Making waves: Plausible lead time for wastewater based epidemiology as an early warning system for COVID-19

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

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

A framework for integrating wastewater-based epidemiology and public health

Wastewater-based epidemiology (WBE) is an environmental approach to monitor community health through the analysis of sewage. The COVID-19 pandemic catalyzed scientists and public health professionals to revisit WBE as a tool to optimize resource allocation to mitigate disease spread and prevent outbreaks. Some studies have highlighted the value of WBE programs that coordinate with public health professionals; however, the details necessary for implementation are not well-characterized. To respond to this knowledge gap, this article documents the framework of a successful WBE program in Arizona, titled Wastewater Analysis for Tactical Epidemiological Response Systems (WATERS), detailing the developed structure and methods of communication that enabled public health preparedness and response actions. This communication illustrates how program operations were employed to reduce outbreak severity. The structure outlined here is customizable and may guide other programs in the implementation of WBE as a public health tool.

EU surveys insights: analytical tools, future directions, and the essential requirement for reference materials in wastewater monitoring of SARS-CoV-2, antimicrobial resistance and beyond

BACKGROUND

Wastewater surveillance (WWS) acts as a vigilant sentinel system for communities, analysing sewage to protect public health by detecting outbreaks and monitoring trends in pathogens and contaminants. To achieve a thorough comprehension of present and upcoming practices and to identify challenges and opportunities for standardisation and improvement in WWS methodologies, two EU surveys were conducted targeting over 750 WWS laboratories across Europe and other regions. The first survey explored a diverse range of activities currently undertaken or planned by laboratories. The second survey specifically targeted methods and quality controls utilised for SARS-CoV-2 surveillance.

RESULTS

The findings of the two surveys provide a comprehensive insight into the procedures and methodologies applied in WWS. In Europe, WWS primarily focuses on SARS-CoV-2 with 99% of the survey participants dedicated to this virus. However, the responses highlighted a lack of standardisation in the methodologies employed for monitoring SARS-CoV-2. The surveillance of other pathogens, including antimicrobial resistance, is currently fragmented and conducted by only a limited number of laboratories. Notably, these activities are anticipated to expand in the future. Survey replies emphasise the collective recognition of the need to enhance the accuracy of results in WWS practices, reflecting a shared commitment to advancing precision and effectiveness in WWS methodologies.

CONCLUSIONS

These surveys identified a lack of standardised common procedures in WWS practices and the need for quality standards and reference materials to enhance the accuracy and reliability of WWS methods in the future. In addition, it is important to broaden surveillance efforts beyond SARS-CoV-2 to include other emerging pathogens and antimicrobial resistance to ensure a comprehensive approach to protecting public health.

Temporal assessment of SARS-CoV-2 detection in wastewater and its epidemiological implications in COVID-19 case dynamics

This research evaluated the relationship between daily new Coronavirus Disease 2019 (COVID-19) cases and Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) concentrations in wastewater, followed by effects of differential SARS-CoV-2 shedding loads across various COVID-19 outbreaks. Linear regression analyses were utilized to examine the lead time of the SARS-CoV-2 signal in wastewater relative to new COVID-19 clinical cases. During the Delta wave, no lead time was evident, highlighting limited predictive capability of wastewater monitoring during this phase. However, significant lead times were observed during the Omicron wave, potentially attributed to testing capacity overload and subsequent case reporting delays or changes in shedding patterns. During the Post-Omicron wave (Febuary 23 to May 19, 2022), no lead time was discernible, whereas following the lifting of the COVID-19 state of emergency (May 30, 2022 to May 30, 2023), the correlation coefficient increased and demonstrated the potential of wastewater surveillance as an early warning system. Subsequently, we explored the virus shedding in wastewater through feces, operationalized as the ratio of SARS-CoV-2 concentrations to daily new COVID-19 cases. This ratio varied significantly across the Delta, Omicron, other variants and post-state-emergency phases, with the Kruskal-Wallis H test confirming a significant difference in medians across these stages (P < 0.0001). Despite its promise, wastewater surveillance of COVID-19 disease prevalence presents several challenges, including virus shedding variability, data interpretation complexity, the impact of environmental factors on viral degradation, and the lack of standardized testing procedures. Overall, our findings offer insights into the correlation between COVID-19 cases and wastewater viral concentrations, potential variation in SARS-CoV-2 shedding in wastewater across different pandemic phases, and underscore the promise and limitations of wastewater surveillance as an early warning system for disease prevalence trends.

Intriguing insight into unanswered questions about Mpox: exploring health policy implications and considerations

The 2022 multi-country Monkeypox (Mpox) outbreak has added concerns to scientific research. However, unanswered questions about the disease remain. These unanswered questions lie in different aspects, such as transmission, the affected community, clinical presentations, infection and prevention control and treatment and vaccination. It is imperative to address these issues to stop the spread and transmission of disease. We documented unanswered questions with Mpox and offered suggestions that could help put health policy into practice. One of those questions is why gay, bisexual or other men who have sex with men (gbMSM) are the most affected community, underscoring the importance of prioritizing this community regarding treatment, vaccination and post-exposure prophylaxis. In addition, destigmatizing gbMSM and implementing community-based gbMSM consultation and action alongside ethical surveillance can facilitate other preventive measures such as ring vaccination to curb disease transmission and track vaccine efficacy. Relevant to that, vaccine and drug side effects have implied the questionability of their use and stimulated the importance of health policy development regarding expanded access and off-label use, expressing the need for safe drug and vaccine development manufacturing. The possibility of reverse zoonotic has also been raised, thus indicating the requirement to screen not only humans, but also their related animals to understand the real magnitude of reverse zoonosis and its potential risks. Implementing infection prevention and control measures to stop the virus circulation at the human-animal interface that includes One Health approach is essential.

AH, Fernandez S, Huang AT, Vatanaprasan P, Saethang T, Luk-in S, Storer RJ, Ounjai P, Devanga Ragupathi NK, Kanthawee P, Ngamwongsatit N, Badavath VN, Thuptimdang W, Leelahavanichkul A, Kanjanabuch T, Miyanaga K, Cui L, Nanbo A, Shibuya K, Kupwiwat R, Sano D, Furukawa T, Sei K, Higgins PG, Kicic A, Singer AC, Chatsuwan T, Trowsdale S, Abe S, Ishikawa H, Amarasiri M, Modchang C, Wannigama DL. Exploring indoor and outdoor dust as a potential tool for detection and monitoring of COVID-19 transmission

This study investigated the potential of using SARS-CoV-2 viral concentrations in dust as an additional surveillance tool for early detection and monitoring of COVID-19 transmission. Dust samples were collected from 8 public locations in 16 districts of Bangkok, Thailand, from June to August 2021. SARS-CoV-2 RNA concentrations in dust were quantified, and their correlation with community case incidence was assessed. Our findings revealed a positive correlation between viral concentrations detected in dust and the relative risk of COVID-19. The highest risk was observed with no delay (0-day lag), and this risk gradually decreased as the lag time increased. We observed an overall decline in viral concentrations in public places during lockdown, closely associated with reduced human mobility. The effective reproduction number for COVID-19 transmission remained above one throughout the study period, suggesting that transmission may persist in locations beyond public areas even after the lockdown measures were in place.

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.

Emerging trends in wastewater treatment: Addressing microorganic pollutants and environmental impacts

This review focuses on the challenges and advances associated with the treatment and management of microorganic pollutants, encompassing pesticides, industrial chemicals, and persistent organic pollutants (POPs) in the environment. The translocation of these contaminants across multiple media, particularly through atmospheric transport, emphasizes their pervasive nature and the subsequent ecological risks. The urgency to develop cost-effective remediation strategies for emerging organic contaminants is paramount. As such, wastewater-based epidemiology and the increasing concern over estrogenicity are explored. By incorporating conventional and innovative wastewater treatment techniques, this article highlights the integration of environmental management strategies, analytical methodologies, and the importance of renewable energy in waste treatment. The primary objective is to provide a comprehensive perspective on the current scenario, imminent threats, and future directions in mitigating the effects of these pollutants on the environment. Furthermore, the review underscores the need for international collaboration in developing standardized guidelines and policies for monitoring and controlling these microorganic pollutants. It advocates for increased investment in research and development of advanced materials and technologies that can efficiently remove or neutralize these contaminants, thereby safeguarding environmental health and promoting sustainable practice.

