Wastewater-Based Epidemiology: Global Collaborative to Maximize Contributions in the Fight Against COVID-19

Surveillance of SARS-CoV-2 in wastewater by quantitative PCR and digital PCR: a case study in Shijiazhuang city, Hebei province, China

In this study, we reported the first long-term monitoring of SARS-CoV-2 in wastewater in Mainland China from November 2021 to October 2023. The city of Shijiazhuang was employed for this case study. We developed a triple reverse transcription droplet digital PCR (RT-ddPCR) method using triple primer-probes for simultaneous detection of the N1 gene, E gene, and Pepper mild mottle virus (PMMoV) to achieve accurate quantification of SARS-CoV-2 RNA in wastewater. Both the RT-ddPCR method and the commercial multiplex reverse transcription quantitative polymerase chain reaction (RT-qPCR) method were implemented for the detection of SARS-CoV-2 in wastewater in Shijiazhuang City over a 24-month period. Results showed that SARS-CoV-2 was detected for the first time in the wastewater of Shijiazhuang City on 10 November 2022. The peak of COVID-19 cases occurred in the middle of December 2022, when the concentration of SARS-CoV-2 in the wastewater was highest. The trend of virus concentration increases and decreases forming a "long-tailed" shape in the COVID-19  outbreak and recession cycle. The results indicated that both multiplex RT-ddPCR and RT-qPCR are effective in detecting SARS-CoV-2 in wastewater, but RT-ddPCR is capable of detecting low concentrations of SARS-CoV-2 in wastewater which is more efficient. The SARS-CoV-2 abundance in wastewater is correlated to clinical data, outlining the public health utility of this work.HighlightsFirst long-term monitoring of SARS-CoV-2 in wastewater in Mainland ChinaCOVID-19 outbreak was tracked in Shijiazhuang City from outbreak to containmentWastewater was monitored simultaneously using RT-ddPCR and RT-qPCR methodsTriple primer-probe RT-ddPCR detects N1 and E genes of SARS-CoV-2 and PMMoV.

Beyond COVID-19: Wastewater-based epidemiology for multipathogen surveillance and normalization strategies

Wastewater-based epidemiology (WBE) is a critical tool for monitoring community health. Although much attention has focused on severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), a causative agent of coronavirus disease 2019 (COVID-19), other pathogens also pose significant health risks. This study quantified the presence of SARS-CoV-2, influenza A virus (Inf-A), and noroviruses of genogroups I (NoV-GI) and II (NoV-GII) in wastewater samples collected weekly (n = 170) from July 2023 to February 2024 from five wastewater treatment plants (WWTPs) in Yamanashi Prefecture, Japan, by quantitative PCR. Inf-A RNA exhibited localized prevalence with positive ratios of 59 %-82 % in different WWTPs, suggesting regional outbreaks within specific areas. NoV-GI (94 %, 160/170) and NoV-GII (100 %, 170/170) RNA were highly prevalent, with NoV-GII (6.1 ± 0.8 log10 copies/L) consistently exceeding NoV-GI (5.4 ± 0.7 log10 copies/L) RNA concentrations. SARS-CoV-2 RNA was detected in 100 % of the samples, with mean concentrations of 5.3 ± 0.5 log10 copies/L in WWTP E and 5.8 ± 0.4 log10 copies/L each in other WWTPs. Seasonal variability was evident, with higher concentrations of all pathogenic viruses during winter. Non-normalized and normalized virus concentrations by fecal indicator bacteria (Escherichia coli and total coliforms), an indicator virus (pepper mild mottle virus (PMMoV)), and turbidity revealed significant positive associations with the reported disease cases. Inf-A and NoV-GI + GII RNA concentrations showed strong correlations with influenza and acute gastroenteritis cases, particularly when normalized to E. coli (Spearman's ρ = 0.70-0.81) and total coliforms (ρ = 0.70-0.81), respectively. For SARS-CoV-2, non-normalized concentrations showed a correlation of 0.61, decreasing to 0.31 when normalized to PMMoV, suggesting that PMMoV is unsuitable. Turbidity normalization also yielded suboptimal results. This study underscored the importance of selecting suitable normalization parameters tailored to specific pathogens for accurate disease trend monitoring using WBE, demonstrating its utility beyond COVID-19 surveillance.

Decay of RNA and infectious SARS-CoV-2 and murine hepatitis virus in wastewater

Wastewater-based epidemiology (WBE) has been an important tool for population surveillance during the COVID-19 pandemic and continues to play a key role in monitoring SARS-CoV-2 infection levels following reductions in national clinical testing schemes. Studies measuring decay profiles of SARS-CoV-2 in wastewater have underscored the value of WBE, however investigations have been hampered by high biosafety requirements for SARS-CoV-2 infection studies. Therefore, surrogate viruses with lower biosafety standards have been used for SARS-CoV-2 decay studies, such as murine hepatitis virus (MHV), but few studies have directly compared decay rates of both viruses. We compared the persistence of SARS-CoV-2 and MHV in wastewater, using 50 % tissue culture infectious dose (TCID50) and reverse transcription quantitative polymerase chain reaction (RT-qPCR) assays to assess infectious virus titre and viral gene markers, respectively. Infectious SARS-CoV-2 and MHV indicate similar endpoints, however observed early decay characteristics differed, with infectious SARS-CoV-2 decaying more rapidly than MHV. We find that MHV is an appropriate infectious virus surrogate for viable SARS-CoV-2, however inconsistencies exist in viral RNA decay parameters, indicating MHV may not be a suitable nucleic acid surrogate across certain temperature regimes. This study highlights the importance of sample preparation and the potential for decay rate overestimation in wastewater surveillance for SARS-CoV-2 and other pathogens.

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

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

Leveraging wastewater surveillance to detect viral diseases in livestock settings

Wastewater surveillance has evolved into a powerful tool for monitoring public health-relevant analytes. Recent applications in tracking severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) infection highlight its potential. Beyond humans, it can be extended to livestock settings where there is increasing demand for livestock products, posing risks of disease emergence. Wastewater surveillance may offer non-invasive, cost-effective means to detect potential outbreaks among animals. This approach aligns with the "One Health" paradigm, emphasizing the interconnectedness of animal, human, and ecosystem health. By monitoring viruses in livestock wastewater, early detection, prevention, and control strategies can be employed, safeguarding both animal and human health, economic stability, and international trade. This integrated "One Health" approach enhances collaboration and a comprehensive understanding of disease dynamics, supporting proactive measures in the Anthropocene era where animal and human diseases are on the rise.

SARS-CoV-2 wastewater variant surveillance: pandemic response leveraging FDA's GenomeTrakr network

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.

IMPORTANCE

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.

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.

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.

SARS-CoV-2 detection in pediatric dental clinic wastewater reflects the number of local COVID-19 cases in children under 10 years old

This was the first longitudinal study to analyze dental clinic wastewater to estimate asymptomatic SARS-CoV-2 infection trends in children. We monitored wastewater over a 14-month period, spanning three major COVID-19 waves driven by the Alpha, Delta, and Omicron variants. Each Saturday, wastewater was sampled at the Pediatric Dental Clinic of the only dental hospital in Japan's Saitama Prefecture. The relationship between the weekly number of cases in Saitama Prefecture among residents aged < 10 years (exposure) and wastewater SARS-CoV-2 RNA detection (outcome) was examined. The number of cases was significantly associated with wastewater SARS-CoV-2 RNA positivity (risk ratio, 5.36; 95% confidence interval, 1.72-16.67; Fisher's exact test, p = 0.0005). A sample from Week 8 of 2022 harbored the Omicron variant. Compared to sporadic individual testing, this approach allows continuous population-level surveillance, which is less affected by healthcare seeking and test availability. Since wastewater from pediatric dental clinics originates from the oral cavities of asymptomatic children, such testing can provide important information regarding asymptomatic COVID-19 in children, complementing clinical pediatric data.

