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

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

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

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

Population infection estimation from wastewater surveillance for SARS-CoV-2 in Nagpur, India during the second pandemic wave.

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.

Seasonality of respiratory, enteric, and urinary viruses revealed by wastewater genomic surveillance.

Wastewater surveillance can reveal population-level infectious disease burden and emergent public health threats can be reliably assessed through wastewater surveillance. While molecular methods for wastewater monitoring of microorganisms have traditionally relied on PCR-based approaches, next-generation sequencing (NGS) can provide deeper insights via genomic analyses of multiple diverse pathogens. We conducted a year-long sequencing surveillance of 1,408 composite wastewater samples collected from 12 neighborhood-level access points in the greater Tempe area, Arizona, USA, and show that variation in wastewater viruses is driven by seasonal time and location. The temporal dynamics of viruses in wastewater were influenced cyclically, with the most dissimilarity between samples 23 weeks apart (i.e., winter vs summer, spring vs fall). We identified diverse urinary and enteric viruses including polyomaviruses, astroviruses, and noroviruses, and showed that their genotypes/subtypes shifted across seasons. We show that while wastewater data of certain respiratory viruses like severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) strongly correlate with clinical case rates, laboratory-reported case incidences were discordant with surges of high viral load in wastewater for other viruses like human coronavirus 229E. These results demonstrate the utility of wastewater sequencing for informing decision-making in public health.IMPORTANCEWastewater surveillance can provide insights into the spread of pathogens in communities. Advances in next-generation sequencing (NGS) methodologies allow for more precise detection of viruses in wastewater. Long-term wastewater surveillance of viruses is an important tool for public health preparedness. This system can act as a public health observatory that gives real-time early warning for infectious disease outbreaks and improved response times.

Analytical validation of a semi-automated methodology for quantitative measurement of SARS-CoV-2 RNA in wastewater collected in northern New England.

Wastewater-based epidemiology (WBE) can be used to monitor the community presence of infectious disease pathogens of public health concern such as the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Viral nucleic acid has been detected in the stool of SARS-CoV-2-infected individuals. Asymptomatic SARS-CoV-2 infections make community monitoring difficult without extensive and continuous population screening. In this study, we validated a procedure that includes manual pre-processing, automated SARS-CoV-2 RNA extraction and detection workflows using both reverse-transcriptase quantitative polymerase chain reaction (RT-qPCR) and reverse transcriptase droplet digital PCR (RT-ddPCR). Genomic RNA and calibration materials were used to create known concentrations of viral material to determine the linearity, accuracy, and precision of the wastewater extraction and SARS-CoV-2 RNA detection. Both RT-qPCR and RT-ddPCR perform similarly in all the validation experiments, with a limit of detection of 50 copies/mL. A wastewater sample from a care facility with a known outbreak was assessed for viral content in replicate, and we showed consistent results across both assays. Finally, in a 2-week survey of two New Hampshire cities, we assessed the suitability of our methods for daily surveillance. This paper describes the technical validation of a molecular assay that can be used for long-term monitoring of SARS-CoV-2 in wastewater as a potential tool for community surveillance to assist with public health efforts.IMPORTANCEThis paper describes the technical validation of a molecular assay that can be used for the long-term monitoring of SARS-CoV-2 in wastewater as a potential tool for community surveillance to assist with public health efforts.

Association between the number of symptomatic mpox cases and the detection of mpox virus DNA in wastewater in Switzerland: an observational surveillance study.

AIM OF THE STUDY: The COVID-19 pandemic has drawn attention to the benefit of wastewater-based epidemiology, particularly when case numbers are underreported. Underreporting may be an issue with mpox, where biological reasons and stigma may prevent patients from getting tested. Therefore, we aimed to assess the validity of wastewater surveillance for monitoring mpox virus DNA in wastewater of a Central European city and its association with official case numbers. METHODS: Wastewater samples were collected between 1 July and 28 August 2022 in the catchment area of Basel, Switzerland, and the number of mpox virus genome copies they contained was determined by real-time quantitative PCR. Logistic regression analyses were used to determine the odds of detectability of mpox virus DNA in wastewater, categorised as detectable or undetectable. Mann-Whitney U tests were used to determine associations between samples that tested positive for the mpox virus and officially reported cases and patients' recorded symptomatic phases. RESULTS: Mpox virus DNA was detected in 15 of 39 wastewater samples. The number of positive wastewater samples was associated with the number of symptomatic cases (odds ratio [OR] = 2.18, 95% confidence interval (CI) = 1.38-3.43, p = 0.001). The number of symptomatic cases differed significantly between days with positive versus negative wastewater results (median = 11 and 8, respectively, p = 0.0024). CONCLUSION: Mpox virus DNA was detectable in wastewater, even when officially reported case numbers were low (0-3 newly reported mpox cases corresponding to 6-12 symptomatic patients). Detectability in wastewater was significantly associated with the number of symptomatic patients within the catchment area. These findings illustrate the value of wastewater-based surveillance systems when assessing the prevalence of emerging and circulating infectious diseases.