Implementing an adaptive, two-tiered SARS-CoV-2 wastewater surveillance program on a university campus using passive sampling

Building-level wastewater-based surveillance (WBS) has been increasingly applied upstream from wastewater treatment plants to conduct targeted monitoring for SARS-CoV-2. In this study, a two-tiered, trigger-based wastewater surveillance program was developed on a university campus to monitor dormitory wastewater. The objective was to determine if passive sampling with cotton gauze as a sampling medium could be used to support institution-level public health action. Two nucleocapsid gene targets (N1 and N2) of SARS-CoV-2 as well as the endogenous fecal indicator pepper mild mottle virus (PMMoV) were quantified using RT-qPCR. >500 samples were analyzed during two contrasting surveillance periods. In the Fall of 2021 community viral burden was low and a tiered sampling network was able to isolate individual clinical cases at the building-scale. In the Winter of 2022 wastewater signals were quickly elevated by the emergence of the highly transmissible SARS-CoV-2 Omicron (B.1.1.529) variant. Prevalence of SARS-CoV-2 shifted surveillance objectives from isolating cases to monitoring trends, revealing both the benefits and limitations of a tiered surveillance design under different public health situations. Normalization of SARS-CoV-2 by PMMoV was not reflective of upstream population differences, suggesting saturation of the material occurred during the exposure period. The passive sampling method detected nearly all known clinical cases and in one instance was able to identify one pre-symptomatic individual days prior to confirmation by clinical test. Comparisons between campus samplers and municipal wastewater influent suggests that the spread of COVID-19 on the campus was similar to that of the broader community. The results demonstrate that passive sampling is an effective tool that can produce semi-quantitative data capable of tracking temporal trends to guide targeted public health decision-making at an institutional level. Practitioners of WBS can utilize these results to inform surveillance program designs that prioritize efficient resource use and rapid reporting.

Estimating the prevalence of dyslipidemia by measuring fenofibrate in 33 cities in China

Dyslipidemia, recognized as a predominant risk factor for atherosclerotic cardiovascular disease (CVD), remains a pressing health concern worldwide, particularly in China with nearly 40 % of the population adversely suffering. Fenofibrate, as one of the most commonly used drugs for dyslipidemia therapy, excreted as the format of fenofibrate-acid, which showed considerable stability in sewage samples and could be detected as WBE-biomarkers to monitor the prevalence of dyslipidemia. In this work, we reported the first research on estimating the prevalence of dyslipidemia by WBE approach. 527 sewage samples from 33 cities in China were extracted by solid phase and analyzed by LC-MS/MS. The detected concentration of fenofibrate acid in sewage was on an average of 120.5 ± 59.9 ng/L, and the reverse-calculated consumption of fenofibrate based on fenofibrate acid was 77.8 ± 25.0 mg/day/1000inh. Detailed analysis unveiled an average prevalence of fenofibrate at 0.056 % ± 0.018 %, and the dyslipidemia prevalence among the population aged over 15 was ultimately estimated to be 37.9 % ± 9.3 % and was in accordance with the China Cardiovascular research result of 40.4 %, which proves that WBE is a substitutable approach of traditional epidemiological investigation methods due to its timeliness and cost-effectiveness. This study demonstrated that estimating dyslipidemia prevalence by WBE with metabolite fenofibrate acid as a biomarker is feasible in most Chinese cities.

Priority setting for global WASH challenges in the age of wastewater-based epidemiological surveillance

Mortality due to inadequate WASH facilities exceeded that of COVID-19 in the regions of South-East Asia and Africa. The costs and benefits of wastewater-based surveillance are not known; however, the benefits of WASH interventions greatly exceed the costs. The dedication of effort and resources to wastewater-based surveillance and WASH in low- and middle-income countries should reflect disease burdens and cost/benefit assessments.

Changes to Public Health Surveillance Methods Due to the COVID-19 Pandemic: Scoping Review

BACKGROUND

Public health surveillance plays a vital role in informing public health decision-making. The onset of the COVID-19 pandemic in early 2020 caused a widespread shift in public health priorities. Global efforts focused on COVID-19 monitoring and contact tracing. Existing public health programs were interrupted due to physical distancing measures and reallocation of resources. The onset of the COVID-19 pandemic intersected with advancements in technologies that have the potential to support public health surveillance efforts.

OBJECTIVE

This scoping review aims to explore emergent public health surveillance methods during the early COVID-19 pandemic to characterize the impact of the pandemic on surveillance methods.

METHODS

A scoping search was conducted in multiple databases and by scanning key government and public health organization websites from March 2020 to January 2022. Published papers and gray literature that described the application of new or revised approaches to public health surveillance were included. Papers that discussed the implications of novel public health surveillance approaches from ethical, legal, security, and equity perspectives were also included. The surveillance subject, method, location, and setting were extracted from each paper to identify trends in surveillance practices. Two public health epidemiologists were invited to provide their perspectives as peer reviewers.

RESULTS

Of the 14,238 unique papers, a total of 241 papers describing novel surveillance methods and changes to surveillance methods are included. Eighty papers were review papers and 161 were single studies. Overall, the literature heavily featured papers detailing surveillance of COVID-19 transmission (n=187). Surveillance of other infectious diseases was also described, including other pathogens (n=12). Other public health topics included vaccines (n=9), mental health (n=11), substance use (n=4), healthy nutrition (n=1), maternal and child health (n=3), antimicrobial resistance (n=2), and misinformation (n=6). The literature was dominated by applications of digital surveillance, for example, by using big data through mobility tracking and infodemiology (n=163). Wastewater surveillance was also heavily represented (n=48). Other papers described adaptations to programs or methods that existed prior to the COVID-19 pandemic (n=9). The scoping search also found 109 papers that discuss the ethical, legal, security, and equity implications of emerging surveillance methods. The peer reviewer public health epidemiologists noted that additional changes likely exist, beyond what has been reported and available for evidence syntheses.

CONCLUSIONS

The COVID-19 pandemic accelerated advancements in surveillance and the adoption of new technologies, especially for digital and wastewater surveillance methods. Given the investments in these systems, further applications for public health surveillance are likely. The literature for surveillance methods was dominated by surveillance of infectious diseases, particularly COVID-19. A substantial amount of literature on the ethical, legal, security, and equity implications of these emerging surveillance methods also points to a need for cautious consideration of potential harm.

Effectiveness of environmental surveillance of SARS-CoV-2 as an early-warning system: Update of a systematic review during the second year of the pandemic

The aim of this updated systematic review was to offer an overview of the effectiveness of environmental surveillance (ES) of SARS-CoV-2 as a potential early-warning system (EWS) for COVID-19 and new variants of concerns (VOCs) during the second year of the pandemic. An updated literature search was conducted to evaluate the added value of ES of SARS-CoV-2 for public health decisions. The search for studies published between June 2021 and July 2022 resulted in 1,588 publications, identifying 331 articles for full-text screening. A total of 151 publications met our inclusion criteria for the assessment of the effectiveness of ES as an EWS and early detection of SARS-CoV-2 variants. We identified a further 30 publications among the grey literature. ES confirms its usefulness as an EWS for detecting new waves of SARS-CoV-2 infection with an average lead time of 1-2 weeks for most of the publication. ES could function as an EWS for new VOCs in areas with no registered cases or limited clinical capacity. Challenges in data harmonization and variant detection require standardized approaches and innovations for improved public health decision-making. ES confirms its potential to support public health decision-making and resource allocation in future outbreaks.

Early warning of statewide COVID-19 Omicron wave by sentineled urbanized sewer network monitoring using digital PCR in a province capital city, of Gujarat, India

Wastewater-based epidemiology (WBE) has been implemented globally. However, there remains confusion about the number and frequency of samples to be collected, as well as which types of treatment systems can provide reliable specific details about the virus prevalence in specific areas or communities, enabling prompt management and intervention measures. More research is necessary to fully comprehend the possibility of deploying sentinel locations in sewer networks in larger geographic areas. The present study introduces the first report on wastewater-based surveillance in Gandhinagar City using digital PCR (d-PCR) as a SARS-Cov-2 quantification tool, which describes the viral load from five pumping stations in Gandhinagar from October 2021 to March 2022. Raw wastewater samples (n = 119) were received and analyzed weekly to detect SARS-CoV-2 RNA, 109 of which were positive for N1 or N2 genes. The monthly variation analysis in viral genome copies depicted the highest concentrations in January 2022 and February 2022 (p < 0.05; Wilcoxon signed rank test) coincided with the Omicron wave, which contributed mainly from Vavol and Jaspur pumping stations. Cross-correlation analysis indicated that WBE from five stations in Gandhinagar, i.e., capital city sewer networks, provided two-week lead times to the citywide and statewide active cases (time-series cross-correlation function [CCF]; 0.666 and 0.648, respectively), mainly from individual contributions of the urbanized Kudasan and Vavol stations (CCF; 0.729 and 0.647, respectively). These findings suggest that sewer pumping stations in urbanized neighborhoods can be used as sentinel sites for statewide clinical surveillance and that WBE surveillance using digital PCR can be an efficient monitoring and management tool.