Understanding associations between antimicrobial agents usage and antimicrobial resistance genes prevalence at the community level using wastewater-based epidemiology: A Spanish pilot study

Understanding the development and spread of antimicrobial resistance (AMR) is important for combating this global threat for public health. Wastewater-based epidemiology (WBE) is a complementary approach to current surveillance programs that minimizes some of the existing limitations. The aim of the present study is to explore WBE for monitoring antibiotics and antibiotic resistance genes (ARGs) in wastewater samples collected during 2021/2022 from the city of Castellon (Spain). Eighteen commonly prescribed antibiotics have been selected and measured by liquid chromatography coupled to tandem mass spectrometry (LC-MS/MS), with triple quadrupole mass analysers. Moreover, qPCR for specific ARGs has been performed to obtain information of these genes in co-presence with antibiotics. All selected ARGs, along with a total of 11 antibiotics, were identified. The highest population-normalized daily loads were observed for the macrolide azithromycin, followed by the quinolones ciprofloxacin and levofloxacin. Subsequently, daily consumption estimates based on wastewater data were compared with prescription data of antibiotics. Statistical analyses were conducted to explore if there is correlation between antibiotics and ARGs. While no correlations were found between antibiotics and their corresponding ARGs, certain correlations (p < 0.05) were identified among non-corresponding ARGs. In addition, a strong positive correlation was found between the sum of all antibiotics and the intl1 gene. Moreover, population-normalized ARG loads significantly correlate with the 16S rRNA-normalized ARG loads, serving as an indicator for population size. Results provide a baseline for future work and a proof-of-concept emphasising the need for future work and long-term surveillance, and highlight the need of similar programs at a regional and global levels worldwide.

Considerations and Opportunities for Probe Capture Enrichment Sequencing of Emerging Viruses from Wastewater

Until recently, wastewater-based monitoring for pathogens of public health concern primarily used PCR-based quantification methods and targeted sequencing for specific pathogens (e.g., SARS-CoV-2). In the past three years, researchers have expanded sequencing to monitor a broad range of pathogens, applying probe capture enrichment to wastewater. The goals of those studies included (1) monitoring and expanding fundamental knowledge of disease dynamics for known pathogens and (2) evaluating the potential for early detection of emerging diseases resulting from zoonotic spillover or novel viral variants. Several studies using off-the-shelf probe panels designed for clinical and environmental surveillance reported that enrichment increased virus relative abundance but did not recover complete genomes for most nonenteric viruses. Based on our experience and recent results reported by others using these panels for wastewater, clinical, and synthetic samples, we discuss challenges and technical factors that affect the rates of false positive and false negative results. We identify trade-offs and opportunities throughout the workflow, including in wastewater sample processing, probe panel design, and bioinformatic analysis. We suggest tailored methods of virus concentration and background removal, carefully designed probe panels, and multithresholded bioinformatics analysis.

Human norovirus (HuNoV) GII RNA in wastewater solids at 145 United States wastewater treatment plants: comparison to positivity rates of clinical specimens and modeled estimates of HuNoV GII shedders

BACKGROUND

Human norovirus (HuNoV) is a leading cause of disease globally, yet actual incidence is unknown. HuNoV infections are not reportable in the United States, and surveillance is limited to tracking severe illnesses or outbreaks. Wastewater monitoring for HuNoV has been done previously and results indicate it is present in wastewater influent and concentrations are associated with HuNoV infections in the communities contributing to wastewater. However, work has mostly been limited to monthly samples of liquid wastewater at one or a few wastewater treatment plants (WWTPs).

OBJECTIVE

The objectives of this study are to investigate whether HuNoV GII preferentially adsorbs to wastewater solids, investigate concentrations of HuNoV GII in wastewater solids in wastewater treatment plants across the county, and explore how those relate to clinical measures of disease occurrence. In addition, we aim to develop and apply a mass-balance model that predicts the fraction of individuals shedding HuNoV in their stool based on measured concentrations in wastewater solids.

METHODS

We measured HuNoV GII RNA in matched wastewater solids and liquid influent in 7 samples from a WWTP. We also applied the HuNoV GII assay to measure viral RNA in over 6000 wastewater solids samples from 145 WWTPs from across the United States daily to three times per week for up to five months. Measurements were made using digital droplet RT-PCR.

RESULTS

HuNoV GII RNA preferentially adsorbs to wastewater solids where it is present at 1000 times the concentration in influent. Concentrations of HuNoV GII RNA correlate positively with clinical HuNoV positivity rates. Model output of the fraction of individuals shedding HuNoV is variable and uncertain, but consistent with indirect estimates of symptomatic HuNoV infections in the United States.

IMPACT STATEMENT

Illness caused by HuNoV is not reportable in the United States so there is limited data on disease occurrence. Wastewater monitoring can provide information about the community spread of HuNoV. Data from wastewater can be available within 24 h of sample receipt at a laboratory. Wastewater is agnostic to whether individuals seek medical care, are symptomatic, and the severity of illness. Knowledge gleaned from wastewater may be used by public health professionals to make recommendations on hand washing, surface disinfection, or other behaviors to reduce transmission of HuNoV, or medical doctors to inform clinical decision making.

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

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

A modeling pipeline to relate municipal wastewater surveillance and regional public health data

As COVID-19 becomes endemic, public health departments benefit from improved passive indicators, which are independent of voluntary testing data, to estimate the prevalence of COVID-19 in local communities. Quantification of SARS-CoV-2 RNA from wastewater has the potential to be a powerful passive indicator. However, connecting measured SARS-CoV-2 RNA to community prevalence is challenging due to the high noise typical of environmental samples. We have developed a generalized pipeline using in- and out-of-sample model selection to test the ability of different correction models to reduce the variance in wastewater measurements and applied it to data collected from treatment plants in the Chicago area. We built and compared a set of multi-linear regression models, which incorporate pepper mild mottle virus (PMMoV) as a population biomarker, Bovine coronavirus (BCoV) as a recovery control, and wastewater system flow rate into a corrected estimate for SARS-CoV-2 RNA concentration. For our data, models with BCoV performed better than those with PMMoV, but the pipeline should be used to reevaluate any new data set as the sources of variance may change across locations, lab methods, and disease states. Using our best-fit model, we investigated the utility of RNA measurements in wastewater as a leading indicator of COVID-19 trends. We did this in a rolling manner for corrected wastewater data and for other prevalence indicators and statistically compared the temporal relationship between new increases in the wastewater data and those in other prevalence indicators. We found that wastewater trends often lead other COVID-19 indicators in predicting new surges.

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

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

Critical Needs for Integrated Surveillance: Wastewater-Based and Clinical Epidemiology in Evolving Scenarios with Lessons Learned from SARS-CoV-2

During the COVID-19 pandemic, wastewater-based epidemiology (WBE) and clinical surveillance have been used as tools for analyzing the circulation of SARS-CoV-2 in the community, but both approaches can be strongly influenced by some sources of variability. From the challenging perspective of integrating environmental and clinical data, we performed a correlation analysis between SARS-CoV-2 concentrations in raw sewage and incident COVID-19 cases in areas served by medium-size wastewater treatment plants (WWTPs) from 2021 to 2023. To this aim, both datasets were adjusted for several sources of variability: WBE data were adjusted for factors including the analytical protocol, sewage flow, and population size, while clinical data adjustments considered the demographic composition of the served population. Then, we addressed the impact on the correlation of differences among sewerage networks and variations in the frequency and type of swab tests due to changes in political and regulatory scenarios. Wastewater and clinical data were significantly correlated when restrictive containment measures and limited movements were in effect (ρ = 0.50) and when COVID-19 cases were confirmed exclusively through molecular testing (ρ = 0.49). Moreover, a positive (although weak) correlation arose for WWTPs located in densely populated areas (ρ = 0.37) and with shorter sewerage lengths (ρ = 0.28). This study provides methodological approaches for interpreting WBE and clinical surveillance data, which could also be useful for other infections. Data adjustments and evaluation of possible sources of bias need to be carefully considered from the perspective of integrated environmental and clinical surveillance of infections.

