Wastewater Surveillance for Infectious Disease: A Systematic Review

Key considerations for pathogen surveillance in wastewater

Wastewater surveillance (WWS) has received significant attention as a rapid, sensitive, and cost-effective tool for monitoring various pathogens in a community. WWS is employed to assess the spatial and temporal trends of diseases and identify their early appearances and reappearances, as well as to detect novel and mutated variants. However, the shedding rates of pathogens vary significantly depending on factors such as disease severity, the physiology of affected individuals, and the characteristics of pathogen. Furthermore, pathogens may exhibit differential fate and decay kinetics in the sewerage system. Variable shedding rates and decay kinetics may affect the detection of pathogens in wastewater. This may influence the interpretation of results and the conclusions of WWS studies. When selecting a pathogen for WWS, it is essential to consider it's specific characteristics. If data are not readily available, factors such as fate, decay, and shedding rates should be assessed before conducting surveillance. Alternatively, these factors can be compared to those of similar pathogens for which such data are available.

Infectious Disease Physicians' Knowledge and Practices Regarding Wastewater Surveillance, United States, 2024

A survey of US infectious disease physicians indicated that few regularly reviewed wastewater surveillance (WWS) data but many reported examples of how WWS has affected or could affect their clinical practice. WWS data can be useful for physicians, but increased communication between public health professionals and physicians regarding WWS could improve its utility.

Wastewater Surveillance for Influenza A Virus and H5 Subtype Concurrent with the Highly Pathogenic Avian Influenza A(H5N1) Virus Outbreak in Cattle and Poultry and Associated Human Cases - United States, May 12-July 13, 2024

As part of the response to the highly pathogenic avian influenza A(H5N1) virus outbreak in U.S. cattle and poultry and the associated human cases, CDC and partners are monitoring influenza A virus levels and detection of the H5 subtype in wastewater. Among 48 states and the District of Columbia that performed influenza A testing of wastewater during May 12-July 13, 2024, a weekly average of 309 sites in 38 states had sufficient data for analysis, and 11 sites in four states reported high levels of influenza A virus. H5 subtype testing was conducted at 203 sites in 41 states, with H5 detections at 24 sites in nine states. For each detection or high level, CDC and state and local health departments evaluated data from other influenza surveillance systems and partnered with wastewater utilities and agriculture departments to investigate potential sources. Among the four states with high influenza A virus levels detected in wastewater, three states had corresponding evidence of human influenza activity from other influenza surveillance systems. Among the 24 sites with H5 detections, 15 identified animal sources within the sewershed or adjacent county, including eight milk-processing inputs. Data from these early investigations can help health officials optimize the use of wastewater surveillance during the upcoming respiratory illness season.

Harnessing non-standard nucleic acids for highly sensitive icosaplex (20-plex) detection of microbial threats

Environmental surveillance and clinical diagnostics heavily rely on the polymerase chain reaction (PCR) for target detection. A growing list of microbial threats warrants new PCR-based detection methods that are highly sensitive, specific, and multiplexable. Here, we introduce a PCR-based icosaplex (20-plex) assay for detecting 18 enteropathogen and two antimicrobial resistance genes. This multiplexed PCR assay leverages the self-avoiding molecular recognition system (SAMRS) to avoid primer dimer formation, the artificially expanded genetic information system (AEGIS) for amplification specificity, and next-generation sequencing for amplicon identification. We benchmarked this assay using a low-cost, portable sequencing platform (Oxford Nanopore) on wastewater, soil, and human stool samples. Using parallelized multi-target TaqMan Array Cards (TAC) to benchmark performance of the 20-plex assay, there was 74% agreement on positive calls and 97% agreement on negative calls. Additionally, we show how sequencing information from the 20-plex can be used to further classify allelic variants of genes and distinguish sub-species. The strategy presented offers sensitive, affordable, and robust multiplex detection that can be used to support efforts in wastewater-based epidemiology, environmental monitoring, and human/animal diagnostics.

Visualizing Neighborhood COVID-19 Levels, Trends, and Inequities in Wastewater: An Equity-Centered Approach and Comparison to CDC Methods

CONTEXT

Monitoring neighborhood-level SARS-CoV-2 wastewater concentrations can help guide public health interventions and provide early warning ahead of lagging COVID-19 clinical indicators. To date, however, U.S. Centers for Disease Control and Prevention's (CDC) National Wastewater Surveillance System (NWSS) has provided methodology solely for communicating national and state-level "wastewater viral activity levels."

PROGRAM

In October 2022, the Boston Public Health Commission (BPHC) began routinely sampling wastewater at 11 neighborhood sites to better understand COVID-19 epidemiology and inequities across neighborhoods, which vary widely in sociodemographic and socioeconomic characteristics. We developed equity-centered methods to routinely report interpretable and actionable descriptions of COVID-19 wastewater levels, trends, and neighborhood-level inequities.

APPROACH AND IMPLEMENTATION

To produce these data visualizations, spanning October 2022 to December 2023, we followed four general steps: (1) smoothing raw values; (2) classifying current COVID-19 wastewater levels; (3) classifying current trends; and (4) reporting and visualizing results.

EVALUATION

COVID-19 wastewater levels corresponded well with lagged COVID-19 hospitalizations and deaths over time, with "Very High" wastewater levels coinciding with winter surges. When citywide COVID-19 levels were at the highest and lowest points, levels and trends tended to be consistent across sites. In contrast, when citywide levels were moderate, neighborhood levels and trends were more variable, revealing inequities across neighborhoods, emphasizing the importance of neighborhood-level results. Applying CDC/NWSS state-level methodology to neighborhood sites resulted in vastly different neighborhood-specific wastewater cut points for "High" or "Low," obscured inequities between neighborhoods, and systematically underestimated COVID-19 levels during surge periods in neighborhoods with the highest COVID-19 morbidity and mortality.