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.

Analyzing community wastewater in sub-sewersheds for the small-scale detection of SARS-CoV-2 variants in a German metropolitan area

Wastewater surveillance of SARS-CoV-2 proved useful, including for identifying the local appearance of newly identified virus variants. Previous studies focused on wastewater treatment plants (WWTP) with sewersheds of several hundred thousand people or at single building level, representing only a small number of people. Both approaches may prove inadequate for small-scale intra-urban inferences for early detection of emerging or novel virus variants. Our study aims (i) to analyze SARS-CoV-2 single nucleotide variants (SNVs) in wastewater of sub-sewersheds and WWTP using whole genome sequencing in order to (ii) investigate the potential of small-scale detection of novel known SARS-CoV-2 variants of concern (VOC) within a metropolitan wastewater system. We selected three sub-sewershed sampling sites, based on estimated population- and built environment-related indicators, and the inlet of the receiving WWTP in the Ruhr region, Germany. Untreated wastewater was sampled weekly between October and December 2021, with a total of 22 samples collected. SARS-CoV-2 RNA was analyzed by RT-qPCR and whole genome sequencing. For all samples, genome sequences were obtained, while only 13 samples were positive for RT-qPCR. We identified multiple specific SARS-CoV-2 SNVs in the wastewater samples of the sub-sewersheds and the WWTP. Identified SNVs reflected the dominance of VOC Delta at the time of sampling. Interestingly, we could identify an Omicron-specific SNV in one sub-sewershed. A concurrent wastewater study sampling the same WWTP detected the VOC Omicron one week later. Our observations suggest that the small-scale approach may prove particularly useful for the detection and description of spatially confined emerging or existing virus variants circulating in populations. Future studies applying small-scale sampling strategies taking into account the specific features of the wastewater system will be useful to analyze temporal and spatial variance in more detail.

Making waves: Establishing a modeling framework to evaluate novel targets for wastewater-based surveillance

Wastewater-based surveillance (WBS) monitoring of pathogens circulating within a community provides an improved understanding of the occurrence and spread of infectious diseases. However, the potential suitability of WBS for novel disease targets is unclear, including many emerging and neglected diseases. The current ad hoc approach of conducting wastewater detection experiments on novel disease targets to determine their suitability for WBS monitoring is resource intensive and may stall investment in this promising technology. In addition, detections, or non-detections, without the context of disease prevalence and shedding by infected individuals are difficult to interpret upon initial WBS target development. In this paper, we present a WBS feasibility analysis framework to identify which diseases are theoretically appropriate for WBS applications and to improve the initial interpretation of target detections. We then discuss five primary factors that influence the probability of detection in WBS monitoring - genome shedding rate, infection rate, per capita wastewater usage, process limit of detection (PLOD), and the number of PCR replicates. Clarifying the relationships between these factors and the likelihood of detection enhances quantitative insights into applying WBS, guiding researchers and stakeholders into mitigating inherent uncertainties of wastewater monitoring and subsequent improvements in WBS outcomes, thereby supporting future investment and expansion of WBS research, especially in novel disease targets.

Making waves: Integrating wastewater surveillance with dynamic modeling to track and predict viral outbreaks

Wastewater surveillance has proved to be a valuable tool to track the COVID-19 pandemic. However, most studies using wastewater surveillance data revolve around establishing correlations and lead time relative to reported case data. In this perspective, we advocate for the integration of wastewater surveillance data with dynamic within-host and between-host models to better understand, monitor, and predict viral disease outbreaks. Dynamic models overcome emblematic difficulties of using wastewater surveillance data such as establishing the temporal viral shedding profile. Complementarily, wastewater surveillance data bypasses the issues of time lag and underreporting in clinical case report data, thus enhancing the utility and applicability of dynamic models. The integration of wastewater surveillance data with dynamic models can enhance real-time tracking and prevalence estimation, forecast viral transmission and intervention effectiveness, and most importantly, provide a mechanistic understanding of infectious disease dynamics and the driving factors. Dynamic modeling of wastewater surveillance data will advance the development of a predictive and responsive monitoring system to improve pandemic preparedness and population health.

M. AH, Fernandez S, Huang AT, Vatanaprasan P, Jay DJ, Saethang T, Luk-in S, Storer RJ, Ounjai P, Devanga Ragupathi NK, Kanthawee P, Sano D, Furukawa T, Sei K, Leelahavanichkul A, Kanjanabuch T, Hirankarn N, Higgins PG, Kicic A, Singer AC, Chatsuwan T, Trowsdale S, Abe S, McLellan AD, Ishikawa H. COVID-19 monitoring with sparse sampling of sewered and non-sewered wastewater in urban and rural communities

Equitable SARS-CoV-2 surveillance in low-resource communities lacking centralized sewers is critical as wastewater-based epidemiology (WBE) progresses. However, large-scale studies on SARS-CoV-2 detection in wastewater from low-and middle-income countries is limited because of economic and technical reasons. In this study, wastewater samples were collected twice a month from 186 urban and rural subdistricts in nine provinces of Thailand mostly having decentralized and non-sewered sanitation infrastructure and analyzed for SARS-CoV-2 RNA variants using allele-specific RT-qPCR. Wastewater SARS-CoV-2 RNA concentration was used to estimate the real-time incidence and time-varying effective reproduction number (Re). Results showed an increase in SARS-CoV-2 RNA concentrations in wastewater from urban and rural areas 14-20 days earlier than infected individuals were officially reported. It also showed that community/food markets were "hot spots" for infected people. This approach offers an opportunity for early detection of transmission surges, allowing preparedness and potentially mitigating significant outbreaks at both spatial and temporal scales.

Targeting a free viral fraction enhances the early alert potential of wastewater surveillance for SARS-CoV-2: a methods comparison spanning the transition between delta and omicron variants in a large urban center

Introduction

Wastewater surveillance has proven to be a valuable approach to monitoring the spread of SARS-CoV-2, the virus that causes Coronavirus disease 2019 (COVID-19). Recognizing the benefits of wastewater surveillance as a tool to support public health in tracking SARS-CoV-2 and other respiratory pathogens, numerous wastewater virus sampling and concentration methods have been tested for appropriate applications as well as their significance for actionability by public health practices.

Methods

Here, we present a 34-week long wastewater surveillance study that covers nearly 4 million residents of the Detroit (MI, United States) metropolitan area. Three primary concentration methods were compared with respect to recovery of SARS-CoV-2 from wastewater: Virus Adsorption-Elution (VIRADEL), polyethylene glycol precipitation (PEG), and polysulfone (PES) filtration. Wastewater viral concentrations were normalized using various parameters (flow rate, population, total suspended solids) to account for variations in flow. Three analytical approaches were implemented to compare wastewater viral concentrations across the three primary concentration methods to COVID-19 clinical data for both normalized and non-normalized data: Pearson and Spearman correlations, Dynamic Time Warping (DTW), and Time Lagged Cross Correlation (TLCC) and peak synchrony.

Results

It was found that VIRADEL, which captures free and suspended virus from supernatant wastewater, was a leading indicator of COVID-19 cases within the region, whereas PEG and PES filtration, which target particle-associated virus, each lagged behind the early alert potential of VIRADEL. PEG and PES methods may potentially capture previously shed and accumulated SARS-CoV-2 resuspended from sediments in the interceptors.