Optimization of the 5-plex digital PCR workflow for simultaneous monitoring of SARS-CoV-2 and other pathogenic viruses in wastewater

Wastewater-based epidemiology is a valuable tool for monitoring pathogenic viruses in the environment, including severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the causative agent of coronavirus disease 2019 (COVID-19). While quantitative polymerase chain reaction (qPCR) is widely used for pathogen surveillance in wastewater, it can be affected by inhibition and is limited to relative quantification. Digital PCR (dPCR) offers potential solutions to these limitations. In this study, a 5-plex dPCR workflow was optimized for the simultaneous detection of SARS-CoV-2, influenza A virus, enteroviruses (EnV), and noroviruses of genogroups I (NoV-GI) and GII (NoV-GII) in wastewater samples. Wastewater samples (n = 36) were collected from a wastewater treatment plant in Japan between August and October 2022. The optimization included the evaluation of singleplex and 5-plex dPCR assays, and two different concentration methods, extraction kits, and dPCR approaches. The performance of singleplex and 5-plex dPCR assays showed comparable linearity and reliability, with the 5-plex assays showing greater efficiency. The polyethylene glycol (PEG) precipitation method showed better performance over the centrifugation method, two-step reverse transcription (RT)-dPCR over the one-step RT-dPCR, and AllPrep PowerViral DNA/RNA Kit showed better performance than the QIAamp Viral RNA Mini Kit. The optimal workflow therefore included PEG precipitation, the AllPrep PowerViral DNA/RNA Kit, and two-step RT-dPCR. This workflow was selected to monitor the presence of SARS-CoV-2 and other pathogenic viruses in wastewater samples in a 5-plex dPCR approach, yielding promising results. SARS-CoV-2 RNA was detected in the majority of samples, with NoV-GI, NoV-GII, and EnV also being detected. The successful optimization and application of the 5-plex dPCR assay for pathogen surveillance in wastewater offers significant benefits, including enhanced community health assessment and more effective responses to public health threats.

Real-Time Epidemiology and Acute Care Need Monitoring and Forecasting for COVID-19 via Bayesian Sequential Monte Carlo-Leveraged Transmission Models

COVID-19 transmission models have conferred great value in informing public health understanding, planning, and response. However, the pandemic also demonstrated the infeasibility of basing public health decision-making on transmission models with pre-set assumptions. No matter how favourably evidenced when built, a model with fixed assumptions is challenged by numerous factors that are difficult to predict. Ongoing planning associated with rolling back and re-instituting measures, initiating surge planning, and issuing public health advisories can benefit from approaches that allow state estimates for transmission models to be continuously updated in light of unfolding time series. A model being continuously regrounded by empirical data in this way can provide a consistent, integrated depiction of the evolving underlying epidemiology and acute care demand, offer the ability to project forward such a depiction in a fashion suitable for triggering the deployment of acute care surge capacity or public health measures, and support quantitative evaluation of tradeoffs associated with prospective interventions in light of the latest estimates of the underlying epidemiology. We describe here the design, implementation, and multi-year daily use for public health and clinical support decision-making of a particle-filtered COVID-19 compartmental model, which served Canadian federal and provincial governments via regular reporting starting in June 2020. The use of the Bayesian sequential Monte Carlo algorithm of particle filtering allows the model to be regrounded daily and adapt to new trends within daily incoming data-including test volumes and positivity rates, endogenous and travel-related cases, hospital census and admissions flows, daily counts of dose-specific vaccinations administered, measured concentration of SARS-CoV-2 in wastewater, and mortality. Important model outputs include estimates (via sampling) of the count of undiagnosed infectives, the count of individuals at different stages of the natural history of frankly and pauci-symptomatic infection, the current force of infection, effective reproductive number, and current and cumulative infection prevalence. Following a brief description of the model design, we describe how the machine learning algorithm of particle filtering is used to continually reground estimates of the dynamic model state, support a probabilistic model projection of epidemiology and health system capacity utilization and service demand, and probabilistically evaluate tradeoffs between potential intervention scenarios. We further note aspects of model use in practice as an effective reporting tool in a manner that is parameterized by jurisdiction, including the support of a scripting pipeline that permits a fully automated reporting pipeline other than security-restricted new data retrieval, including automated model deployment, data validity checks, and automatic post-scenario scripting and reporting. As demonstrated by this multi-year deployment of the Bayesian machine learning algorithm of particle filtering to provide industrial-strength reporting to inform public health decision-making across Canada, such methods offer strong support for evidence-based public health decision-making informed by ever-current articulated transmission models whose probabilistic state and parameter estimates are continually regrounded by diverse data streams.

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

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

Wastewater surveillance of high risk substances in Southern Nevada: Sucralose normalization to translate data for potential public health action

The COVID-19 pandemic highlighted the value of wastewater surveillance in providing unbiased assessments of incidence/prevalence for infectious disease targets, ultimately leading to the development of local, state, and national programs across the United States. To address the growing epidemic of drug abuse, there have been calls to extend these programs to high risk substances (HRS) and metabolites, while leveraging the experience gained during the pandemic and from ongoing efforts in other countries. This study further advances the science of wastewater surveillance for HRS by (1) highlighting analytical and sewer transport considerations, (2) proposing sucralose normalization to adjust for varying human urine/fecal load and confounded population estimates (e.g., high tourism areas), and (3) characterizing temporal and geographic trends in HRS use. This one-year study across eight sewersheds in Southern Nevada (208 total samples) monitored concentrations of 17 pharmaceuticals and personal care products (PPCPs) and 22 HRS and metabolites, including natural, semi-synthetic, and synthetic opioids. The data indicated a ∼200 % increase in heroin and methamphetamine use since 2010, a stark increase in fentanyl consumption beginning in October 2022, and statistically significant differences in HRS consumption patterns between sewersheds and on certain dates. Notably, the latter outcome highlights the potential for wastewater surveillance data to be strategically translated into public health action to reduce and/or more rapidly respond to overdoses.

Evaluation of colorimetric RT-LAMP for screening of SARS-CoV-2 in untreated wastewater

This study compared reverse transcription-loop-mediated isothermal amplification (RT-LAMP) and three reverse transcription-quantitative polymerase chain reaction (RT-qPCR) assays targeting the N and E genes of the SARS-CoV-2 genome for detecting RNA in untreated wastewater samples. RT-qPCR assays exhibited consistent amplification down to 2 × 102 GC/reaction, with greater analytical sensitivity at 2 × 101 GC/reaction by US CDC N1 and US CDC N2 assays. In contrast, RT-LAMP exhibited lower sensitivity, detecting SARS-CoV-2 only at or above 2 × 103 GC/reaction. For SARS-CoV-2 seeded wastewater samples, the US CDC N1 assay exhibited greater analytical sensitivity than the US CDC N2, E_Sarbeco, and RT-LAMP assays. Out of 30 wastewater samples, RT-qPCR detected endogenous SARS-CoV-2 RNA in 29 samples, while RT-LAMP identified 27 positive samples, with 20 displaying consistent amplifications in all three RT-LAMP technical replicates. Agreement analysis revealed a strong concordance between RT-LAMP and the US CDC N1 and E_Sarbeco RT-qPCR assays (κ = 0.474) but lower agreement with the US CDC N2 RT-qPCR assay (κ = 0.359). Quantification of SARS-CoV-2 RNA in positive samples revealed a strong correlation between the US CDC N1 and E_Sarbeco assays, while the US CDC N1 and US CDC N2 assays exhibited weak correlation. Logistic regression analysis indicated that RT-LAMP results correlated with RNA quantified by the US CDC N1 and E_Sarbeco assays, with 95 % limits of detection of 3.99 and 3.47 log10 GC/15 mL, respectively. In conclusion, despite lower sensitivity compared to RT-qPCR assays, RT-LAMP may offer advantages for wastewater surveillance, such as rapid results (estimated as twice as fast), and simplicity, making it a valuable tool in the shifting landscape of COVID-19 wastewater surveillance. Furthermore, LAMP positive wastewater samples might be prioritized for SARS-CoV-2 sequencing due to reduced analytical sensitivity. These findings support the use of RT-LAMP as a specific and efficient method for screening wastewater samples for SARS-CoV-2, particularly in resource-limited settings.