DISCUSSION

Our methods offer an approach that other local jurisdictions can use for routinely monitoring, comparing, and communicating neighborhood-level wastewater levels, trends, and inequities. Applying CDC/NWSS methodology at the neighborhood-level can obscure and perpetuate COVID-19 inequities. We recommend jurisdictions adopt equity-focused approaches in neighborhood-level wastewater surveillance for valid community comparisons.

Simultaneous biomass concentration and subsequent quantitation of multiple infectious disease agents and antimicrobial resistance genes from community wastewater

Wastewater-based surveillance (WBS) of infectious disease agents is increasingly seen as a reliable source of population health data. To date, wastewater-based surveillance efforts have largely focused on individual pathogens. However, given that wastewater contains a broad range of pathogens circulating in the population, a more comprehensive approach could enhance its usability. We focused on the simultaneous detection of SARS-CoV-2, sapovirus, Campylobacter jejuni, Campylobacter coli, Salmonella spp., pathogenic Escherichia coli, Cryptosporidium spp., Giardia spp. and antimicrobial resistance genes (ARGs) of clinical relevance. To achieve this goal, biomass concentration and nucleic acid extraction methods were optimized, and samples were analyzed by using a set of (RT)-qPCR and (HT)-qPCR methods. We determined the prevalence and the spatial and temporal trends of the targeted pathogens and collected novel information on ARGs in Finnish wastewater. In addition, the use of different wastewater concentrates, namely the ultrafiltered concentrate of the supernatant and the centrifuged pellet, and the effect of freezing and thawing wastewater prior to sample processing were investigated with the indicator microbe crAssphage. Freeze-thawing of wastewater decreased the gene copy count of crAssphage in comparison to analyzing fresh samples (p < 0.001). Campylobacters were most abundant in two of the four studied summer months (30 % detection rate) and in wastewaters from regions with intensive animal farming. Salmonella, however, was detected in 40 % of the samples without any clear seasonal trends, and the highest gene copy numbers were recorded from the largest wastewater treatment plants. Beta-lactamase resistance genes that have commonly been detected in bacteria isolated from humans in Finland, namely blaCTX-M, blaOXA48, blaNDM, and blaKPC, were also frequently detected in wastewaters (100, 98, 98, and 70 % detection rates, respectively). These results confirm the reliability of using wastewater in public health surveillance and demonstrate the possibility to simultaneously perform WBS of multiple pathogens.

Nucleic Acid Based Testing (NABing): A Game Changer Technology for Public Health

Timely and accurate detection of the causal agent of a disease is crucial to restrict suffering and save lives. Mere symptoms are often not enough to detect the root cause of the disease. Better diagnostics applied for screening at a population level and sensitive detection assays remain the crucial component of disease surveillance which may include clinical, plant, and environmental samples, including wastewater. The recent advances in genome sequencing, nucleic acid amplification, and detection methods have revolutionized nucleic acid-based testing (NABing) and screening assays. A typical NABing assay consists of three modules: isolation of the nucleic acid from the collected sample, identification of the target sequence, and final reading the target with the help of a signal, which may be in the form of color, fluorescence, etc. Here, we review current NABing assays covering the different aspects of all three modules. We also describe the frequently used target amplification or signal amplification procedures along with the variety of applications of this fast-evolving technology and challenges in implementation of NABing in the context of disease management especially in low-resource settings.

Pathogen dynamics and discovery of novel viruses and enzymes by deep nucleic acid sequencing of wastewater

Wastewater contains an extensive reservoir of genetic information, yet largely unexplored. Here, we analyzed by high-throughput sequencing total nucleic acids extracted from wastewater samples collected during a 17 month-period in Berlin, Germany. By integrating global wastewater datasets and applying a novel computational approach to accurately identify viral strains within sewage RNA-sequencing data, we demonstrated the emergence and global dissemination of a specific astrovirus strain. Astrovirus abundance and sequence variation mirrored temporal and spatial patterns of infection, potentially serving as footprints of specific timeframes and geographical locations. Additionally, we revealed more than 100,000 sequence contigs likely originating from novel viral species, exhibiting distinct profiles in total RNA and DNA datasets and including undescribed bunyaviruses and parvoviruses. Finally, we identified thousands of new CRISPR-associated protein sequences, including Transposase B (TnpB), a class of compact, RNA-guided DNA editing enzymes. Collectively, our findings underscore the potential of high-throughput sequencing of total nucleic acids derived from wastewater for a broad range of applications.

Focus, vigilance, resilience: towards stronger infectious disease surveillance, threat detection and response in the EU/EEA

This perspective summarises and explains the long-term surveillance framework 2021-2027 for infectious diseases in the European Union/European Economic Area (EU/EEA) published in April 2023. It shows how shortcomings in the areas of public health focus, vigilance and resilience will be addressed through specific strategies in the coming years and how these strategies will lead to stronger surveillance systems for early detection and monitoring of public health threats as well as informing their effective prevention and control. A sharper public health focus is expected from a more targeted list of notifiable diseases, strictly public-health-objective-driven surveillance standards, and consequently, leaner surveillance systems. Vigilance should improve through mandatory event reporting, more automated epidemic intelligence processing and increased use of genomic surveillance. Finally, EU/EEA surveillance systems should become more resilient by modernising the underlying information technology infrastructure, expanding the influenza sentinel surveillance system to other respiratory viruses for better pandemic preparedness, and increasingly exploiting potentially more robust alternative data sources, such as electronic health records and wastewater surveillance. Continued close collaboration across EU/EEA countries will be key to ensuring the full implementation of this surveillance framework and more effective disease prevention and control.