Discussion

These results indicate that the VIRADEL method can be used to enhance the early-warning potential of wastewater surveillance applications although drawbacks include the need to process large volumes of wastewater to concentrate sufficiently free and suspended virus for detection. While lagging the VIRADEL method for early-alert potential, both PEG and PES filtration can be used for routine COVID-19 wastewater monitoring since they allow a large number of samples to be processed concurrently while being more cost-effective and with rapid turn-around yielding results same day as collection.

Monitoring SARS-CoV-2 variants in wastewater of Dhaka City, Bangladesh: approach to complement public health surveillance systems

BACKGROUND

Wastewater-based epidemiological surveillance has been considered a powerful tool for early detection and monitoring of the dynamics of SARS-CoV-2 and its lineages circulating in a community. This study is aimed to investigate the complexity of SARS-CoV-2 infection dynamics in Dhaka city by examining its genetic variants in wastewater. Also, the study seeks to determine a connection between the SARS-CoV-2 variations detected in clinical testing and those found in wastewater samples.

RESULTS

Out of 504 samples tested in RT-qPCR, 185 (36.7%) tested positive for SARS-CoV-2 viral RNA. The median log10 concentration of SARS-CoV-2 N gene copies/Liter of wastewater (gc/L) was 5.2, and the median log10 concentration of ORF1ab was 4.9. To further reveal the genetic diversity of SARS-CoV-2, ten samples with ORF1ab real-time RT-PCR cycle threshold (Ct) values ranging from 28.78 to 32.13 were subjected to whole genome sequencing using nanopore technology. According to clade classification, sequences from wastewater samples were grouped into 4 clades: 20A, 20B, 21A, 21J, and the Pango lineage, B.1, B.1.1, B.1.1.25, and B.1.617.2, with coverage ranging from 94.2 to 99.8%. Of them, 70% belonged to clade 20B, followed by 10% to clade 20A, 21A, and 21J. Lineage B.1.1.25 was predominant in Bangladesh and phylogenetically related to the sequences from India, the USA, Canada, the UK, and Italy. The Delta variant (B.1.617.2) was first identified in clinical samples at the beginning of May 2021. In contrast, we found that it was circulating in the community and was detected in wastewater in September 2020.

CONCLUSION

Environmental surveillance is useful for monitoring temporal and spatial trends of existing and emerging infectious diseases and supports evidence-based public health measures. The findings of this study supported the use of wastewater-based epidemiology and provided the baseline data for the dynamics of SARS-CoV-2 variants in the wastewater environment in Dhaka, Bangladesh.

Wastewater surveillance beyond COVID-19: a ranking system for communicable disease testing in the tri-county Detroit area, Michigan, USA

Introduction

Throughout the coronavirus disease 2019 (COVID-19) pandemic, wastewater surveillance has been utilized to monitor the disease in the United States through routine national, statewide, and regional monitoring projects. A significant canon of evidence was produced showing that wastewater surveillance is a credible and effective tool for disease monitoring. Hence, the application of wastewater surveillance can extend beyond monitoring SARS-CoV-2 to encompass a diverse range of emerging diseases. This article proposed a ranking system for prioritizing reportable communicable diseases (CDs) in the Tri-County Detroit Area (TCDA), Michigan, for future wastewater surveillance applications at the Great Lakes Water Authority's Water Reclamation Plant (GLWA's WRP).

Methods

The comprehensive CD wastewater surveillance ranking system (CDWSRank) was developed based on 6 binary and 6 quantitative parameters. The final ranking scores of CDs were computed by summing the multiplication products of weighting factors for each parameter, and then were sorted based on decreasing priority. Disease incidence data from 2014 to 2021 were collected for the TCDA. Disease incidence trends in the TCDA were endowed with higher weights, prioritizing the TCDA over the state of Michigan.

Results

Disparities in incidences of CDs were identified between the TCDA and state of Michigan, indicating epidemiological differences. Among 96 ranked CDs, some top ranked CDs did not present relatively high incidences but were prioritized, suggesting that such CDs require significant attention by wastewater surveillance practitioners, despite their relatively low incidences in the geographic area of interest. Appropriate wastewater sample concentration methods are summarized for the application of wastewater surveillance as per viral, bacterial, parasitic, and fungal pathogens.

Discussion

The CDWSRank system is one of the first of its kind to provide an empirical approach to prioritize CDs for wastewater surveillance, specifically in geographies served by centralized wastewater collection in the area of interest. The CDWSRank system provides a methodological tool and critical information that can help public health officials and policymakers allocate resources. It can be used to prioritize disease surveillance efforts and ensure that public health interventions are targeted at the most potentially urgent threats. The CDWSRank system can be easily adopted to geographical locations beyond the TCDA.

Wastewater-based modeling, reconstruction, and prediction for COVID-19 outbreaks in Hungary caused by highly immune evasive variants

(MOTIVATION)

Wastewater-based epidemiology (WBE) has emerged as a promising approach for monitoring the COVID-19 pandemic, since the measurement process is cost-effective and is exposed to fewer potential errors compared to other indicators like hospitalization data or the number of detected cases. Consequently, WBE was gradually becoming a key tool for epidemic surveillance and often the most reliable data source, as the intensity of clinical testing for COVID-19 drastically decreased by the third year of the pandemic. Recent results suggests that the model-based fusion of wastewater measurements with clinical data and other indicators is essential in future epidemic surveillance.

(METHOD)

In this work, we developed a wastewater-based compartmental epidemic model with a two-phase vaccination dynamics and immune evasion. We proposed a multi-step optimization-based data assimilation method for epidemic state reconstruction, parameter estimation, and prediction. The computations make use of the measured viral load in wastewater, the available clinical data (hospital occupancy, delivered vaccine doses, and deaths), the stringency index of the official social distancing rules, and other measures. The current state assessment and the estimation of the current transmission rate and immunity loss allow a plausible prediction of the future progression of the pandemic.

(RESULTS)

Qualitative and quantitative evaluations revealed that the contribution of wastewater data in our computational epidemiological framework makes predictions more reliable. Predictions suggest that at least half of the Hungarian population has lost immunity during the epidemic outbreak caused by the BA.1 and BA.2 subvariants of Omicron in the first half of 2022. We obtained a similar result for the outbreaks caused by the subvariant BA.5 in the second half of 2022.

(APPLICABILITY)

The proposed approach has been used to support COVID management in Hungary and could be customized for other countries as well.

Prediction of hospitalisations based on wastewater-based SARS-CoV-2 epidemiology

Wastewater-based epidemiology is widely applied in Austria since April 2020 to monitor the SARS-CoV-2 pandemic. With a steadily increasing number of monitored wastewater facilities, 123 plants covering roughly 70 % of the 9 million population were monitored as of August 2022. In this study, the SARS-CoV-2 viral concentrations in raw sewage were analysed to infer short-term hospitalisation occupancy. The temporal lead of wastewater-based epidemiological time series over hospitalisation occupancy levels facilitates the construction of forecast models. Data pre-processing techniques are presented, including the approach of comparing multiple decentralised wastewater signals with aggregated and centralised clinical data. Time‑lead quantification was performed using cross-correlation analysis and coefficient of determination optimisation approaches. Multivariate regression models were successfully applied to infer hospitalisation bed occupancy. The results show a predictive potential of viral loads in sewage towards Covid-19 hospitalisation occupancy, with an average lead time towards ICU and non-ICU bed occupancy between 14.8-17.7 days and 8.6-11.6 days, respectively. The presented procedure provides access to the trend and tipping point behaviour of pandemic dynamics and allows the prediction of short-term demand for public health services. The results showed an increase in forecast accuracy with an increase in the number of monitored wastewater treatment plants. Trained models are sensitive to changing variant types and require recalibration of model parameters, likely caused by immunity by vaccination and/or infection. The utilised approach displays a practical and rapidly implementable application of wastewater-based epidemiology to infer hospitalisation occupancy.

The dynamic relationship between COVID-19 cases and SARS-CoV-2 wastewater concentrations across time and space: Considerations for model training data sets

During the COVID-19 pandemic, wastewater-based surveillance has been used alongside diagnostic testing to monitor infection rates. With the decline in cases reported to public health departments due to at-home testing, wastewater data may serve as the primary input for epidemiological models, but training these models is not straightforward. We explored factors affecting noise and bias in the ratio between wastewater and case data collected in 26 sewersheds in California from October 2020 to March 2022. The strength of the relationship between wastewater and case data appeared dependent on sampling frequency and population size, but was not increased by wastewater normalization to flow rate or case count normalization to testing rates. Additionally, the lead and lag times between wastewater and case data varied over time and space, and the ratio of log-transformed individual cases to wastewater concentrations changed over time. This ratio decreased between the Epsilon/Alpha and Delta variant surges of COVID-19 and increased during the Omicron BA.1 variant surge, and was also related to the diagnostic testing rate. Based on this analysis, we present a framework of scenarios describing the dynamics of the case to wastewater ratio to aid in data handling decisions for ongoing modeling efforts.