COVID-19 hospitalizations and deaths predicted by SARS-CoV-2 levels in Boise, Idaho wastewater

The viral load of COVID-19 in untreated wastewater from Idaho's capital city Boise, ID (Ada County) has been used to predict changes in hospital admissions (statewide in Idaho) and deaths (Ada County) using distributed fixed lag modeling and artificial neural networks (ANN). The wastewater viral counts were used to determine the lag time between peaks in wastewater viral counts and COVID-19 hospitalizations as well as deaths (14 and 23 days, respectively). Quantitative measurement of SARS-CoV-2 viral RNA counts in the untreated wastewater was determined three times a week using RT-qPCR over a span of 13 months. To mitigate the effects of PCR inhibitors in wastewater, a series of dilution tests were conducted, and the 1/4 dilution was used to generate the most successful model. Wastewater SARS-CoV-2 viral RNA counts and hospitalization from June 7, 2021 to December 29, 2021 were used as training data to predict hospitalizations; and wastewater SARS-CoV-2 viral RNA counts and deaths from June 7, 2021 to December 20, 2021 were used as training data to predict deaths. These training data were used to make predictive ANN models for future hospitalizations and deaths. To the best of our knowledge, this is the first report of prediction of deaths from COVID-19 based on wastewater SARS-CoV-2 viral RNA counts using machine learning-based multilayered ANN. The applied modeling demonstrates that wastewater surveillance data can be combined with hospitalizations and death data to generate machine learning-based ANN models that predict future COVID-19 hospital admissions and deaths, providing an early warning for medical response teams and healthcare policymakers.

Impact of coagulation on SARS-CoV-2 and PMMoV viral signal in wastewater solids

Wastewater surveillance (WWS) of SARS-CoV-2 has become a crucial tool for monitoring COVID-19 cases and outbreaks. Previous studies have indicated that SARS-CoV-2 RNA measurement from testing solid-rich primary sludge yields better sensitivity compared to testing wastewater influent. Furthermore, measurement of pepper mild mottle virus (PMMoV) signal in wastewater allows for precise normalization of SARS-CoV-2 viral signal based on solid content, enhancing disease prevalence tracking. However, despite the widespread adoption of WWS, a knowledge gap remains regarding the impact of ferric sulfate coagulation, commonly used in enhanced primary clarification, the initial stage of wastewater treatment where solids are sedimented and removed, on SARS-CoV-2 and PMMoV quantification in wastewater-based epidemiology. This study examines the effects of ferric sulfate addition, along with the associated pH reduction, on the measurement of SARS-CoV-2 and PMMoV viral measurements in wastewater primary clarified sludge through jar testing. Results show that the addition of Fe3+ concentrations in the conventional 0 to 60 mg/L range caused no effect on SARS-CoV-2 N1 and N2 gene region measurements in wastewater solids. However, elevated Fe3+ concentrations were shown to be associated with a statistically significant increase in PMMoV viral measurements in wastewater solids, which consequently resulted in the underestimation of PMMoV-normalized SARS-CoV-2 viral signal measurements (N1 and N2 copies/copies of PMMoV). The observed pH reduction from coagulant addition did not contribute to the increased PMMoV measurements, suggesting that this phenomenon arises from the partitioning of PMMoV viral particles into wastewater solids.

Evaluating the sensitivity of droplet digital PCR for the quantification of SARS-CoV-2 in wastewater

Wastewater surveillance for SARS-CoV-2 has been demonstrated to be a valuable tool in monitoring community-level virus circulation and assessing new outbreaks. It may become a useful tool in the early detection and response to future pandemics, enabling public health authorities to implement timely interventions and mitigate the spread of infectious diseases with the fecal excretion of their agents. It also offers a chance for cost-effective surveillance. Reverse transcription-quantitative polymerase chain reaction (RTqPCR) is the most commonly used method for viral RNA detection in wastewater due to its sensitivity, reliability, and widespread availability. However, recent studies have indicated that reverse transcription droplet digital PCR (RTddPCR) has the potential to offer improved sensitivity and accuracy for quantifying SARS-CoV-2 RNA in wastewater samples. In this study, we compared the performance of RTqPCR and RTddPCR approaches for SARS-CoV-2 detection and quantification on wastewater samples collected during the third epidemic wave in Saxony, Germany, characterized by low-incidence infection periods. The determined limits of detection (LOD) and quantification (LOQ) were within the same order of magnitude, and no significant differences were observed between the PCR approaches with respect to the number of positive or quantifiable samples. Our results indicate that both RTqPCR and RTddPCR are highly sensitive methods for detecting SARS-CoV-2. Consequently, the actual gain in sensitivity associated with ddPCR lags behind theoretical expectations. Hence, the choice between the two PCR methods in further environmental surveillance programs is rather a matter of available resources and throughput requirements.

Comparison of viral concentration techniques for native fecal indicators and pathogens from wastewater

Viral pathogens are typically dilute in environmental waters, necessitating a concentration step prior to subsequent quantification or analysis. Historically, studies on viral concentration efficiency have been done by spiking known viruses into the sample; however, spike-in controls may not have the same behavior as "native" viruses exposed to environmental conditions. In this study, four concentration methods, including polyethylene glycol precipitation (PEG), skimmed milk flocculation (SMF), pH drop followed by filtration through a 0.45 μm filter (pH), and centrifugation using an Amicon filter (Amicon), were evaluated to concentrate native viral targets in wastewater. Viral targets included both indicators (crAssphage and pepper mild mottle virus) and pathogens (adenovirus, norovirus GII, human polyomavirus, and SARS-CoV-2) in addition to a bacterial marker (HF183). A non-native spike-in control was also added to compare native and spike-in recoveries. Recovery varied widely across targets and methods, ranging from 0.1 to 39.3 %. The Amicon method was the most broadly effective concentration for recovery efficiency. For the lowest-titer target, the PEG method resulted in the lowest number of non-detections, with 96.7 % positive detections for SARS-CoV-2, compared to 66.7 %, 80 %, and 76.7 % positive detections for SMF, pH, and Amicon, respectively. The non-native spike-ins chosen were only representative of a few native recovery trends, varying by both target and concentration method, and consistently under or over-estimated recovery. Overall, this study suggests the utility of including native targets in viral concentration evaluation and determining the efficiency of concentration methods for a specific target of interest.

Enhanced detection of mpox virus in wastewater using a pre-amplification approach: A pilot study informing population-level monitoring of low-titer pathogens

A recent outbreak of the mpox virus (MPXV) occurred in non-endemic regions of the world beginning in May 2022. Pathogen surveillance systems faced pressure to quickly establish response protocols, offering an opportunity to employ wastewater-based epidemiology (WBE) for population-level monitoring. The pilot study reported herein aimed to: (i) develop a reliable protocol for MPXV DNA detection in wastewater which would reduce false negative reporting, (ii) test this protocol on wastewater from various regions across the United States, and (iii) conduct a state of the science review of the current literature reporting on experimental methods for MPXV detection using WBE. Twenty-four-hour composite samples of untreated municipal wastewater were collected from the states of New Jersey, Georgia, Illinois, Texas, Arizona, and Washington beginning July 3rd, 2022 through October 16th, 2022 (n = 60). Samples underwent vacuum filtration, DNA extraction from captured solids, MPXV DNA pre-amplification, and qPCR analysis. Of the 60 samples analyzed, a total of eight (13%) tested positive for MPXV in the states of Washington, Texas, New Jersey, and Illinois. The presence of clade IIb MPXV DNA in these samples was confirmed via Sanger sequencing and integration of pre-amplification prior to qPCR decreased the rate of false negative detections by 87% as compared to qPCR analysis alone. Wastewater-derived detections of MPXV were compared to clinical datasets, with 50% of detections occurring as clinical cases were increasing/peaking and 50% occurring as clinical cases waned. Results from the literature review (n = 9 studies) revealed successful strategies for the detection of MPXV DNA in wastewater, however also emphasized a need for further method optimization and standardization. Overall, this work highlights the use of pre-amplification prior to qPCR detection as a means to capture the presence of MPXV DNA in community wastewater and offers guidance for monitoring low-titer pathogens via WBE.

