 pandemic response, and second, establishing both capacity and best practices for culture-independent, population-level environmental surveillance for other pathogens of interest to the FDA.
OBJECTIVE: This paper serves two primary objectives. First, it summarizes the genomic and contextual data collected during a Covid-19 pandemic response project, which utilized the FDA\'s laboratory network, traditionally employed for sequencing foodborne pathogens, for sequencing SARS-CoV-2 from wastewater samples. Second, it outlines best practices for gathering and organizing population-level next generation sequencing (NGS) data collected for culture-free, surveillance of pathogens sourced from environmental samples.

Wastewater-Based Epidemiology (WBE) is widely used to monitor the progression of SARS-CoV-2 pandemic. While there is a clear correlation between the number of COVID patients in a sewershed and the viral load in the wastewater, there is notable variability across different treatment plants. In particular, some facilities consistently exhibit higher viral content per diagnosed patient, implying a potential underestimation of the number of COVID patients, while others show a low viral load per diagnosed case, indicating potential attenuation of genetic material from the sewershed. In this study, we investigated the impact of nonylphenol ethoxylate (NPHE), linear alkylbenzene sulfonic acid (LABS), bisoctyl dimethyl ammonium chloride (BDAC), and didecyldimethylammonium chloride (DDAC), the surfactants that have been commonly used as detergents, emulsifiers, wetting agents on the stability of SARS-CoV-2 in wastewater. The results showed multiple and dynamic mechanisms, including degradation and desorption, can occur simultaneously during the interaction between SARS-CoV-2 and different chemicals depending on the physicochemical properties of each chemical. Through the elucidation of the dynamic interactions, the findings from this study could help the state health organizations and scientific community to optimize the SARS-CoV-2 wastewater-based epidemiology strategies.

Wastewater-based epidemiology (WBE) has emerged as an effective environmental surveillance tool for predicting severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) disease outbreaks in high-income countries (HICs) with centralized sewage infrastructure. However, few studies have applied WBE alongside epidemic disease modelling to estimate the prevalence of SARS-CoV-2 in low-resource settings. This study aimed to explore the feasibility of collecting untreated wastewater samples from rural and urban catchment areas of Nagpur district, to detect and quantify SARS-CoV-2 using real-time qPCR, to compare geographic differences in viral loads, and to integrate the wastewater data into a modified Susceptible-Exposed-Infectious-Confirmed Positives-Recovered (SEIPR) model. Of the 983 wastewater samples analyzed for SARS-CoV-2 RNA, we detected significantly higher sample positivity rates, 43.7% (95% confidence interval (CI) 40.1, 47.4) and 30.4% (95% CI 24.66, 36.66), and higher viral loads for the urban compared with rural samples, respectively. The Basic reproductive number, R0, positively correlated with population density and negatively correlated with humidity, a proxy for rainfall and dilution of waste in the sewers. The SEIPR model estimated the rate of unreported coronavirus disease 2019 (COVID-19) cases at the start of the wave as 13.97 [95% CI (10.17, 17.0)] times that of confirmed cases, representing a material difference in cases and healthcare resource burden. Wastewater surveillance might prove to be a more reliable way to prepare for surges in COVID-19 cases during future waves for authorities.

Wastewater-based epidemiological surveillance at municipal wastewater treatment plants has proven to play an important role in COVID-19 surveillance. Considering international passenger hubs contribute extensively to global transmission of viruses, wastewater surveillance at this type of location may be of added value as well. The aim of this study is to explore the potential of long-term wastewater surveillance at a large passenger hub as an additional tool for public health surveillance during different stages of a pandemic. Here, we present an analysis of SARS-CoV-2 viral loads in airport wastewater by reverse-transcription quantitative polymerase chain reaction (RT-qPCR) from the beginning of the COVID-19 pandemic in Feb 2020, and an analysis of SARS-CoV-2 variants by whole-genome next-generation sequencing from Sep 2020, both until Sep 2022, in the Netherlands. Results are contextualized using (inter)national measures and data sources such as passenger numbers, clinical surveillance data and national wastewater surveillance data. Our findings show that wastewater surveillance was possible throughout the study period, irrespective of measures, as viral loads were detected and quantified in 98.6 % (273/277) of samples. Emergence of SARS-CoV-2 variants, identified in 91.0 % (161/177) of sequenced samples, coincided with increases in viral loads. Furthermore, trends in viral load and variant detection in airport wastewater closely followed, and in some cases preceded, trends in national daily average viral load in wastewater and variants detected in clinical surveillance. Wastewater-based epidemiology at a large international airport is a valuable addition to classical COVID-19 surveillance and the developed expertise can be applied in pandemic preparedness plans for other (emerging) pathogens in the future.