Wastewater disinfection with photodynamic treatment and evaluation of its ecotoxicological effects

Research has demonstrated the presence of viruses in wastewater (WW), which can remain viable for a long period, posing potential health risks. Conventional WW treatment methods involving UV light, chlorine and ozone efficiently reduce microbial concentrations, however, they produce hazardous byproducts and microbial resistance that are detrimental to human health and the ecosystem. Hence, there is a need for novel disinfection techniques. Antimicrobial Photodynamic Inactivation (PDI) emerges as a promising strategy, utilizing photosensitizers (PS), light, and dioxygen to inactivate viruses. This study aims to assess the efficacy of PDI by testing methylene blue (MB) and the cationic porphyrin TMPyP as PSs, along a low energy consuming white light source (LED) at an irradiance of 50 mW/cm2, for the inactivation of bacteriophage Phi6. Phi6 serves as an enveloped RNA-viruses surrogate model in WW. PDI experiments were conducted in a buffer solution (PBS) and real WW matrices (filtered and non-filtered). Considering the environmental release of the treated effluents, this research also evaluated the ecotoxicity of the resulting solution (post-PDI treatment effluent) on the model organism Daphnia magna, following the Organisation for Economic Cooperation and Development (OECD) immobilization technical 202 guideline. Daphnids were exposed to WW containing the tested PS at different concentrations and dilutions (accounting for the dilution factor during WW release into receiving waters) over 48 h. The results indicate that PDI with MB efficiently inactivated the model virus in the different aqueous matrices, achieving reductions superior to 8 log10 PFU/mL, after treatments of 5 min in PBS and of ca. 90 min in WW. Daphnids survival increased when subjected to the PDI-treated WW with MB, considering the dilution factor. Overall, the effectiveness of PDI in eliminating viruses in WW, the fading of the toxic effects on daphnids after MB' irradiation and the rapid dilution effect upon WW release in the environment highlight the possibility of using MB in WW PDI-disinfection.

Jointly estimating epidemiological dynamics of Covid-19 from case and wastewater data in Aotearoa New Zealand

BACKGROUND

Timely and informed public health responses to infectious diseases such as COVID-19 necessitate reliable information about infection dynamics. The case ascertainment rate (CAR), the proportion of infections that are reported as cases, is typically much less than one and varies with testing practices and behaviours, making reported cases unreliable as the sole source of data. The concentration of viral RNA in wastewater samples provides an alternate measure of infection prevalence that is not affected by clinical testing, healthcare-seeking behaviour or access to care.

METHODS

We construct a state-space model with observed data of levels of SARS-CoV-2 in wastewater and reported case incidence and estimate the hidden states of the effective reproduction number, R, and CAR using sequential Monte Carlo methods.

RESULTS

We analyse data from 1 January 2022 to 31 March 2023 from Aotearoa New Zealand. Our model estimates that R peaks at 2.76 (95% CrI 2.20, 3.83) around 18 February 2022 and the CAR peaks around 12 March 2022. We calculate that New Zealand's second Omicron wave in July 2022 is similar in size to the first, despite fewer reported cases. We estimate that the CAR in the BA.5 Omicron wave in July 2022 is approximately 50% lower than in the BA.1/BA.2 Omicron wave in March 2022.

CONCLUSIONS

Estimating R, CAR, and cumulative number of infections provides useful information for planning public health responses and understanding the state of immunity in the population. This model is a useful disease surveillance tool, improving situational awareness of infectious disease dynamics in real-time.

RBD amplicon sequencing of wastewater reveals patterns of variant emergence and evolution

Rapid evolution of SARS-CoV-2 has resulted in the emergence of numerous variants, posing significant challenges to public health surveillance. Clinical genome sequencing, while valuable, has limitations in capturing the full epidemiological dynamics of circulating variants in the general population. This study utilized receptor-binding domain (RBD) amplicon sequencing of wastewater samples to monitor the SARS-CoV-2 community dynamics and evolution in El Paso, TX. Over 17 months, we identified 91 variants and observed waves of dominant variants transitioning from BA.2 to BA.2.12.1, BA.4&5, BQ.1, and XBB.1.5. Our findings demonstrated early detection of variants and identification of unreported outbreaks, while showing strong consistency with clinical genome sequencing data at the local, state, and national levels. Alpha diversity analyses revealed significant periodical variations, with the highest diversity observed in winter and the outbreak lag phases, likely due to lower competition among variants before the outbreak growth phase. The data underscores the importance of low transmission periods for rapid mutation and variant evolution. This study highlights the effectiveness of integrating RBD amplicon sequencing with wastewater surveillance in tracking viral evolution, understanding variant emergence, and enhancing public health preparedness.

Balancing public health and group privacy: Ethics, rights, and obligations for wastewater surveillance systems

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

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.