Utilizing river and wastewater as a SARS-CoV-2 surveillance tool in settings with limited formal sewage systems

The COVID-19 pandemic has profoundly impacted health systems globally and robust surveillance has been critical for pandemic control, however not all countries can currently sustain community pathogen surveillance programs. Wastewater surveillance has proven valuable in high-income settings, but less is known about the utility of water surveillance of pathogens in low-income countries. Here we show how wastewater surveillance of SAR-CoV-2 can be used to identify temporal changes and help determine circulating variants quickly. In Malawi, a country with limited community-based COVID-19 testing capacity, we explore the utility of rivers and wastewater for SARS-CoV-2 surveillance. From May 2020-May 2022, we collect water from up to 112 river or defunct wastewater treatment plant sites, detecting SARS-CoV-2 in 8.3% of samples. Peak SARS-CoV-2 detection in water samples predate peaks in clinical cases. Sequencing of water samples identified the Beta, Delta, and Omicron variants, with Delta and Omicron detected well in advance of detection in patients. Our work highlights how wastewater can be used to detect emerging waves, identify variants of concern, and provide an early warning system in settings with no formal sewage systems.

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

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

SARS-CoV-2 Burden in Wastewater and its Elimination Using Disinfection

Background

Pathogenic viruses have been abundant and diverse in wastewater, reflecting the pattern of infection in humans. Human feces, urine, and perhaps other washouts that frequently circulate in sewage systems may contaminate wastewater with SARS-CoV-2. It's crucial to effectively disinfect wastewater since poorly handled wastewater could put the population at risk of infection.

Aims

To emphasize the presence and spread of SARS-CoV-2 in sewage (wastewater) through viral shedding from the patients to detect the virus in the population using wastewater-based epidemiology. Also, to effectively manage the transmission of SARS-CoV-2 and reduce the spread of the virus in the population using disinfectants is highlighted.

Methods

We evaluated articles from December 2019 to August 2022 that addressed SARS-CoV-2 shedding in wastewater and surveillance through wastewater-based epidemiology. We included the papers on wastewater disinfection for the elimination of SARS-CoV-2. Google Scholar, PubMed, and Research4Life are the three electronic databases from which all of the papers were retrieved.

Results

It is possible for viral shedding to get into the wastewater. The enumeration of viral RNA from it can be used to monitor virus circulation in the human community. SARS-CoV-2 can be removed from wastewater by using modern disinfection techniques such as sodium hypochlorite, liquid chlorine, chlorine dioxide, peracetic acid, and ultraviolet light.

Conclusion

SARS-CoV-2 burden estimates at the population level can be obtained via longitudinal examination of wastewater, and SARS-CoV-2 can be removed from the wastewater through disinfection.

Sunlight photolysis of SARS-CoV-2 N1 gene target in the water environment: considerations for the environmental surveillance of wastewater-impacted surface waters

Wastewater surveillance of SARS-CoV-2 has been used around the world to supplement clinical testing data for situational awareness of COVID-19 disease trends. Many regions of the world lack centralized wastewater collection and treatment infrastructure, which presents additional considerations for wastewater surveillance of SARS-CoV-2, including environmental decay of the RT-qPCR gene targets used for quantification of SARS-CoV-2 virions. Given the role of sunlight in the environmental decay of RNA, we evaluated sunlight photolysis kinetics of the N1 gene target in heat-inactivated SARS-CoV-2 with a solar simulator under laboratory conditions. Insignificant photolysis of the N1 target was observed in a photosensitizer-free matrix. Conversely, significant decay of the N1 target was observed in wastewater at a shallow depth (<1 cm). Given that sunlight irradiance is affected by several environmental factors, first-order decay rate models were used to evaluate the effect of water column depth, time of the year, and latitude on decay kinetics. Decay rate constants were found to decrease significantly with greater depth of the well-mixed water column, at high latitudes, and in the winter. Therefore, sunlight-mediated decay of the N1 gene target is likely to be minimal, and is unlikely to confound results from wastewater-based epidemiology programs utilizing wastewater-impacted surface waters.

Predictive potential of SARS-CoV-2 RNA concentration in wastewater to assess the dynamics of COVID-19 clinical outcomes and infections

Coronavirus disease 2019 - caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) -, has triggered a worldwide pandemic resulting in 665 million infections and over 6.5 million deaths as of December 15, 2022. The development of different epidemiological tools have helped predict new outbreaks and assess the behavior of clinical variables in different health contexts. In this study, we aimed to monitor concentrations of SARS-CoV-2 in wastewater as a tool to predict the progression of clinical variables during Waves 3, 5, and 6 of the pandemic in the Spanish city of Xátiva from September 2020 to March 2022. We estimated SARS-CoV-2 RNA concentrations in 195 wastewater samples using the RT-PCR Diagnostic Panel validated by the Center for Disease Control and Prevention. We also compared the trends of several clinical variables (14-day cumulative incidence, positive cases, hospital cases and stays, critical cases and stays, primary care visits, and deaths) for each study wave against wastewater SARS-CoV-2 RNA concentrations using Pearson's product-moment correlations, a two-sided Mann-Whitney U test, and a cross-correlation analysis. We found strong correlations between SARS-CoV-2 concentrations with 14-day cumulative incidence and positive cases over time. Wastewater RNA concentrations showed strong correlations with these variables one and two weeks in advance. There were significant correlations with hospitalizations and critical care during Wave 3 and Wave 6; cross-correlations were stronger for hospitalization stays one week before during Wave 6. No association between vaccination percentages and wastewater viral concentrations was observed. Our findings support wastewater SARS-CoV-2 concentrations as a potential surveillance tool to anticipate infection and epidemiological data such as 14-day cumulative incidence, hospitalizations, and critical care stays. Public health authorities could use this epidemiological tool on a similar population as an aid for health care decision-making during an epidemic outbreak.

Monitoring sewer systems to detect the eDNA of missing persons and persons of interest

The paper proposes a theoretical framework for using eDNA detection devices to locate missing persons, wanted criminals, and persons of interest in densely populated areas by monitoring sewer water. The proposed system includes a computer application to enter information on missing targets, and the data collected by the system can be used to narrow down their location for rescue or apprehension. The paper investigates eDNA persistence, sewer water studies, and current eDNA and DNA analysis tools to formulate a research concept. The limitations of the concept are mentioned, and it is suggested that collaboration between a large university and a leading DNA analysis equipment manufacturer is needed to custom-build eDNA detection devices to fulfill the requirements of the concept. Eventually, manufacturing costs will drive down the initial and nationwide adoption costs of the system.