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.

Simple methods for early warnings of COVID-19 surges: Lessons learned from 21 months of wastewater and clinical data collection in Detroit, Michigan, United States

Wastewater-based epidemiology (WBE) has drawn great attention since the Coronavirus disease 2019 (COVID-19) pandemic, not only due to its capability to circumvent the limitations of traditional clinical surveillance, but also due to its potential to forewarn fluctuations of disease incidences in communities. One critical application of WBE is to provide "early warnings" for upcoming fluctuations of disease incidences in communities which traditional clinical testing is incapable to achieve. While intricate models have been developed to determine early warnings based on wastewater surveillance data, there is an exigent need for straightforward, rapid, broadly applicable methods for health departments and partner agencies to implement. Our purpose in this study is to develop and evaluate such early-warning methods and clinical-case peak-detection methods based on WBE data to mount an informed public health response. Throughout an extended wastewater surveillance period across Detroit, MI metropolitan area (the entire study period is from September 2020 to May 2022) we designed eight early-warning methods (three real-time and five post-factum). Additionally, we designed three peak-detection methods based on clinical epidemiological data. We demonstrated the utility of these methods for providing early warnings for COVID-19 incidences, with their counterpart accuracies evaluated by hit rates. "Hit rates" were defined as the number of early warning dates (using wastewater surveillance data) that captured defined peaks (using clinical epidemiological data) divided by the total number of early warning dates. Hit rates demonstrated that the accuracy of both real-time and post-factum methods could reach 100 %. Furthermore, the results indicate that the accuracy was influenced by approaches to defining peaks of disease incidence. The proposed methods herein can assist health departments capitalizing on WBE data to assess trends and implement quick public health responses to future epidemics. Besides, this study elucidated critical factors affecting early warnings based on WBE amid the COVID-19 pandemic.

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.

SARS-CoV-2 raw wastewater surveillance from student residences on an urban university campus

The COVID-19 pandemic brought about an urgent need to monitor the community prevalence of infection and detect the presence of SARS-CoV-2. Testing individual people is the most reliable method to measure the spread of the virus in any given community, but it is also the most expensive and time-consuming. Wastewater-based epidemiology (WBE) has been used since the 1960s when scientists implemented monitoring to measure the effectiveness of the Polio vaccine. Since then, WBE has been used to monitor populations for various pathogens, drugs, and pollutants. In August 2020, the University of Tennessee-Knoxville implemented a SARS-CoV-2 surveillance program that began with raw wastewater surveillance of the student residence buildings on campus, the results of which were shared with another lab group on campus that oversaw the pooled saliva testing of students. Sample collection began at 8 am, and the final RT-qPCR results were obtained by midnight. The previous day's results were presented to the campus administrators and the Student Health Center at 8 am the following morning. The buildings surveyed included all campus dormitories, fraternities, and sororities, 46 buildings in all representing an on-campus community of over 8,000 students. The WBE surveillance relied upon early morning "grab" samples and 24-h composite sampling. Because we only had three Hach AS950 Portable Peristaltic Sampler units, we reserved 24-h composite sampling for the dormitories with the highest population of students. Samples were pasteurized, and heavy sediment was centrifuged and filtered out, followed by a virus concentration step before RNA extraction. Each sample was tested by RT-qPCR for the presence of SARS-CoV-2, using the CDC primers for N Capsid targets N1 and N3. The subsequent pooled saliva tests from sections of each building allowed lower costs and minimized the total number of individual verification tests that needed to be analyzed by the Student Health Center. Our WBE results matched the trend of the on-campus cases reported by the student health center. The highest concentration of genomic copies detected in one sample was 5.06 × 10⁷ copies/L. Raw wastewater-based epidemiology is an efficient, economical, fast, and non-invasive method to monitor a large community for a single pathogen or multiple pathogen targets.

Early detection of local SARS-CoV-2 outbreaks by wastewater surveillance: a feasibility study

Wastewater surveillance and quantitative analysis of SARS-CoV-2 RNA are increasingly used to monitor the spread of COVID-19 in the community. We studied the feasibility of applying the surveillance data for early detection of local outbreaks. A Monte Carlo simulation model was constructed, applying data on reported variation in RNA gene copy concentration in faeces and faecal masses shed. It showed that, even with a constant number of SARS-CoV-2 RNA shedders, the variation in concentrations found in wastewater samples will be large, and that it will be challenging to translate viral concentrations into incidence estimates, especially when the number of shedders is low. Potential signals for early detection of hypothetical outbreaks were analysed for their performance in terms of sensitivity and specificity of the signals. The results suggest that a sudden increase in incidence is not easily identified on the basis of wastewater surveillance data, especially in small sampling areas and in low-incidence situations. However, with a high number of shedders and when combining data from multiple consecutive tests, the performance of wastewater sampling is expected to improve considerably. The developed modelling approach can increase our understanding of the results from wastewater surveillance of SARS-CoV-2.

Variant-specific SARS-CoV-2 shedding rates in wastewater

Previous studies show that SARS-CoV-2 waste shedding rates vary by community and are influenced by multiple factors; however, differences in shedding rates across multiple variants have yet to be evaluated. The purpose of this work is to build on previous research that evaluated waste shedding rates for early SARS-CoV-2 and the Delta variant, and update population level waste shedding rates for the more-recent Omicron variant in six communities. Mean SARS-CoV-2 waste shedding rates were found to increase with the predominance of the Delta variant and subsequently decrease with Omicron infections. Interestingly, the Delta stage had the highest mean shedding rates and was associated with the most severe disease symptoms reported in other clinical studies, while Omicron, exhibiting reduced symptoms, had the lowest mean shedding rates. Additionally, shedding rates were most consistent across communities during the Omicron stage. This is the first paper to identify waste shedding rates specific to the Omicron variant and fills a knowledge gap critical to disease prevalence modeling.

Monkeypox outbreak: Wastewater and environmental surveillance perspective

Monkeypox disease (MPXD), a viral disease caused by the monkeypox virus (MPXV), is an emerging zoonotic disease endemic in some countries of Central and Western Africa but seldom reported outside the affected region. Since May 2022, MPXD has been reported at least in 74 countries globally, prompting the World Health Organization to declare the MPXD outbreak a Public Health Emergency of International Concern. As of July 24, 2022; 92 % (68/74) of the countries with reported MPXD cases had no historical MPXD case reports. From the One Health perspective, the spread of MPXV in the environment poses a risk not only to humans but also to small mammals and may, ultimately, spread to potent novel host populations. Wastewater-based surveillance (WBS) has been extensively utilized to monitor communicable diseases, particularly during the ongoing COVID-19 pandemic. It helped in monitoring infectious disease caseloads as well as specific viral variants circulating in communities. The detection of MPXV DNA in lesion materials (e.g. skin, vesicle fluid, crusts), skin rashes, and various body fluids, including respiratory and nasal secretions, saliva, urine, feces, and semen of infected individuals, supports the possibility of using WBS as an early proxy for the detection of MPXV infections. WBS of MPXV DNA can be used to monitor MPXV activity/trends in sewerage network areas even before detecting laboratory-confirmed clinical cases within a community. However, several factors affect the detection of MPXV in wastewater including, but not limited to, routes and duration time of virus shedding by infected individuals, infection rates in the relevant affected population, environmental persistence, the processes and analytical sensitivity of the used methods. Further research is needed to identify the key factors that impact the detection of MPXV biomarkers in wastewater and improve the utility of WBS of MPXV as an early warning and monitoring tool for safeguarding human health. In this review, we shortly summarize aspects of the MPXV outbreak relevant to wastewater monitoring and discuss the challenges associated with WBS.