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

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

Long-term wastewater monitoring of SARS-CoV-2 viral loads and variants at the major international passenger hub Amsterdam Schiphol Airport: A valuable addition to COVID-19 surveillance

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

Developing wastewater-based surveillance schemes for multiple pathogens: The WastPan project in Finland

Wastewater comprises multiple pathogens and offers a potential for wastewater-based surveillance (WBS) to track the prevalence of communicable diseases. The Finnish WastPan project aimed to establish wastewater-based pandemic preparedness for multiple pathogens (viruses, bacteria, parasites, fungi), including antimicrobial resistance (AMR). This article outlines WastPan's experiences in this project, including the criteria for target selection, sampling locations, frequency, analysis methods, and results communication. Target selection relied on epidemiological and microbiological evidence and practical feasibility. Within the WastPan framework, wastewater samples were collected between 2021 and 2023 from 10 wastewater treatment plants (WWTPs) covering 40 % of Finland's population. WWTP selection was validated for reported cases of Extended Spectrum Beta-lactamase-producing bacterial pathogens (Escherichia coli and Klebsiella pneumoniae) from the National Infectious Disease Register. The workflow included 24-h composite influent samples, with one fraction for culture-based analysis (bacteria and fungi) and the rest of the sample was reserved for molecular analysis (viruses, bacteria, antibiotic resistance genes, and parasites). The reproducibility of the monitoring workflow was assessed for SARS-CoV-2 through inter-laboratory comparisons using the N2 and N1 assays. Identical protocols were applied to same-day samples, yielding similar positivity trends in the two laboratories, but the N2 assay achieved a significantly higher detection rate (Laboratory 1: 91.5 %; Laboratory 2: 87.4 %) than the N1 assay (76.6 %) monitored only in Laboratory 2 (McNemar, p < 0.001 Lab 1, = 0.006 Lab 2). This result indicates that the selection of monitoring primers and assays may impact monitoring sensitivity in WBS. Overall, the current study recommends that the selection of sampling frequencies and population coverage of the monitoring should be based on pathogen-specific epidemiological characteristics. For example, pathogens that are stable over time may need less frequent annual sampling, while those that are occurring across regions may require reduced sample coverage. Here, WastPan successfully piloted WBS for monitoring multiple pathogens, highlighting the significance of one-litre community composite wastewater samples for assessing community health. The infrastructure established for COVID-19 WBS is valuable for monitoring various pathogens. The prioritization of the monitoring targets optimizes resource utilization. In the future legislative support in target selection, coverage determination, and sustained funding for WBS is recomended.

Assessment of sewer connectivity in the United States and its implications for equity in wastewater-based epidemiology

Wastewater-based epidemiology is a promising public health tool that can yield a more representative view of the population than case reporting. However, only about 80% of the U.S. population is connected to public sewers, and the characteristics of populations missed by wastewater-based epidemiology are unclear. To address this gap, we used publicly available datasets to assess sewer connectivity in the U.S. by location, demographic groups, and economic groups. Data from the U.S. Census' American Housing Survey revealed that sewer connectivity was lower than average when the head of household was American Indian and Alaskan Native, White, non-Hispanic, older, and for larger households and those with higher income, but smaller geographic scales revealed local variations from this national connectivity pattern. For example, data from the U.S. Environmental Protection Agency showed that sewer connectivity was positively correlated with income in Minnesota, Florida, and California. Data from the U.S. Census' American Community Survey and Environmental Protection Agency also revealed geographic areas with low sewer connectivity, such as Alaska, the Navajo Nation, Minnesota, Michigan, and Florida. However, with the exception of the U.S. Census data, there were inconsistencies across datasets. Using mathematical modeling to assess the impact of wastewater sampling inequities on inferences about epidemic trajectory at a local scale, we found that in some situations, even weak connections between communities may allow wastewater monitoring in one community to serve as a reliable proxy for an interacting community with no wastewater monitoring, when cases are widespread. A systematic, rigorous assessment of sewer connectivity will be important for ensuring an equitable and informed implementation of wastewater-based epidemiology as a public health monitoring system.

Identifying outbreaks in sewer networks: An adaptive sampling scheme under network's uncertainty

Motivated by the implementation of a SARS-Cov-2 sewer surveillance system in Chile during the COVID-19 pandemic, we propose a set of mathematical and algorithmic tools that aim to identify the location of an outbreak under uncertainty in the network structure. Given an upper bound on the number of samples we can take on any given day, our framework allows us to detect an unknown infected node by adaptively sampling different network nodes on different days. Crucially, despite the uncertainty of the network, the method allows univocal detection of the infected node, albeit at an extra cost in time. This framework relies on a specific and well-chosen strategy that defines new nodes to test sequentially, with a heuristic that balances the granularity of the information obtained from the samples. We extensively tested our model in real and synthetic networks, showing that the uncertainty of the underlying graph only incurs a limited increase in the number of iterations, indicating that the methodology is applicable in practice.

From pandemic to endemic: Divergence of COVID-19 positive-tests and hospitalization numbers from SARS-CoV-2 RNA levels in wastewater of Rochester, Minnesota

Traditionally, public health surveillance relied on individual-level data but recently wastewater-based epidemiology (WBE) for the detection of infectious diseases including COVID-19 became a valuable tool in the public health arsenal. Here, we use WBE to follow the course of the COVID-19 pandemic in Rochester, Minnesota (population 121,395 at the 2020 census), from February 2021 to December 2022. We monitored the impact of SARS-CoV-2 infections on public health by comparing three sets of data: quantitative measurements of viral RNA in wastewater as an unbiased reporter of virus level in the community, positive results of viral RNA or antigen tests from nasal swabs reflecting community reporting, and hospitalization data. From February 2021 to August 2022 viral RNA levels in wastewater were closely correlated with the oscillating course of COVID-19 case and hospitalization numbers. However, from September 2022 cases remained low and hospitalization numbers dropped, whereas viral RNA levels in wastewater continued to oscillate. The low reported cases may reflect virulence reduction combined with abated inclination to report, and the divergence of virus levels in wastewater from reported cases may reflect COVID-19 shifting from pandemic to endemic. WBE, which also detects asymptomatic infections, can provide an early warning of impending cases, and offers crucial insights during pandemic waves and in the transition to the endemic phase.