Wastewater based epidemiology as a surveillance tool during the current COVID-19 pandemic on a college campus (East Carolina University) and its accuracy in predicting SARS-CoV-2 outbreaks in dormitories.

The COVID-19 outbreak led governmental officials to close many businesses and schools, including colleges and universities. Thus, the ability to resume normal campus operation required adoption of safety measures to monitor and respond to COVID-19. The objective of this study was to determine the efficacy of wastewater-based epidemiology as a surveillance method in monitoring COVID-19 on a college campus. The use of wastewater monitoring as part of a surveillance program to control COVID-19 outbreaks at East Carolina University was evaluated. During the Spring and Fall 2021 semesters, wastewater samples (N = 830) were collected every Monday, Wednesday, and Friday from the sewer pipes exiting the dormitories on campus. Samples were analyzed for SARS-CoV-2 and viral quantification was determined using qRT-PCR. During the Spring 2021 semester, there was a significant difference in SARS-CoV-2 virus copies in wastewater when comparing dorms with the highest number student cases of COVID-19 and those with the lowest number of student cases, (p = 0.002). Additionally, during the Fall 2021 semester it was observed that when weekly virus concentrations exceeded 20 copies per ml, there were new confirmed COVID-19 cases 85% of the time during the following week. Increases in wastewater viral concentration spurred COVID-19 swab testing for students residing in dormitories, aiding university officials in effectively applying COVID testing policies. This study showed wastewater-based epidemiology can be a cost-effective surveillance tool to guide other surveilling methods (e.g., contact tracing, nasal/salvia testing, etc.) to identify and isolate afflicted individuals to reduce the spread of pathogens and potential outbreaks within a community.

Group A Streptococcus pyogenes in wastewater: Applicability of wastewater-based epidemiology for monitoring the prevalence of GAS pharyngitis during the late COVID-19 pandemic phase.

Streptococcus pyogenes, Group A Streptococcus (GAS), is a human pathogen that causes a spectrum of diseases from mild to severe, including GAS pharyngitis, a common acute respiratory disease in developed countries. Although wastewater-based epidemiology (WBE) has been extensively used to monitor viral pathogens such as severe acute respiratory syndrome coronavirus 2, its applicability to S. pyogenes remains unexplored. This study was conducted to investigate the feasibility of detecting and quantifying S. pyogenes in wastewater by quantitative polymerase chain reaction (qPCR) and evaluate the applicability of WBE for monitoring the prevalence of GAS pharyngitis. A total of 52 grab influent samples were collected from a wastewater treatment plant in Japan once a week between March 2023 and February 2024. The samples were centrifuged, followed by nucleic acid extraction and qPCR for the S. pyogenes-specific genes speB and spy1258. Of the 52 samples, 90 % and 81 % were positive for speB and spy1258 genes, respectively, indicating the feasibility of S. pyogenes for wastewater surveillance. However, the percentage of quantifiable samples for speB gene was significantly higher in winter than in spring and summer. Similarly, the concentrations of both genes in wastewater samples were significantly higher in winter (speB, 4.1 ± 0.27 log10 copies/L; spy1258, 4.1 ± 0.28 log10 copies/L; One-way ANOVA, p < 0.01) than in spring and summer. Higher concentrations and detection ratios of S. pyogenes genes were observed during increased GAS pharyngitis cases in the catchment. Significant moderate correlations were observed between target gene concentrations and reported GAS pharyngitis cases. This study enhances the understanding role of WBE in monitoring and managing infectious diseases within communities.

Small-scale wastewater-based epidemiology (WBE) for infectious diseases and antibiotic resistance: A scoping review.