One Health and Global Health View of Antimicrobial Susceptibility through the "Eye" of Aeromonas: Systematic Review and Meta-Analysis

Antimicrobial resistance (AMR) is one of the most pressing public health concerns; therefore, it is imperative to advance our understanding of the factors influencing AMR from Global and One Health perspectives. To address this, Aeromonas populations were identified using 16S rRNA gene libraries among human, agriculture, aquaculture, drinking water, surface water, and wastewater samples, supporting its use as indicator bacteria to study AMR. A systematic review and meta-analysis was then performed from Global and One Health perspectives, including data from 221 articles describing 15 891 isolates from 57 countries. The interconnectedness of different environments was evident as minimal differences were identified between sectors among 21 different antimicrobials. However, resistance to critically important antibiotics (aztreonam and cefepime) was significantly higher among wastewater populations compared with clinical isolates. Additionally, isolates from untreated wastewater typically exhibited increased AMR compared with those from treated wastewater. Furthermore, aquaculture was associated with increased AMR to ciprofloxacin and tetracycline compared with wild-caught seafood. Using the World Health Organization AWaRe classifications, countries with lower consumption of "Access" compared to "Watch" drugs from 2000 to 2015 demonstrated higher AMR levels. The current analysis revealed negative correlations between AMR and anthropogenic factors, such as environmental performance indices and socioeconomic standing. Environmental health and sanitation were two of the environmental factors most strongly correlated with AMR. The current analysis highlights the negative impacts of "Watch" drug overconsumption, anthropogenic activity, absence of wastewater infrastructure, and aquaculture on AMR, thus stressing the need for proper infrastructure and global regulations to combat this growing problem.

Interactive SARS-CoV-2 dashboard for real-time geospatial visualisation of sewage and clinical surveillance data from Dhaka, Bangladesh: a tool for public health situational awareness

Throughout the COVID-19 pandemic, many dashboards were created to visualise clinical case incidence. Other dashboards have displayed SARS-CoV-2 sewage data, largely from countries with formal sewage networks. However, very few dashboards from low-income and lower-middle-income countries integrated both clinical and sewage data sets. We created a dashboard to track in real-time both COVID-19 clinical cases and the level of SARS-CoV-2 virus in sewage in Dhaka, Bangladesh. The development of this dashboard was a collaborative iterative process with Bangladesh public health stakeholders to include specific features to address their needs. The final dashboard product provides spatiotemporal visualisations of COVID-19 cases and SARS-CoV-2 viral load at 51 sewage collection sites in 21 wards in Dhaka since 24 March 2020. Our dashboard was updated weekly for the Bangladesh COVID-19 national task force to provide supplemental data for public health stakeholders making public policy decisions on mitigation efforts. Here, we highlight the importance of working closely with public health stakeholders to create a COVID-19 dashboard for public health impact. In the future, the dashboard can be expanded to track trends of other infectious diseases as sewage surveillance is increased for other pathogens.

Intensity of sample processing methods impacts wastewater SARS-CoV-2 whole genome amplicon sequencing outcomes

Wastewater SARS-CoV-2 surveillance has been deployed since the beginning of the COVID-19 pandemic to monitor the dynamics in virus burden in local communities. Genomic surveillance of SARS-CoV-2 in wastewater, particularly efforts aimed at whole genome sequencing for variant tracking and identification, are still challenging due to low target concentration, complex microbial and chemical background, and lack of robust nucleic acid recovery experimental procedures. The intrinsic sample limitations are inherent to wastewater and are thus unavoidable. Here, we use a statistical approach that couples correlation analyses to a random forest-based machine learning algorithm to evaluate potentially important factors associated with wastewater SARS-CoV-2 whole genome amplicon sequencing outcomes, with a specific focus on the breadth of genome coverage. We collected 182 composite and grab wastewater samples from the Chicago area between November 2020 to October 2021. Samples were processed using a mixture of processing methods reflecting different homogenization intensities (HA + Zymo beads, HA + glass beads, and Nanotrap), and were sequenced using one of the two library preparation kits (the Illumina COVIDseq kit and the QIAseq DIRECT kit). Technical factors evaluated using statistical and machine learning approaches include sample types, certain sample intrinsic features, and processing and sequencing methods. The results suggested that sample processing methods could be a predominant factor affecting sequencing outcomes, and library preparation kits was considered a minor factor. A synthetic SARS-CoV-2 RNA spike-in experiment was performed to validate the impact from processing methods and suggested that the intensity of the processing methods could lead to different RNA fragmentation patterns, which could also explain the observed inconsistency between qPCR quantification and sequencing outcomes. Overall, extra attention should be paid to wastewater sample processing (i.e., concentration and homogenization) for sufficient and good quality SARS-CoV-2 RNA for downstream sequencing.

Long-term impacts of an urban sanitation intervention on enteric pathogens in children in Maputo city, Mozambique: study protocol for a cross-sectional follow-up to the Maputo Sanitation (MapSan) trial 5 years postintervention

INTRODUCTION

We previously assessed the effect of an onsite sanitation intervention in informal neighbourhoods of urban Maputo, Mozambique on enteric pathogen detection in children after 2 years of follow-up (Maputo Sanitation (MapSan) trial, ClinicalTrials.gov: NCT02362932). We found significant reductions in Shigella and Trichuris prevalence but only among children born after the intervention was delivered. In this study, we assess the health impacts of the sanitation intervention after 5 years among children born into study households postintervention.

METHODS AND ANALYSIS

We are conducting a cross-sectional household study of enteric pathogen detection in child stool and the environment at compounds (household clusters sharing sanitation and outdoor living space) that received the pour-flush toilet and septic tank intervention at least 5 years prior or meet the original criteria for trial control sites. We are enrolling at least 400 children (ages 29 days to 60 months) in each treatment arm. Our primary outcome is the prevalence of 22 bacterial, protozoan, and soil transmitted helminth enteric pathogens in child stool using the pooled prevalence ratio across the outcome set to assess the overall intervention effect. Secondary outcomes include the individual pathogen detection prevalence and gene copy density of 27 enteric pathogens (including viruses); mean height-for-age, weight-for-age, and weight-for-height z-scores; prevalence of stunting, underweight, and wasting; and the 7-day period prevalence of caregiver-reported diarrhoea. All analyses are adjusted for prespecified covariates and examined for effect measure modification by age. Environmental samples from study households and the public domain are assessed for pathogens and faecal indicators to explore environmental exposures and monitor disease transmission.

ETHICS AND DISSEMINATION

Study protocols have been reviewed and approved by human subjects review boards at the Ministry of Health, Republic of Mozambique and the University of North Carolina at Chapel Hill. Deidentified study data will be deposited at https://osf.io/e7pvk/.

TRIAL REGISTRATION NUMBER

ISRCTN86084138.

Water sector resilience in the United Kingdom and Ireland: The COVID-19 challenge

The outbreak of COVID-19 led to restrictions on movements and activities, which presented a serious challenge to the resilience of the water sector. It is essential to understand how successfully water companies responded to this unprecedented event so effective plans can be built for future disruptive events. This study aimed to evaluate how the water sectors in the UK and Ireland were affected from a holistic sustainability and resilience-based perspective. Using pre-COVID data for 18 indicators of company performance and comparing them to the first year of the pandemic, the direction and magnitudes of change varied across companies. Financial indicators were significantly negatively affected, with interest cover ratio, post-tax return on regulated equity, and operating profit, exhibiting the greatest average declines of 21%, 21%, and 18%, respectively, a trend that would be dangerous to provisions and company operations if continued. Despite this, service and environmental indicators improved during the first year of the pandemic, exemplified by unplanned outage, risk of sewer storm flooding, and water quality compliance risk decreasing by a mean average of 37%, 32%, and 27%, respectively. Analysis using the Hicks-Moorsteen Productivity Index concluded that average productivity increased by 35%. The results suggest that the water sector was relatively resilient to the COVID-19 pandemic in terms of services, but adverse effects may have manifested in a deteriorated financial position that could exacerbate future challenges arising from exogenous pressures such as climate change. Specific advice for the UK water sector is to scrutinize non-critical spending, such as shareholder payments, during periods of economic downturn to ensure essential capital projects can be carried out. Although results are temporal and indicator selection sensitive, we recommend that policy, regulation, and corporate culture embrace frameworks that support long-term resilience to since the relative success in response to COVID-19 does not guarantee future success against differing challenges. This study generates a timely yet tentative insight into the diverse performance of the water sector during the pandemic, pertinent to the water industry, regulators, academia, and the public.