Wastewater-based Epidemiology for COVID-19 Surveillance: A Survey

The pandemic of COVID-19 has imposed tremendous pressure on public health systems and social economic ecosystems over the past years. To alleviate its social impact, it is important to proactively track the prevalence of COVID-19 within communities. The traditional way to estimate the disease prevalence is to estimate from reported clinical test data or surveys. However, the coverage of clinical tests is often limited and the tests can be labor-intensive, requires reliable and timely results, and consistent diagnostic and reporting criteria. Recent studies revealed that patients who are diagnosed with COVID-19 often undergo fecal shedding of SARS-CoV-2 virus into wastewater, which makes wastewater-based epidemiology (WBE) for COVID-19 surveillance a promising approach to complement traditional clinical testing. In this paper, we survey the existing literature regarding WBE for COVID-19 surveillance and summarize the current advances in the area. Specifically, we have covered the key aspects of wastewater sampling, sample testing, and presented a comprehensive and organized summary of wastewater data analytical methods. Finally, we provide the open challenges on current wastewater-based COVID-19 surveillance studies, aiming to encourage new ideas to advance the development of effective wastewater-based surveillance systems for general infectious diseases.

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.

A broad wastewater screening and clinical data surveillance for virus-related diseases in the metropolitan Detroit area in Michigan

BACKGROUND

Periodic bioinformatics-based screening of wastewater for assessing the diversity of potential human viral pathogens circulating in a given community may help to identify novel or potentially emerging infectious diseases. Any identified contigs related to novel or emerging viruses should be confirmed with targeted wastewater and clinical testing.

RESULTS

During the COVID-19 pandemic, untreated wastewater samples were collected for a 1-year period from the Great Lakes Water Authority Wastewater Treatment Facility in Detroit, MI, USA, and viral population diversity from both centralized interceptor sites and localized neighborhood sewersheds was investigated. Clinical cases of the diseases caused by human viruses were tabulated and compared with data from viral wastewater monitoring. In addition to Betacoronavirus, comparison using assembled contigs against a custom Swiss-Prot human virus database indicated the potential prevalence of other pathogenic virus genera, including: Orthopoxvirus, Rhadinovirus, Parapoxvirus, Varicellovirus, Hepatovirus, Simplexvirus, Bocaparvovirus, Molluscipoxvirus, Parechovirus, Roseolovirus, Lymphocryptovirus, Alphavirus, Spumavirus, Lentivirus, Deltaretrovirus, Enterovirus, Kobuvirus, Gammaretrovirus, Cardiovirus, Erythroparvovirus, Salivirus, Rubivirus, Orthohepevirus, Cytomegalovirus, Norovirus, and Mamastrovirus. Four nearly complete genomes were recovered from the Astrovirus, Enterovirus, Norovirus and Betapolyomavirus genera and viral species were identified.

CONCLUSIONS

The presented findings in wastewater samples are primarily at the genus level and can serve as a preliminary "screening" tool that may serve as indication to initiate further testing for the confirmation of the presence of species that may be associated with human disease. Integrating innovative environmental microbiology technologies like metagenomic sequencing with viral epidemiology offers a significant opportunity to improve the monitoring of, and predictive intelligence for, pathogenic viruses, using wastewater.

A scoping review of human pathogens detected in untreated human wastewater and sludge

Wastewater monitoring is an approach to identify the presence or abundance of pathogens within a population. The objective of this scoping review (ScR) was to identify and characterize research on human pathogens and antimicrobial resistance detected in untreated human wastewater and sludge. A search was conducted up to March 2023 and standard ScR methodology was followed. This ScR included 1,722 articles, of which 56.5% were published after the emergence of COVID-19. Viruses and bacteria were commonly investigated, while research on protozoa, helminths, and fungi was infrequent. Articles prior to 2019 were dominated by research on pathogens transmitted through fecal-oral or waterborne pathways, whereas more recent articles have explored the detection of pathogens transmitted through other pathways such as respiratory and vector-borne. There was variation in sampling, samples, and sample processing across studies. The current evidence suggests that wastewater monitoring could be applied to a range of pathogens as a public health tool to detect an emerging pathogen and understand the burden and spread of disease to inform decision-making. Further development and refinement of the methods to identify and interpret wastewater signals for different prioritized pathogens are needed to develop standards on when, why, and how to monitor effectively.