Wastewater analysis can serve as a source of public health information. In recent years, wastewater-based epidemiology (WBE) has emerged and proven useful for the detection of infectious diseases. However, insights from the wastewater treatment plant do not allow for the small-scale differentiation within the sewer system that is needed to analyze the target population under study in more detail. Small-scale WBE offers several advantages, but there has been no systematic overview of its application. The aim of this scoping review is to provide a comprehensive overview of the current state of knowledge on small-scale WBE for infectious diseases, including methodological considerations for its application. A systematic database search was conducted, considering only peer-reviewed articles. Data analyses included quantitative summary and qualitative narrative synthesis. Of 2130 articles, we included 278, most of which were published since 2020. The studies analyzed wastewater at the building level (n = 203), especially healthcare (n = 110) and educational facilities (n = 80), and at the neighborhood scale (n = 86). The main analytical parameters were viruses (n = 178), notably SARS-CoV-2 (n = 161), and antibiotic resistance (ABR) biomarkers (n = 99), often analyzed by polymerase chain reaction (PCR), with DNA sequencing techniques being less common. In terms of sampling techniques, active sampling dominated. The frequent lack of detailed information on the specification of selection criteria and the characterization of the small-scale sampling sites was identified as a concern. In conclusion, based on the large number of studies, we identified several methodological considerations and overarching strategic aspects for small-scale WBE. An enabling environment for small-scale WBE requires inter- and transdisciplinary knowledge sharing across countries. Promoting the adoption of small-scale WBE will benefit from a common international conceptualization of the approach, including standardized and internationally accepted terminology. In particular, the development of good WBE practices for different aspects of small-scale WBE is warranted. This includes the establishment of guidelines for a comprehensive characterization of the local sewer system and its sub-sewersheds, and transparent reporting to ensure comparability of small-scale WBE results.

Understanding treatment of pain during SARS-CoV-2 pandemic in a two-year intercity longitudinal study using wastewater-based epidemiology.

SARS-CoV-2 pandemic had a significant impact on the society, economy, and health of people around the world with consequences that need to be better understood for future pandemic preparedness. This manuscript provides insights into the usage of pharmaceuticals for pain treatment management throughout SARS-CoV-2 pandemic. Four towns and cities with a total population of > 1 million people covering an area of 2000 km2 in South West England were monitored for twenty-four months. Results showed different patterns in pain pharma usage, with small towns having higher population normalised daily loads (PNDLs) than big cities for majority of pain killers studied. This is likely due to demographics of these cities with smaller cities having older population. Per capita consumption of non-steroidal anti-inflammatory drugs (NSAIDs) increased compared to pre-pandemic usage in line with SARS-CoV-2 infections (ibuprofen and acetaminophen), while body pain drugs (diclofenac and naproxen) decreased in line with restrictions and closure of sports facilities. Changes in population normalised daily intake (PNDI) of pain killers were particularly apparent during the 1st and 3rd national lockdown. Comparison of PNDIs with prescriptions highlighted differences related to medication availability (OTC drugs) and patients' nonadherence (prescribed drugs). In addition, several instances of direct disposal events across the catchments were observed which raises an issue of lack of pharma compliance and general understanding of potential environmental impacts from pharma usage.

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

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

Wastewater-based epidemiology as a public health resource in low- and middle-income settings.

In the face of emerging and re-emerging diseases, novel and innovative approaches to population scale surveillance are necessary for the early detection and quantification of pathogens. The last decade has seen the rapid development of wastewater and environmental surveillance (WES) to address public health challenges, which has led to establishment of wastewater-based epidemiology (WBE) approaches being deployed to monitor a range of health hazards. WBE exploits the fact that excretions and secretions from urine, and from the gut are discharged in wastewater, particularly sewage, such that sampling sewage systems provides an early warning system for disease outbreaks by providing an early indication of pathogen circulation. While WBE has been mainly used in locations with networked wastewater systems, here we consider its value for less connected populations typical of lower-income settings, and in assess the opportunity afforded by pit latrines to sample communities and localities. We propose that where populations struggle to access health and diagnostic facilities, and despite several additional challenges, sampling unconnected wastewater systems remains an important means to monitor the health of large populations in a relatively cost-effective manner.

Estimation of the spatial pattern of gout prevalence across China by wastewater-based epidemiology.