Does normalization of SARS-CoV-2 concentrations by Pepper Mild Mottle Virus improve correlations and lead time between wastewater surveillance and clinical data in Alberta (Canada): comparing twelve SARS-CoV-2 normalization approaches

Wastewater-based surveillance (WBS) data normalization is an analyte measurement correction that addresses variations resulting from dilution of fecal discharge by non-sanitary sewage, stormwater or groundwater infiltration. No consensus exists on what WBS normalization parameters result in the strongest correlations and lead time between SARS-CoV-2 WBS data and COVID-19 cases. This study compared flow, population size and biomarker normalization impacts on the correlations and lead times for ten communities in twelve sewersheds in Alberta (Canada) between September 2020 and October 2021 (n = 1024) to determine if normalization by Pepper Mild Mottle Virus (PMMoV) provides any advantages compared to other normalization parameters (e.g., flow, reported and dynamic population sizes, BOD, TSS, NH3, TP). PMMoV concentrations (GC/mL) corresponded with plant influent flows and were highest in the urban centres. SARS-CoV-2 target genes E, N1 and N2 were all negatively associated with wastewater influent pH, while PMMoV was positively associated with temperature. Pooled data analysis showed that normalization increased ρ-values by almost 0.1 and was highest for ammonia, TKN and TP followed by PMMoV. Normalization by other parameters weakened associations. None of the differences were statistically significant. Site-specific correlations showed that normalization of SARS-CoV-2 data by PMMoV only improved correlations significantly in two of the twelve systems; neither were large sewersheds or combined sewer systems. In five systems, normalization by traditional wastewater strength parameters and dynamic population estimates improved correlations. Lead time ranged between 1 and 4 days in both pooled and site-specific comparisons. We recommend that WBS researchers and health departments: a) Investigate WWTP influent properties (e.g., pH) in the WBS planning phase and use at least two parallel approaches for normalization only if shown to provide value; b) Explore normalization by wastewater strength parameters and dynamic population size estimates further; and c) Evaluate purchasing an influent flow meter in small communities to support long-term WBS efforts and WWTP management.

Not a waste: Wastewater surveillance to enhance public health

Domestic wastewater, when collected and evaluated appropriately, can provide valuable health-related information for a community. As a relatively unbiased and non-invasive approach, wastewater surveillance may complement current practices towards mitigating risks and protecting population health. Spurred by the COVID-19 pandemic, wastewater programs are now widely implemented to monitor viral infection trends in sewersheds and inform public health decision-making. This review summarizes recent developments in wastewater-based epidemiology for detecting and monitoring communicable infectious diseases, dissemination of antimicrobial resistance, and illicit drug consumption. Wastewater surveillance, a quickly advancing Frontier in environmental science, is becoming a new tool to enhance public health, improve disease prevention, and respond to future epidemics and pandemics.

Research progress of electrochemical oxidation and self-action of electric field for medical wastewater treatment

A large number of pathogenic microorganisms exist in medical wastewater, which could invade the human body through the water and cause harm to human health. With the global pandemic coronavirus (COVID-19), public health safety become particularly important, and medical wastewater treatment is an important part of it. In particular, electrochemical disinfection technology has been widely studied in medical wastewater treatment due to its greenness, high efficiency, convenient operation, and other advantages. In this paper, the development status of electrochemical disinfection technology in the treatment of medical wastewater is reviewed, and an electrochemical three-stage disinfection system is proposed for the treatment of medical wastewater. Moreover, prospects for the electrochemical treatment of medical wastewater will be presented. It is hoped that this review could provide insight and guidance for the research and application of electrochemical disinfection technology to treat medical wastewater.GRAPHICAL ABSTRACT.

Timing and Trends for Municipal Wastewater, Lab-Confirmed Case, and Syndromic Case Surveillance of COVID-19 in Raleigh, North Carolina

Objectives. To compare 4 COVID-19 surveillance metrics in a major metropolitan area. Methods. We analyzed severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) RNA in wastewater influent and primary solids in Raleigh, North Carolina, from April 10 through December 13, 2020. We compared wastewater results with lab-confirmed COVID-19 cases and syndromic COVID-like illness (CLI) cases to answer 3 questions: (1) Did they correlate? (2) What was the temporal alignment of the different surveillance systems? (3) Did periods of significant change (i.e., trends) align? Results. In the Raleigh sewershed, wastewater influent, wastewater primary solids, lab-confirmed cases, and CLI were strongly or moderately correlated. Trends in lab-confirmed cases and wastewater influent were observed earlier, followed by CLI and, lastly, wastewater primary solids. All 4 metrics showed sustained increases in COVID-19 in June, July, and November 2020 and sustained decreases in August and September 2020. Conclusions. In a major metropolitan area in 2020, the timing of and trends in municipal wastewater, lab-confirmed case, and syndromic case surveillance of COVID-19 were in general agreement. Public Health Implications. Our results provide evidence for investment in SARS-CoV-2 wastewater and CLI surveillance to complement information provided through lab-confirmed cases. (Am J Public Health. 2023;113(1):79-88. https://doi.org/10.2105/AJPH.2022.307108).

Wastewater pandemic preparedness: Toward an end-to-end pathogen monitoring program

Molecular analysis of public wastewater has great potential as a harbinger for community health and health threats. Long-used to monitor the presence of enteric viruses, in particular polio, recent successes of wastewater as a reliable lead indicator for trends in SARS-CoV-2 levels and hospital admissions has generated optimism and emerging evidence that similar science can be applied to other pathogens of pandemic potential (PPPs), especially respiratory viruses and their variants of concern (VOC). However, there are substantial challenges associated with implementation of this ideal, namely that multiple and distinct fields of inquiry must be bridged and coordinated. These include engineering, molecular sciences, temporal-geospatial analytics, epidemiology and medical, and governmental and public health messaging, all of which present their own caveats. Here, we outline a framework for an integrated, state-wide, end-to-end human pathogen monitoring program using wastewater to track viral PPPs.

SARS-CoV-2 RNA Is Readily Detectable at Least 8 Months after Shedding in an Isolation Facility

Environmental monitoring of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) for research and public health purposes has grown exponentially throughout the coronavirus disease 2019 (COVID-19) pandemic. Monitoring wastewater for SARS-CoV-2 provides early warning signals of virus spread and information on trends in infections at a community scale. Indoor environmental monitoring (e.g., swabbing of surfaces and air filters) to identify potential outbreaks is less common, and the evidence for its utility is mixed. A significant challenge with surface and air filter monitoring in this context is the concern of "relic RNA," noninfectious RNA found in the environment that is not from recently deposited virus. Here, we report detection of SARS-CoV-2 RNA on surfaces in an isolation unit (a university dorm room) for up to 8 months after a COVID-19-positive individual vacated the space. Comparison of sequencing results from the same location over two time points indicated the presence of the entire viral genome, and sequence similarity confirmed a single source of the virus. Our findings highlight the need to develop approaches that account for relic RNA in environmental monitoring. IMPORTANCE Environmental monitoring of SARS-CoV-2 is rapidly becoming a key tool in infectious disease research and public health surveillance. Such monitoring offers a complementary and sometimes novel perspective on population-level incidence dynamics relative to that of clinical studies by potentially allowing earlier, broader, more affordable, less biased, and less invasive identification. Environmental monitoring can assist public health officials and others when deploying resources to areas of need and provides information on changes in the pandemic over time. Environmental surveillance of the genetic material of infectious agents (RNA and DNA) in wastewater became widely applied during the COVID-19 pandemic. There has been less research on other types of environmental samples, such as surfaces, which could be used to indicate that someone in a particular space was shedding virus. One challenge with surface surveillance is that the noninfectious genetic material from a pathogen (e.g., RNA from SARS-CoV-2) may be detected in the environment long after an infected individual has left the space. This study aimed to determine how long SARS-CoV-2 RNA could be detected in a room after a COVID-positive person had been housed there.