Wastewater surveillance for bacterial targets: current challenges and future goals

Wastewater-based epidemiology (WBE) expanded rapidly in response to the COVID-19 pandemic. As the public health emergency has ended, researchers and practitioners are looking to shift the focus of existing wastewater surveillance programs to other targets, including bacteria. Bacterial targets may pose some unique challenges for WBE applications. To explore the current state of the field, the National Science Foundation-funded Research Coordination Network (RCN) on Wastewater Based Epidemiology for SARS-CoV-2 and Emerging Public Health Threats held a workshop in April 2023 to discuss the challenges and needs for wastewater bacterial surveillance. The targets and methods used in existing programs were diverse, with twelve different targets and nine different methods listed. Discussions during the workshop highlighted the challenges in adapting existing programs and identified research gaps in four key areas: choosing new targets, relating bacterial wastewater data to human disease incidence and prevalence, developing methods, and normalizing results. To help with these challenges and research gaps, the authors identified steps the larger community can take to improve bacteria wastewater surveillance. This includes developing data reporting standards and method optimization and validation for bacterial programs. Additionally, more work is needed to understand shedding patterns for potential bacterial targets to better relate wastewater data to human infections. Wastewater surveillance for bacteria can help provide insight into the underlying prevalence in communities, but much work is needed to establish these methods.IMPORTANCEWastewater surveillance was a useful tool to elucidate the burden and spread of SARS-CoV-2 during the pandemic. Public health officials and researchers are interested in expanding these surveillance programs to include bacterial targets, but many questions remain. The NSF-funded Research Coordination Network for Wastewater Surveillance of SARS-CoV-2 and Emerging Public Health Threats held a workshop to identify barriers and research gaps to implementing bacterial wastewater surveillance programs.

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.

Predictive power of wastewater for nowcasting infectious disease transmission: A retrospective case study of five sewershed areas in Louisville, Kentucky

BACKGROUND

Epidemiological nowcasting traditionally relies on count surveillance data. The availability and quality of such count data may vary over time, limiting representation of true infections. Wastewater data correlates with traditional surveillance data and may provide additional value for nowcasting disease trends.

METHODS

We obtained SARS-CoV-2 case, death, wastewater, and serosurvey data for Jefferson County, Kentucky (USA), between August 2020 and March 2021, and parameterized an existing nowcasting model using combinations of these data. We assessed the predictive performance and variability at the sewershed level and compared the effects of adding or replacing wastewater data to case and death reports.

FINDINGS

Adding wastewater data minimally improved the predictive performance of nowcasts compared to a model fitted to case and death data (Weighted Interval Score (WIS) 0.208 versus 0.223), and reduced the predictive performance compared to a model fitted to deaths data (WIS 0.517 versus 0.500). Adding wastewater data to deaths data improved the nowcasts agreement to estimates from models using cases and deaths data. These findings were consistent across individual sewersheds as well as for models fit to the aggregated total data of 5 sewersheds. Retrospective reconstructions of epidemiological dynamics created using different combinations of data were in general agreement (coverage >75%).

INTERPRETATION

These findings show wastewater data may be valuable for infectious disease nowcasting when clinical surveillance data are absent, such as early in a pandemic or in low-resource settings where systematic collection of epidemiologic data is difficult.

Where Does Wastewater-Based Epidemiology Fall in Medical Student Education?

Doctors are well-trained in the collection, analysis, and interpretation of individual stool or urine sample data; however, wastewater-based epidemiology (WBE) combines the excretion of many community members into an anonymous health sample tied to a geographic location. We advocate for the inclusion of WBE in medical education. WBE offers physicians an opportunity to better care for patients with diseases seen at health clinics and doctors' offices, customize and inform treatment, and accept positive results as true positives, backed by the contextual information provided by wastewater findings. It is also a tool to combat biased or misinformed risk perceptions. Medical education should include how to evaluate wastewater information presented, detect inconsistencies, and determine applicability; just as medical students are taught to do with data from other sources.

What is the functional reach of wastewater surveillance for respiratory viruses, pathogenic viruses of concern, and bacterial antibiotic resistance genes of interest?

BACKGROUND

Despite a clear appreciation of the impact of human pathogens on community health, efforts to understand pathogen dynamics within populations often follow a narrow-targeted approach and rely on the deployment of specific molecular probes for quantitative detection or rely on clinical detection and reporting.

MAIN TEXT

Genomic analysis of wastewater samples for the broad detection of viruses, bacteria, fungi, and antibiotic resistance genes of interest/concern is inherently difficult, and while deep sequencing of wastewater provides a wealth of information, a robust and cooperative foundation is needed to support healthier communities. In addition to furthering the capacity of high-throughput sequencing wastewater-based epidemiology to detect human pathogens in an unbiased and agnostic manner, it is critical that collaborative networks among public health agencies, researchers, and community stakeholders be fostered to prepare communities for future public health emergencies or for the next pandemic. A more inclusive public health infrastructure must be built for better data reporting where there is a global human health risk burden.

CONCLUSIONS

As wastewater platforms continue to be developed and refined, high-throughput sequencing of human pathogens in wastewater samples will emerge as a gold standard for understanding community health.

Wastewater surveillance provides 10-days forecasting of COVID-19 hospitalizations superior to cases and test positivity: A prediction study

Background

The public health response to COVID-19 has shifted to reducing deaths and hospitalizations to prevent overwhelming health systems. The amount of SARS-CoV-2 RNA fragments in wastewater are known to correlate with clinical data including cases and hospital admissions for COVID-19. We developed and tested a predictive model for incident COVID-19 hospital admissions in New York State using wastewater data.

Methods

Using county-level COVID-19 hospital admissions and wastewater surveillance covering 13.8 million people across 56 counties, we fit a generalized linear mixed model predicting new hospital admissions from wastewater concentrations of SARS-CoV-2 RNA from April 29, 2020 to June 30, 2022. We included covariates such as COVID-19 vaccine coverage in the county, comorbidities, demographic variables, and holiday gatherings.

Findings

Wastewater concentrations of SARS-CoV-2 RNA correlated with new hospital admissions per 100,000 up to ten days prior to admission. Models that included wastewater had higher predictive power than models that included clinical cases only, increasing the accuracy of the model by 15%. Predicted hospital admissions correlated highly with observed admissions (r = 0.77) with an average difference of 0.013 hospitalizations per 100,000 (95% CI = [0.002, 0.025]).