Gout is a metabolic arthritis caused by hyperuricemia. In recent years, the prevalence of gout has been increased significantly in China due to the improvement of the living standards, and gout has become another common metabolic disease following diabetes mellitus. Gout severely affects the health status and life quality of human. In order to monitor the near real-time prevalence of gout, a wastewater-based epidemiology (WBE) approach was carried out in 257 Chinese cities using febuxostat as the biomarker. Febuxostat in wastewater was measured by a LC-MS/MS method with satisfactory results of method validation. The average concentration of febuxostat in wastewater was 53.05 ± 31.76 ng/L, with the estimated per capita consumption of 124.40 ± 73.37 mg/day/1000 inhabitant. The calculated prevalence of febuxostat was 0.41 % ± 0.24 %, and the prevalence of gout was finally estimated to be 1.30 % ± 0.77 % (0.60 % to 2.11 %), which was nearly consistent with value of 1.10 % obtained from the Guideline for the diagnosis and management of hyperuricemia and gout in China (2019). The results indicated that the febuxostat-based WBE approach might be reasonable to assess the near real-time gout prevalence in China.

Refining the correction factor for a better monitoring of antidepressant use by wastewater-based epidemiology: A case study of amitriptyline.

Wastewater-based epidemiology (WBE) is proposed as a cost-effective approach to objectively monitor the antidepressant use but it requires more accurate correction factors (CF) than what had been used in previous studies. Amitriptyline is a popular prescription medicine for treating depression and nerve pain, which could be prone to misuse and need monitoring. The CF of amitriptyline employed in previous WBE studies varied from 10 to 100, leading to substantial disparities between WBE estimates and expected mass of antidepressants in wastewater. Hence, this study aimed to take amitriptyline as a case study and refine the CF by correlating mass loads measured in wastewater from 12.2 million inhabitants collected during the 2016 Census with corresponding annual sales data. The triangulation of WBE data and sales data resulted in a newly-derived CF of 7, which is significantly different from the CF values used in previous studies. The newly derived CF was applied to a secondary, multi-year (2017 to 2020) WBE dataset for validation against sales data in the same period, demonstrating the estimated amitriptyline use (380 ± 320 mg/day/1000 inhabitants) is consistent with sales data (450 ± 190 mg/day/1000 inhabitants). When we applied the new CF to previous studies, the wastewater consumption loads matched better to prescription data than previous WBE estimations. The refined CF of amitriptyline can be used in future WBE studies to improve the accuracy of the consumption estimates.

Comparison of adsorption-extraction (AE) workflows for improved measurements of viral and bacterial nucleic acid in untreated wastewater.

The lack of standardized methods and large differences in virus concentration and extraction workflows have hampered Severe Acute Respiratory Syndrome (SARS-CoV-2) wastewater surveillance and data reporting practices. Numerous studies have shown that adsorption-extraction (AE) method holds promise, yet several uncertainties remain regarding the optimal AE workflow. Several procedural components may influence the recovered concentrations of target nucleic acid, including membrane types, homogenization instruments, speed and duration, and lysis buffer. In this study, 42 different AE workflows that varied these components were compared to determine the optimal workflow by quantifying endogenous SARS-CoV-2, human adenovirus 40/41 (HAdV 40/41), and a bacterial marker gene of fecal contamination (Bacteroides HF183). Our findings suggest that the workflow chosen had a significant impact on SARS-CoV-2 concentrations, whereas it had minimal impact on HF183 and no effect on HAdV 40/41 concentrations. When comparing individual components in a workflow, such as membrane type (MF-Millipore™ 0.45 µm MCE vs. Isopore™ 0.40 µm), we found that they had no impact on SARS-CoV-2, HAdV 40/41, and HF183 concentrations. This suggests that at least some consumables and equipment are interchangeable. Buffer PM1 + TRIzol-based workflows yielded higher concentrations of SARS-CoV-2 than other workflows. HF183 concentrations were higher in workflows without chloroform. Similarly, higher homogenization speeds (5000-10,000 rpm) led to increased concentrations of SARS-CoV-2 and HF183 but had no effect on HAdV 40/41. Our findings indicate that minor enhancements to the AE workflow can improve the recovery of viruses and bacteria from the wastewater, leading to improved outcomes from wastewater surveillance efforts.

Persistence of human respiratory viral RNA in wastewater-settled solids.