Wastewater to clinical case (WC) ratio of COVID-19 identifies insufficient clinical testing, onset of new variants of concern and population immunity in urban communities

Clinical testing has been the cornerstone of public health monitoring and infection control efforts in communities throughout the COVID-19 pandemic. With the anticipated reduction of clinical testing as the disease moves into an endemic state, SARS-CoV-2 wastewater surveillance (WWS) will have greater value as an important diagnostic tool. An in-depth analysis and understanding of the metrics derived from WWS is required to interpret and utilize WWS-acquired data effectively (McClary-Gutierrez et al., 2021; O'Keeffe, 2021). In this study, the SARS-CoV-2 wastewater signal to clinical cases (WC) ratio was investigated across seven cities in Canada over periods ranging from 8 to 21 months. This work demonstrates that significant increases in the WC ratio occurred when clinical testing eligibility was modified to appointment-only testing, identifying a period of insufficient clinical testing (resulting in a reduction to testing access and a reduction in the number of daily tests) in these communities, despite increases in the wastewater signal. Furthermore, the WC ratio decreased significantly in 6 of the 7 studied locations, serving as a potential signal of the emergence of the Alpha variant of concern (VOC) in a relatively non-immunized community (40-60 % allelic proportion), while a more muted decrease in the WC ratio signaled the emergence of the Delta VOC in a relatively well-immunized community (40-60 % allelic proportion). Finally, a significant decrease in the WC ratio signaled the emergence of the Omicron VOC, likely because of the variant's greater effectiveness at evading immunity, leading to a significant number of new reported clinical cases, even when community immunity was high. The WC ratio, used as an additional monitoring metric, could complement clinical case counts and wastewater signals as individual metrics in its potential ability to identify important epidemiological occurrences, adding value to WWS as a diagnostic technology during the COVID-19 pandemic and likely for future pandemics.

VirPool: model-based estimation of SARS-CoV-2 variant proportions in wastewater samples

BACKGROUND

The genomes of SARS-CoV-2 are classified into variants, some of which are monitored as variants of concern (e.g. the Delta variant B.1.617.2 or Omicron variant B.1.1.529). Proportions of these variants circulating in a human population are typically estimated by large-scale sequencing of individual patient samples. Sequencing a mixture of SARS-CoV-2 RNA molecules from wastewater provides a cost-effective alternative, but requires methods for estimating variant proportions in a mixed sample.

RESULTS

We propose a new method based on a probabilistic model of sequencing reads, capturing sequence diversity present within individual variants, as well as sequencing errors. The algorithm is implemented in an open source Python program called VirPool. We evaluate the accuracy of VirPool on several simulated and real sequencing data sets from both Illumina and nanopore sequencing platforms, including wastewater samples from Austria and France monitoring the onset of the Alpha variant.

CONCLUSIONS

VirPool is a versatile tool for wastewater and other mixed-sample analysis that can handle both short- and long-read sequencing data. Our approach does not require pre-selection of characteristic mutations for variant profiles, it is able to use the entire length of reads instead of just the most informative positions, and can also capture haplotype dependencies within a single read.

Surveillance of SARS-CoV-2 in nine neighborhood sewersheds in Detroit Tri-County area, United States: Assessing per capita SARS-CoV-2 estimations and COVID-19 incidence

Wastewater-based epidemiology (WBE) has been suggested as a useful tool to predict the emergence and investigate the extent of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). In this study, we screened appropriate population biomarkers for wastewater SARS-CoV-2 normalization and compared the normalized SARS-CoV-2 values across locations with different demographic characteristics in southeastern Michigan. Wastewater samples were collected between December 2020 and October 2021 from nine neighborhood sewersheds in the Detroit Tri-County area. Using reverse transcriptase droplet digital polymerase chain reaction (RT-ddPCR), concentrations of N1 and N2 genes in the studied sites were quantified, with N1 values ranging from 1.92 × 10² genomic copies/L to 6.87 × 10³ gc/L and N2 values ranging from 1.91 × 10² gc/L to 6.45 × 10³ gc/L. The strongest correlations were observed with between cumulative COVID-19 cases per capita (referred as COVID-19 incidences thereafter), and SARS-CoV-2 concentrations normalized by total Kjeldahl nitrogen (TKN), creatinine, 5-hydroxyindoleacetic acid (5-HIAA) and xanthine when correlating the per capita SARS-CoV-2 and COVID-19 incidences. When SARS-CoV-2 concentrations in wastewater were normalized and compared with COVID-19 incidences, the differences between neighborhoods of varying demographics were reduced as compared to differences observed when comparing non-normalized SARS-CoV-2 with COVID-19 cases. This indicates when studying the disease burden in communities of different demographics, accurate per capita estimation is of great importance. The study suggests that monitoring selected water quality parameters or biomarkers, along with RNA concentrations in wastewater, will allow adequate data normalization for spatial comparisons, especially in areas where detailed sanitary sewage flows and contributing populations in the catchment areas are not available. This opens the possibility of using WBE to assess community infections in rural areas or the developing world where the contributing population of a sample could be unknown.

Comparison of RT-qPCR and RT-dPCR Platforms for the Trace Detection of SARS-CoV-2 RNA in Wastewater

We compared reverse transcription-quantitative polymerase chain reaction (RT-qPCR) and RT digital PCR (RT-dPCR) platforms for the trace detection of SARS-CoV-2 RNA in low-prevalence COVID-19 locations in Queensland, Australia, using CDC N1 and CDC N2 assays. The assay limit of detection (ALOD), PCR inhibition rates, and performance characteristics of each assay, along with the positivity rates with the RT-qPCR and RT-dPCR platforms, were evaluated by seeding known concentrations of exogenous SARS-CoV-2 in wastewater. The ALODs using RT-dPCR were approximately 2-5 times lower than those using RT-qPCR. During sample processing, the endogenous (n = 96) and exogenous (n = 24) SARS-CoV-2 wastewater samples were separated, and RNA was extracted from both wastewater eluates and pellets (solids). The RT-dPCR platform demonstrated a detection rate significantly greater than that of RT-qPCR for the CDC N1 and CDC N2 assays in the eluate (N1, p = 0.0029; N2, p = 0.0003) and pellet (N1, p = 0.0015; N2, p = 0.0067) samples. The positivity results also indicated that for the analysis of SARS-CoV-2 RNA in wastewater, including the eluate and pellet samples may further increase the detection sensitivity using RT-dPCR.

Rapid Implementation of High-Frequency Wastewater Surveillance of SARS-CoV-2

There have been over 507 million cases of COVID-19, the disease caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), resulting in 6 million deaths globally. Wastewater surveillance has emerged as a valuable tool in understanding SARS-CoV-2 burden in communities. The National Wastewater Surveillance System (NWSS) partnered with the United States Geological Survey (USGS) to implement a high-frequency sampling program. This report describes basic surveillance and sampling statistics as well as a comparison of SARS-CoV-2 trends between high-frequency sampling 3-5 times per week, referred to as USGS samples, and routine sampling 1-2 times per week, referred to as NWSS samples. USGS samples provided a more nuanced impression of the changes in wastewater trends, which could be important in emergency response situations. Despite the rapid implementation time frame, USGS samples had similar data quality and testing turnaround times as NWSS samples. Ensuring there is a reliable sample collection and testing plan before an emergency arises will aid in the rapid implementation of a high-frequency sampling approach. High-frequency sampling requires a constant flow of information and supplies throughout sample collection, testing, analysis, and data sharing. High-frequency sampling may be a useful approach for increased resolution of disease trends in emergency response.

Subsewershed SARS-CoV-2 Wastewater Surveillance and COVID-19 Epidemiology Using Building-Specific Occupancy and Case Data

To evaluate the use of wastewater-based surveillance and epidemiology to monitor and predict SARS-CoV-2 virus trends, over the 2020-2021 academic year we collected wastewater samples twice weekly from 17 manholes across Virginia Tech's main campus. We used data from external door swipe card readers and student isolation/quarantine status to estimate building-specific occupancy and COVID-19 case counts at a daily resolution. After analyzing 673 wastewater samples using reverse transcription quantitative polymerase chain reaction (RT-qPCR), we reanalyzed 329 samples from isolation and nonisolation dormitories and the campus sewage outflow using reverse transcription digital droplet polymerase chain reaction (RT-ddPCR). Population-adjusted viral copy means from isolation dormitory wastewater were 48% and 66% higher than unadjusted viral copy means for N and E genes (1846/100 mL to 2733/100 mL/100 people and 2312/100 mL to 3828/100 mL/100 people, respectively; n = 46). Prespecified analyses with random-effects Poisson regression and dormitory/cluster-robust standard errors showed that the detection of N and E genes were associated with increases of 85% and 99% in the likelihood of COVID-19 cases 8 days later (incident-rate ratio (IRR) = 1.845, p = 0.013 and IRR = 1.994, p = 0.007, respectively; n = 215), and one-log increases in swipe card normalized viral copies (copies/100 mL/100 people) for N and E were associated with increases of 21% and 27% in the likelihood of observing COVID-19 cases 8 days following sample collection (IRR = 1.206, p < 0.001, n = 211 for N; IRR = 1.265, p < 0.001, n = 211 for E). One-log increases in swipe normalized copies were also associated with 40% and 43% increases in the likelihood of observing COVID-19 cases 5 days after sample collection (IRR = 1.403, p = 0.002, n = 212 for N; IRR = 1.426, p < 0.001, n = 212 for E). Our findings highlight the use of building-specific occupancy data and add to the evidence for the potential of wastewater-based epidemiology to predict COVID-19 trends at subsewershed scales.