Interpretation

Using wastewater to predict future hospital admissions from COVID-19 is accurate and effective with superior results to using case data alone. The lead time of ten days could alert the public to take precautions and improve resource allocation for seasonal surges.

Establishing a Statewide Wastewater Surveillance System in Response to the COVID-19 Pandemic: A Reliable Model for Continuous and Emerging Public Health Threats

CONTEXT

The COVID-19 pandemic sparked efforts across the globe to implement wastewater surveillance for SARS-CoV-2.

PROGRAM

New York State (NYS) established the NYS Wastewater Surveillance Network to estimate the levels of COVID-19 community risk and to provide an early indication of SARS-CoV-2 transmission trends. The network is designed to provide a better understanding of public health burdens and to assist health departments to respond effectively to public health threats.

IMPLEMENTATION

Wastewater surveillance across NYS increased from sporadic and geographically spare in 2020 to routine and widespread in 2022, reaching all 62 counties in the state and covering 74% of New Yorkers. The network team focused on engaging local health departments and wastewater treatment plants to provide wastewater samples, which are then analyzed through a network-affiliated laboratory. Both participating local health departments and wastewater treatment plants receive weekly memos on current SARS-CoV-2 trends and levels. The data are also made publicly available at the state dashboard.

EVALUATION

Using standard indicators to evaluate infectious disease surveillance systems, the NYS Wastewater Surveillance Network was assessed for accuracy, timeliness, and completeness during the first year of operations. We observed 96.5% sensitivity of wastewater to identify substantial/high COVID-19 transmission and 99% specificity to identify low COVID-19 transmission. In total, 80% of results were reported within 1 day of sample collection and were published on the public dashboard within 2 days of sample collection. Among participating wastewater treatment plants, 32.5% provided weekly samples with zero missing data, 31% missed 1 or 2 weeks, and 36.5% missed 3 or more weeks.

DISCUSSION

The NYS Wastewater Surveillance Network continues to be a key component of the state and local health departments' pandemic response. The network fosters prompt public health actions through real-time data, enhancing the preparedness capability for both existing and emerging public health threats.

Unveiling indicator, enteric, and respiratory viruses in aircraft lavatory wastewater using adsorption-extraction and Nanotrap® Microbiome A Particles workflows

The effective detection of viruses in aircraft wastewater is crucial to establish surveillance programs for monitoring virus spread via aircraft passengers. This study aimed to compare the performance of two virus concentration workflows, adsorption-extraction (AE) and Nanotrap® Microbiome A Particles (NMAP), in detecting the prevalence and concentrations of 15 endogenous viruses including ssDNA, dsDNA, ssRNA in 24 aircraft lavatory wastewater samples. The viruses tested included two indicator viruses, four enteric viruses, and nine respiratory viruses. The results showed that cross-assembly phage (crAssphage), human polyomavirus (HPyV), rhinovirus A (RhV A), and rhinovirus B (RhV B) were detected in all wastewater samples using both workflows. However, enterovirus (EV), human norovirus GII (HNoV GII), human adenovirus (HAdV), bocavirus (BoV), parechovirus (PeV), epstein-barr virus (EBV). Influenza A virus (IAV), and respiratory syncytial virus B (RsV B) were infrequently detected by both workflows, and hepatitis A virus (HAV), influenza B virus (IBV), and respiratory syncytial virus B (RsV A) were not detected in any samples. The NMAP workflow had greater detection rates of RNA viruses (EV, PeV, and RsV B) than the AE workflow, while the AE workflow had greater detection rates of DNA viruses (HAdV, BoV, and EBV) than the NMAP workflow. The concentration of each virus was also analyzed, and the results showed that crAssphage had the highest mean concentration (6.76 log10 GC/12.5 mL) followed by HPyV (5.46 log10 GC/12.5 mL using the AE workflow, while the mean concentrations of enteric and respiratory viruses ranged from 2.48 to 3.63 log10 GC/12.5 mL. Using the NMAP workflow, the mean concentration of crAssphage was 5.18 log10 GC/12.5 mL and the mean concentration of HPyV was 4.20 log10 GC/12.5 mL, while mean concentrations of enteric and respiratory viruses ranged from 2.55 to 3.74 log10 GC/12.5 mL. Significantly higher (p < 0.05) mean concentrations of crAssphage and HPyV were observed when employing the AE workflow in comparison to the NMAP workflow. Conversely, the NMAP workflow yielded significantly greater (p < 0.05) concentrations of RhV A, and RhV B compared to the AE workflow. The findings of this study can aid in the selection of an appropriate concentration workflow for virus surveillance studies and contribute to the development of efficient virus detection methods.

Wastewater surveillance facilitates climate change-resilient pathogen monitoring

Traditional disease surveillance systems are ill-equipped to handle climate change-driven shifts in pathogen dynamics. If paired with wastewater surveillance, a cost-effective and scalable approach for generating high-resolution health data, such next-generation systems can enable effective resource allocation and delivery of targeted interventions.