Wastewater-based epidemiology has emerged as a valuable tool for monitoring respiratory viral diseases within communities by analyzing concentrations of viral nucleic-acids in wastewater. However, little is known about the fate of respiratory virus nucleic-acids in wastewater. Two important fate processes that may modulate their concentrations in wastewater as they move from household drains to the point of collection include sorption or partitioning to wastewater solids and degradation. This study investigated the decay kinetics of genomic nucleic-acids of seven human respiratory viruses, including severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), respiratory syncytial virus (RSV), human coronavirus (HCoV)-OC43, HCoV-229E, HCoV-NL63, human rhinovirus (HRV), and influenza A virus (IAV), as well as pepper mild mottle virus (PMMoV) in wastewater solids. Viruses (except for PMMoV) were spiked into wastewater solids and their concentrations were followed for 50 days at three different temperatures (4°C, 22°C, and 37°C). Viral genomic RNA decayed following first-order kinetics with decay rate constants k from 0 to 0.219 per day. Decay rate constants k were not different from 0 for all targets in solids incubated at 4°C; k values were largest at 37°C and at this temperature, k values were similar across nucleic-acid targets. Regardless of temperature, there was limited viral RNA decay, with an estimated 0% to 20% reduction, over the typical residence times of sewage in the piped systems between input and collection point (<1 day). The k values reported herein can be used directly in fate and transport models to inform the interpretation of measurements made during wastewater surveillance.IMPORTANCEUnderstanding whether or not the RNA targets quantified for wastewater-based epidemiology (WBE) efforts decay during transport between drains and the point of sample collection is critical for data interpretation. Here we show limited decay of viral RNA targets typically measured for respiratory disease WBE.

Feasibility of sulfated BPA and BPS as wastewater-based epidemiology biomarkers: Insights from wastewater and reported human urine analysis.

In wastewater-based epidemiology (WBE), the selection of appropriate biomarkers presents a significant challenge. Recently, sulfated bisphenols have garnered attention as potential WBE biomarkers due to their increased stability in wastewater compared to glucuronide conjugates. This study aims to comprehensively assess the feasibility of employing sulfated BPA and BPS as WBE biomarkers by analyzing both WBE and human biomonitoring data. To conduct this research, wastewater samples were collected from six domestic wastewater treatment plants in Guangzhou, China, and urinary concentration of BPA and BPS were obtained from peer-reviewed literature. The results revealed that mean urinary concentrations of BPA and BPS, calculated using Monte Carlo simulations, significantly exceeded those reported in human biomonitoring studies. Furthermore, the per capita mass load ratio of sulfated BPA and BPS in human urine to the mass load in wastewater was found to be below 10 %. This outcome suggests that the excretion of BPA-S and BPS-S in urine does not make a substantial contribution to wastewater, hinting at the existence of other notable sources. Consequently, our study concludes that sulfated BPA-S and BPS-S are not suitable candidates as WBE biomarkers. This work provides a referenceable analytical framework for evaluating the feasibility of WBE biomarkers and emphasizes the necessity for caution when utilizing WBE to assess human exposure to chemicals.

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.

Integrating socio-economic vulnerability factors improves neighborhood-scale wastewater-based epidemiology for public health applications.

Wastewater Based Epidemiology (WBE) of COVID-19 is a low-cost, non-invasive, and inclusive early warning tool for disease spread. Previously studied WBE focused on sampling at wastewater treatment plant scale, limiting the level at which demographic and geographic variations in disease dynamics can be incorporated into the analysis of certain neighborhoods. This study demonstrates the integration of demographic mapping to improve the WBE of COVID-19 and associated post-COVID disease prediction (here kidney disease) at the neighborhood level using machine learning. WBE was conducted at six neighborhoods in Seattle during October 2020 - February 2022. Wastewater processing and RT-qPCR were performed to obtain SARS-CoV-2 RNA concentration. Census data, clinical data of COVID-19, as well as patient data of acute kidney injury (AKI) cases reported during the study period were collected and the distribution across the city was studied using Geographic Information System (GIS) mapping. Further, we analyzed the data set to better understand socioeconomic impacts on disease prevalence of COVID-19 and AKI per neighborhood. The heterogeneity of eleven demographic factors (such as education and age among others) was observed within neighborhoods across the city of Seattle. Dynamics of COVID-19 clinical cases and wastewater SARS-CoV-2 varied across neighborhood with different levels of demographics. Machine learning models trained with data from the earlier stages of the pandemic were able to predict both COVID-19 and AKI incidence in the later stages of the pandemic (Spearman correlation coefficient of 0·546 - 0·904), with the most predictive model trained on the combination of wastewater data and demographics. The integration of demographics strengthened machine learning models' capabilities to predict prevalence of COVID-19, and of AKI as a marker for post-COVID sequelae. Demographic-based WBE presents an effective tool to monitor and manage public health beyond COVID-19 at the neighborhood level.