Real-time sewage surveillance for SARS-CoV-2 in Dhaka, Bangladesh versus clinical COVID-19 surveillance: a longitudinal environmental surveillance study (December, 2019-December, 2021)

BACKGROUND

Clinical surveillance for COVID-19 has typically been challenging in low-income and middle-income settings. From December, 2019, to December, 2021, we implemented environmental surveillance in a converging informal sewage network in Dhaka, Bangladesh, to investigate SARS-CoV-2 transmission across different income levels of the city compared with clinical surveillance.

METHODS

All sewage lines were mapped, and sites were selected with estimated catchment populations of more than 1000 individuals. We analysed 2073 sewage samples, collected weekly from 37 sites, and 648 days of case data from eight wards with varying socioeconomic statuses. We assessed the correlations between the viral load in sewage samples and clinical cases.

FINDINGS

SARS-CoV-2 was consistently detected across all wards (low, middle, and high income) despite large differences in reported clinical cases and periods of no cases. The majority of COVID-19 cases (26 256 [55·1%] of 47 683) were reported from Ward 19, a high-income area with high levels of clinical testing (123 times the number of tests per 100 000 individuals compared with Ward 9 [middle-income] in November, 2020, and 70 times the number of tests per 100 000 individuals compared with Ward 5 [low-income] in November, 2021), despite containing only 19·4% of the study population (142 413 of 734 755 individuals). Conversely, a similar quantity of SARS-CoV-2 was detected in sewage across different income levels (median difference in high-income vs low-income areas: 0·23 log10 viral copies + 1). The correlation between the mean sewage viral load (log10 viral copies + 1) and the log10 clinical cases increased with time (r = 0·90 in July-December, 2021 and r=0·59 in July-December, 2020). Before major waves of infection, viral load quantity in sewage samples increased 1-2 weeks before the clinical cases.

INTERPRETATION

This study demonstrates the utility and importance of environmental surveillance for SARS-CoV-2 in a lower-middle-income country. We show that environmental surveillance provides an early warning of increases in transmission and reveals evidence of persistent circulation in poorer areas where access to clinical testing is limited.

FUNDING

Bill & Melinda Gates Foundation.

Wastewater-based epidemiology for the assessment of population exposure to chemicals: The need for integration with human biomonitoring for global One Health actions

WBE has now become a complimentary tool in SARS-CoV-2 surveillance. This was preceded by the established application of WBE to assess the consumption of illicit drugs in communities. It is now timely to build on this and take the opportunity to expand WBE to enable comprehensive assessment of community exposure to chemical stressors and their mixtures. The goal of WBE is to quantify community exposure, discover exposure-outcome associations, and trigger policy, technological or societal intervention strategies with the overarching aim of exposure prevention and public health promotion. To achieve WBE's full potential, the following key aspects require further action: (1) Integration of WBE-HBM (human biomonitoring) initiatives that provide comprehensive community-individual multichemical exposure assessment. (2) Global WBE monitoring campaigns to provide much needed data on exposure in low- and middle-income countries (LMICs) and fill in the gaps in knowledge especially in the underrepresented highly urbanised as well as rural settings in LMICs. (3) Combining WBE with One Health actions to enable effective interventions. (4) Advancements in new analytical tools and methodologies for WBE progression to enable biomarker selection for exposure studies, and to provide sensitive and selective multiresidue analysis for trace multi-biomarker quantification in a complex wastewater matrix. Most of all, further developments of WBE needs to be undertaken by co-design with key stakeholder groups: government organisations, health authorities and private sector.

Wastewater-based epidemiology for comprehensive community health diagnostics in a national surveillance study: Mining biochemical markers in wastewater

This manuscript showcases results from a large scale and comprehensive wastewater-based epidemiology (WBE) study focussed on multi-biomarker suite analysis of both chemical and biological determinants in 10 cities and towns across England equating to a population of ∼7 million people. Multi-biomarker suite analysis, describing city metabolism, can provide a holistic understanding to encompass all of human, and human-derived, activities of a city in a single model: from lifestyle choices (e.g. caffeine intake, nicotine) through to health status (e.g. prevalence of pathogenic organisms, usage of pharmaceuticals as proxy for non-communicable disease, NCD, conditions or infectious disease status), and exposure to harmful chemicals due to environmental and industrial sources (e.g. pesticide intake via contaminated food and industrial exposure). Population normalised daily loads (PNDLs) of many chemical markers were found, to a large extent, driven by the size of population contributing to wastewater (especially NCDs). However, there are several exceptions providing insights into chemical intake that can inform either disease status in various communities or unintentional exposure to hazardous chemicals: e.g. very high PNDLs of ibuprofen in Hull resulting from its direct disposal (confirmed by ibuprofen/2-hydroxyibuprofen ratios) and bisphenol A (BPA) in Hull, Lancaster and Portsmouth likely related to industrial discharge. An importance for tracking endogenous health markers such as 4-hydroxy-2-nonenal-mercapturic acid (HNE-MA, an oxidative stress marker) as a generic marker of health status in communities was observed due to increased levels of HNE-MA seen at Barnoldswick wastewater treatment plant that coincided with higher-than-average paracetamol usage and SARS-CoV-2 prevalence in this community. PNDLs of virus markers were found to be highly variable. Being very prevalent in communities nationwide during sampling, SARS-CoV-2 presence in wastewater was to a large extent community driven. The same applies to the fecal marker virus, crAssphage, which is very prevalent in urban communities. In contrast, norovirus and enterovirus showed much higher variability in prevalence across all sites investigated, with clear cases of localized outbreaks in some cities while maintaining low prevalence in other locations. In conclusion, this study clearly demonstrates the potential for WBE to provide an integrated assessment of community health which can help target and validate policy interventions aimed at improving public health and wellbeing.

Utilizing river and wastewater as a SARS-CoV-2 surveillance tool to predict trends and identify variants of concern in settings with limited formal sewage systems

The COVID-19 pandemic continues to impact health systems globally and robust surveillance is critical for pandemic control, however not all countries can sustain community surveillance programs. Wastewater surveillance has proven valuable in high-income settings, but little is known about how river and informal sewage in low-income countries can be used for environmental surveillance of SARS-CoV-2. In Malawi, a country with limited community-based COVID-19 testing capacity, we explored the utility of rivers and wastewater for SARS-CoV-2 surveillance. From May 2020 - January 2022, we collected water from up to 112 river or informal sewage sites/month, detecting SARS-CoV-2 in 8.3% of samples. Peak SARS-CoV-2 detection in water samples predated peaks in clinical cases. Sequencing of water samples identified the Beta, Delta, and Omicron variants, with Delta and Omicron detected well in advance of detection in patients. Our work highlights wastewater can be used for detecting emerging waves, identifying variants of concern and function as an early warning system in settings with no formal sewage systems.

Frequency and degradation of SARS-CoV-2 markers N1, N2, and E in sewage

Coronavirus disease 2019 (COVID-19) is an infectious disease that is mainly spread through aerosolized droplets containing severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and is excreted in feces by infected individuals. Sewage surveillance has been applied widely to obtain data on the prevalence of COVID-19 in whole communities. We used SARS-CoV-2 gene targets N1, N2, and E to determine the prevalence of COVID-19 at both municipal and building levels. Frequency analysis of wastewater testing indicated that single markers detected only 85% or less of samples that were detected as positive for SARS-CoV-2 with the three markers combined, indicating the necessity of pairing markers to lower the false-negative rate. The best pair of markers in both municipal and building level monitoring was N1 and N2, which correctly identified 98% of positive samples detected with the three markers combined. The degradation rates of all three targets were assessed at two different temperatures (25 and 35 °C) as a possible explanation for observed differences between markers in frequency. Results indicated that all three RNA targets degrade at nearly the same rate, indicating that differences in degradation rate are not responsible for the observed differences in marker frequency.