RNA in Municipal Wastewater Reveals Magnitudes of COVID-19 Outbreaks across Four Waves Driven by SARS-CoV-2 Variants of Concern

There are no standardized protocols for quantifying severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in wastewater to date, especially for population normalization. Here, a pipeline was developed, applied, and assessed to quantify SARS-CoV-2 and key variants of concern (VOCs) RNA in wastewater at Saskatoon, Canada. Normalization approaches using recovery ratio and extraction efficiency, wastewater parameters, or population indicators were assessed by comparing to daily numbers of new cases. Viral load was positively correlated with daily new cases reported in the sewershed. Wastewater surveillance (WS) had a lead time of approximately 7 days, which indicated surges in the number of new cases. WS revealed the variant α and δ driving the third and fourth wave, respectively. The adjustment with the recovery ratio and extraction efficiency improved the correlation between viral load and daily new cases. Normalization of viral concentration to concentrations of the artificial sweetener acesulfame K improved the trend of viral load during the Christmas and New Year holidays when populations were dynamic and variable. Acesulfame K performed better than pepper mild mottle virus, creatinine, and ammonia for population normalization. Hence, quality controls to characterize recovery ratios and extraction efficiencies and population normalization with acesulfame are promising for precise WS programs supporting decision-making in public health.

Quantitative Reverse Transcription PCR Surveillance of SARS-CoV-2 Variants of Concern in Wastewater of Two Counties in Texas, United States

After its emergence in late November/December 2019, the severe acute respiratory syndrome coronavirus 2 virus (SARS-CoV-2) rapidly spread globally. Recognizing that this virus is shed in feces of individuals and that viral RNA is detectable in wastewater, testing for SARS-CoV-2 in sewage collections systems has allowed for the monitoring of a community's viral burden. Over a 9 month period, the influents of two regional wastewater treatment facilities were concurrently examined for wild-type SARS-CoV-2 along with variants B.1.1.7 and B.1.617.2 incorporated as they emerged. Epidemiological data including new confirmed COVID-19 cases and associated hospitalizations and fatalities were tabulated within each location. RNA from SARS-CoV-2 was detectable in 100% of the wastewater samples, while variant detection was more variable. Quantitative reverse transcription PCR (RT-qPCR) results align with clinical trends for COVID-19 cases, and increases in COVID-19 cases were positively related with increases in SARS-CoV-2 RNA load in wastewater, although the strength of this relationship was location specific. Our observations demonstrate that clinical and wastewater surveillance of SARS-CoV-2 wild type and constantly emerging variants of concern can be combined using RT-qPCR to characterize population infection dynamics. This may provide an early warning for at-risk communities and increases in COVID-19 related hospitalizations.

Application of machine learning for multi-community COVID-19 outbreak predictions with wastewater surveillance

The potential of wastewater-based epidemiology (WBE) as a surveillance and early warning tool for the COVID-19 outbreak has been demonstrated. For areas with limited testing capacity, wastewater surveillance can provide information on the disease dynamic at a community level. A predictive model is a key to generating quantitative estimates of the infected population. Modeling longitudinal wastewater data can be challenging as biomarkers in wastewater are susceptible to variations caused by multiple factors associated with the wastewater matrix and the sewersheds characteristics. As WBE is an emerging trend, the model should be able to address the uncertainties of wastewater from different sewersheds. We proposed exploiting machine learning and deep learning techniques, which are supported by the growing WBE data. In this article, we reviewed the existing predictive models, among which the emerging machine learning/deep learning models showed great potential. However, most models are built for individual sewersheds with few features extracted from the wastewater. To fulfill the research gap, we compared different time-series and non-time-series models for their short-term predictive performance of COVID-19 cases in 9 diverse sewersheds. The time-series models, long short-term memory (LSTM) and Prophet, outcompeted the non-time-series models. Besides viral (SARS-CoV-2) loads and location identity, domain-specific features like biochemical parameters of wastewater, geographical parameters of the sewersheds, and some socioeconomic parameters of the communities can contribute to the models. With proper feature engineering and hyperparameter tuning, we believe machine learning models like LSTM can be a feasible solution for the COVID-19 trend prediction via WBE. Overall, this is a proof-of-concept study on the application of machine learning in COVID-19 WBE. Future studies are needed to deploy and maintain the model in more real-world applications.

Implementing wastewater surveillance for SARS-CoV-2 on a university campus: Lessons learned

Wastewater surveillance, also known as wastewater-based epidemiology (WBE), has been successfully used to detect SARS-CoV-2 and other viruses in sewage in many locations in the United States and globally. This includes implementation of the surveillance on college and university campuses. A two-phase study was conducted during the 2020-2021 academic year to test the feasibility of a WBE system on campus and to supplement the clinical COVID-19 testing performed for the student, staff, and faculty body. The primary objective during the Fall 2020 semester was to monitor a large portion of the on-campus population and to obtain an understanding of the spreading of the SARS-CoV-2 virus. The Spring 2021 objective was focused on selected residence halls and groups of residents on campus, as this was more efficient and relevant for an effective follow-up response. Logistical problems and planning oversights initially occurred but were corrected with improved communication and experience. Many lessons were learned, including effective mapping, site planning, communication, personnel organization, and equipment management, and obtained along the way, thereby paving an opportune guide for future planning efforts. PRACTITIONER POINTS: WBE was successful in the detection of many SARS-CoV-2 variants incl. Alpha, Beta, Gamma, Delta, Lambda, Mu, and Omicron. Careful planning and contingencies were essential for a successful implementation of a SARS-CoV-2 monitoring program. A surveillance program may be important for detection and monitoring of other public health relevant targets in wastewater incl. bacteria, viruses, fungi and viruses. Diverse lessons were learned incl. effective mapping, site planning, communication, personnel organization, and equipment management, thereby providing a guide for future planning efforts.

Five-week warning of COVID-19 peaks prior to the Omicron surge in Detroit, Michigan using wastewater surveillance

Wastewater-based epidemiology (WBE) is useful in predicting temporal fluctuations of COVID-19 incidence in communities and providing early warnings of pending outbreaks. To investigate the relationship between SARS-CoV-2 concentrations in wastewater and COVID-19 incidence in communities, a 12-month study between September 1, 2020, and August 31, 2021, prior to the Omicron surge, was conducted. 407 untreated wastewater samples were collected from the Great Lakes Water Authority (GLWA) in southeastern Michigan. N1 and N2 genes of SARS-CoV-2 were quantified using RT-ddPCR. Daily confirmed COVID-19 cases for the City of Detroit, and Wayne, Macomb, Oakland counties between September 1, 2020, and October 4, 2021, were collected from a public data source. The total concentrations of N1 and N2 genes ranged from 714.85 to 7145.98 gc/L and 820.47 to 6219.05 gc/L, respectively, which were strongly correlated with the 7-day moving average of total daily COVID-19 cases in the associated areas, after 5 weeks of the viral measurement. The results indicate a potential 5-week lag time of wastewater surveillance preceding COVID-19 incidence for the Detroit metropolitan area. Four statistical models were established to analyze the relationship between SARS-CoV-2 concentrations in wastewater and COVID-19 incidence in the study areas. Under a 5-week lag time scenario with both N1 and N2 genes, the autoregression model with seasonal patterns and vector autoregression model were more effective in predicting COVID-19 cases during the study period. To investigate the impact of flow parameters on the correlation, the original N1 and N2 gene concentrations were normalized by wastewater flow parameters. The statistical results indicated the optimum models were consistent for both normalized and non-normalized data. In addition, we discussed parameters that explain the observed lag time. Furthermore, we evaluated the impact of the omicron surge that followed, and the impact of different sampling methods on the estimation of lag time.