Highly socially vulnerable communities exhibit disproportionately increased viral loads as measured in community wastewater

Wastewater surveillance has proven to be a useful tool for evidence-based epidemiology in the fight against the SARS-CoV-2 virus. It is particularly useful at the population level where acquisition of individual test samples may be time or cost-prohibitive. Wastewater surveillance for SARS-CoV-2 has typically been performed at wastewater treatment plants; however, this study was designed to sample on a local level to monitor the spread of the virus among three communities with distinct social vulnerability indices in Shreveport, Louisiana, located in a socially vulnerable region of the United States. Twice-monthly grab samples were collected from September 30, 2020, to March 23, 2021, during the Beta wave of the pandemic. The goals of the study were to examine whether: 1) concentrations of SARS-CoV-2 RNA in wastewater varied with social vulnerability indices and, 2) the time lag of spikes differed during wastewater monitoring in the distinct communities. The size of the population contributing to each sample was assessed via the quantification of the pepper mild mottle virus (PMMoV), which was significantly higher in the less socially vulnerable community. We found that the communities with higher social vulnerability exhibited greater viral loads as assessed by wastewater when normalized with PMMoV (Kruskal-Wallis, p < 0.05). The timing of the spread of the virus through the three communities appeared to be similar. These results suggest that interconnected communities within a municipality experienced the spread of the SARS-CoV-2 virus at similar times, but areas of high social vulnerability experienced more intense wastewater viral loads.

Improving wastewater-based epidemiology performance through streamlined automation

Wastewater-based epidemiology (WBE) has enabled us to describe Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) infections in populations. However, implementation of wastewater monitoring of SARS-CoV-2 is limited due to the need for expert staff, expensive equipment, and prolonged processing times. As WBE increases in scope (beyond SARS-CoV-2) and scale (beyond developed regions), there is a need to make WBE processes simpler, cheaper, and faster. We developed an automated workflow based on a simplified method termed exclusion-based sample preparation (ESP). Our automated workflow takes 40 min from raw wastewater to purified RNA, which is several times faster than conventional WBE methods. The total assay cost per sample/replicate is $6.50 which includes consumables and reagents for concentration, extraction, and RT-qPCR quantification. The assay complexity is reduced significantly, as extraction and concentration steps are integrated and automated. The high recovery efficiency of the automated assay (84.5 ± 25.4%) yielded an improved Limit of Detection (LoDAutomated=40 copies/mL) compared to the manual process (LoDManual=206 copies/mL), increasing analytical sensitivity. We validated the performance of the automated workflow by comparing it with the manual method using wastewater samples from several locations. The results from the two methods correlated strongly (r = 0.953), while the automated method was shown to be more precise. In 83% of the samples, the automated method showed lower variation between replicates, which is likely due to higher technical errors in the manual process e.g., pipetting. Our automated wastewater workflow can support the expansion of WBE in the fight against Coronavirus Disease of 2019 (COVID-19) and other epidemics.

Degradation rates influence the ability of composite samples to represent 24-hourly means of SARS-CoV-2 and other microbiological target measures in wastewater

The utility of using severe-acute respiratory syndrome coronavirus-2 (SARS-CoV-2) RNA for assessing the prevalence of COVID-19 within communities begins with the design of the sample collection program. The objective of this study was to assess the utility of 24-hour composites as representative samples for measuring multiple microbiological targets in wastewater, and whether normalization of SARS-CoV-2 by endogenous targets can be used to decrease hour to hour variability at different watershed scales. Two sets of experiments were conducted, in tandem with the same wastewater, with samples collected at the building, cluster, and community sewershed scales. The first set of experiments focused on evaluating degradation of microbiological targets: SARS-CoV-2, Simian Immunodeficiency Virus (SIV) - a surrogate spiked into the wastewater, plus human waste indicators of Pepper Mild Mottle Virus (PMMoV), Beta-2 microglobulin (B2M), and fecal coliform bacteria (FC). The second focused on the variability of these targets from samples, collected each hour on the hour. Results show that SARS-CoV-2, PMMoV, and B2M were relatively stable, with minimal degradation over 24-h. SIV, which was spiked-in prior to analysis, degraded significantly and FC increased significantly over the course of 24 h, emphasizing the possibility for decay and growth within wastewater. Hour-to-hour variability of the source wastewater was large between each hour of sampling relative to the variability of the SARS-CoV-2 levels calculated between sewershed scales; thus, differences in SARS-CoV-2 hourly variability were not statistically significant between sewershed scales. Results further provided that the quantified representativeness of 24-h composite samples (i.e., statistical equivalency compared against hourly collected grabs) was dependent upon the molecular target measured. Overall, improvements made by normalization were minimal within this study. Degradation and multiplication for other targets should be evaluated when deciding upon whether to collect composite or grab samples in future studies.

Wastewater surveillance of SARS-CoV-2 at intra-city level demonstrated high resolution in tracking COVID-19 and calibration using chemical indicators

Wastewater-based epidemiology has proven to be a supportive tool to better comprehend the dynamics of the COVID-19 pandemic. As the disease moves into endemic stage, the surveillance at wastewater sub-catchments such as pump station and manholes is providing a novel mechanism to examine the reemergence and to take measures that can prevent the spread. However, there is still a lack of understanding when it comes to wastewater-based epidemiology implementation at the smaller intra-city level for better granularity in data, and dilution effect of rain precipitation at pump stations. For this study, grab samples were collected from six areas of Seattle between March-October 2021. These sampling sites comprised five manholes and one pump station with population ranging from 2580 to 39,502 per manhole/pump station. The wastewater samples were analyzed for SARS-CoV-2 RNA concentrations, and we also obtained the daily COVID-19 cases (from individual clinical testing) for each corresponding sewershed, which ranged from 1 to 12 and the daily incidence varied between 3 and 64 per 100,000 of population. Rain precipitation lowered viral RNA levels and sensitivity of viral detection but wastewater total ammonia (NH₄⁺-N) and phosphate (PO₄^3--P) were shown as potential chemical indicators to calibrate/level out the dilution effect. These chemicals showed the potential in improving the wastewater surveillance capacity of COVID-19.

Evaluation of intra- and inter-lab variability in quantifying SARS-CoV-2 in a state-wide wastewater monitoring network

In December 2019, SARS-CoV-2, the virus that causes coronavirus disease 2019, was first reported and subsequently triggered a global pandemic. Wastewater monitoring, a strategy for quantifying viral gene concentrations from wastewater influents within a community, has served as an early warning and management tool for the spread of SARS-CoV-2 in a community. Ohio built a collaborative statewide wastewater monitoring network that is supported by eight labs (university, government, and commercial laboratories) with unique sample processing workflows. Consequently, we sought to characterize the variability in wastewater monitoring results for network labs. Across seven trials between October 2020 and November 2021, eight participating labs successfully quantified two SARS-CoV-2 RNA targets and human fecal indicator virus targets in wastewater sample aliquots with reproducible results, although recovery efficiencies of spiked surrogates ranged from 3 to 75%. When SARS-CoV-2 gene fragment concentrations were adjusted for recovery efficiency and flow, the proportion of variance between laboratories was minimized, serving as the best model to account for between-lab variance. Another adjustment factor (alone and in different combinations with the above factors) considered to account for sample and measurement variability includes fecal marker normalization. Genetic quantification variability can be attributed to many factors, including the methods, individual samples, and water quality parameters. In addition, statistically significant correlations were observed between SARS-CoV-2 RNA and COVID-19 case numbers, supporting the notion that wastewater surveillance continues to serve as an effective monitoring tool. This study serves as a real-time example of multi-laboratory collaboration for public health preparedness for infectious diseases.

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

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