Wastewater Surveillance in Europe for Non-Polio Enteroviruses and Beyond

Wastewater surveillance (WWS) was developed in the early 1960s for the detection of poliovirus (PV) circulation in the population. It has been used to monitor several pathogens, including non-polio enteroviruses (NPEVs), which are increasingly recognised as causes of morbidity in children. However, when applying WWS to a new pathogen, it is important to consider the purpose of such a study as well as the suitability of the chosen methodology. With this purpose, the European Non-Polio Enterovirus Network (ENPEN) organised an expert webinar to discuss its history, methods, and applications; its evolution from a culture-based method to molecular detection; and future implementation of next generation sequencing (NGS). The first simulation experiments with PV calculated that a 400 mL sewage sample is sufficient for the detection of viral particles if 1:10,000 people excrete poliovirus in a population of 700,000 people. If the method is applied correctly, several NPEV types are detected. Despite culture-based methods remaining the gold standard for WWS, direct methods followed by molecular-based and sequence-based assays have been developed, not only for enterovirus but for several pathogens. Along with case-based sentinel and/or syndromic surveillance, WWS for NPEV and other pathogens represents an inexpensive, flexible, anonymised, reliable, population-based tool for monitoring outbreaks and the (re)emergence of these virus types/strains within the general population.

Wastewater-Based Surveillance Does Not Belong in a Regulatory Framework Designed to Protect Waters That Receive Treated Wastewater. Comment on Wright, T.; Adhikari, A. Utilizing a National Wastewater Monitoring Program to Address the U.S. Opioid Epidemic: A Focus on Metro Atlanta, Georgia. Int. J. Environ. Res. Public Health 2023, 20, 5282

We read with great interest the work by Wright and Adhikari on "Utilizing a National Wastewater Monitoring Program to Address the U.S. Opioid Epidemic: A Focus on Metro Atlanta, Georgia" [...].

Prolonged viral shedding from noninfectious individuals confounds wastewater-based epidemiology

Wastewater surveillance has been widely used to track and estimate SARS-CoV-2 incidence. While both infectious and recovered individuals shed virus into wastewater, epidemiological inferences using wastewater often only consider the viral contribution from the former group. Yet, the persistent shedding in the latter group could confound wastewater-based epidemiological inference, especially during the late stage of an outbreak when the recovered population outnumbers the infectious population. To determine the impact of recovered individuals' viral shedding on the utility of wastewater surveillance, we develop a quantitative framework that incorporates population-level viral shedding dynamics, measured viral RNA in wastewater, and an epidemic dynamic model. We find that the viral shedding from the recovered population can become higher than the infectious population after the transmission peak, which leads to a decrease in the correlation between wastewater viral RNA and case report data. Furthermore, the inclusion of recovered individuals' viral shedding into the model predicts earlier transmission dynamics and slower decreasing trends in wastewater viral RNA. The prolonged viral shedding also induces a potential delay in the detection of new variants due to the time needed to generate enough new cases for a significant viral signal in an environment dominated by virus shed by the recovered population. This effect is most prominent toward the end of an outbreak and is greatly affected by both the recovered individuals' shedding rate and shedding duration. Our results suggest that the inclusion of viral shedding from non-infectious recovered individuals into wastewater surveillance research is important for precision epidemiology.

Significance of wastewater surveillance in detecting the prevalence of SARS-CoV-2 variants and other respiratory viruses in the community - A multi-site evaluation

Detection of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) viral genome in wastewater has proven to be useful for tracking the trends of virus prevalence within the community. The surveillance also provides precise and early detection of any new and circulating variants, which aids in response to viral outbreaks. Site-specific monitoring of SARS-CoV-2 variants provides valuable information on the prevalence of new or emerging variants in the community. We sequenced the genomic RNA of viruses present in the wastewater samples and analyzed for the prevalence of SARS-CoV-2 variants as well as other respiratory viruses for a period of one year to account for seasonal variations. The samples were collected from the Reno-Sparks metropolitan area on a weekly basis between November 2021 to November 2022. Samples were analyzed to detect the levels of SARS-CoV-2 genomic copies and variants identification. This study confirmed that wastewater monitoring of SARS-CoV-2 variants can be used for community surveillance and early detection of circulating variants and supports wastewater-based epidemiology (WBE) as a complement to clinical respiratory virus testing as a healthcare response effort. Our study showed the persistence of the SARS-CoV-2 virus throughout the year compared to a seasonal presence of other respiratory viruses, implicating SARS-CoV-2's broad genetic diversity and strength to persist and infect susceptible hosts. Through secondary analysis, we further identified antimicrobial resistance (AMR) genes in the same wastewater samples and found WBE to be a feasible tool for community AMR detection and monitoring.

Wastewater treatment plant operators report high capacity to support wastewater surveillance for COVID-19 across New York State, USA

Wastewater surveillance for infectious disease expanded greatly during the COVID-19 pandemic. As a collaboration between sanitation engineers and scientists, the most cost-effective deployment of wastewater surveillance routinely tests wastewater samples from wastewater treatment plants. To evaluate the capacity of treatment plants of different sizes and characteristics to participate in surveillance efforts, we developed and distributed a survey to New York State municipal treatment plant supervisors in the summer and fall of 2021. The goal of the survey was to assess the knowledge, capacity, and attitudes toward wastewater surveillance as a public health tool. Our objectives were to: (1) determine what treatment plant operators know about wastewater surveillance for public health; (2) assess how plant operators feel about the affordability and benefits of wastewater surveillance; and (3) determine how frequently plant personnel can take and ship samples using existing resources. Results show that 62% of respondents report capacity to take grab samples twice weekly. Knowledge about wastewater surveillance was mixed with most supervisors knowing that COVID-19 can be tracked via wastewater but having less knowledge about surveillance for other public health issues such as opioids. We found that attitudes toward wastewater testing for public health were directly associated with differences in self-reported capacity of the plant to take samples. Further, findings suggest a diverse capacity for sampling across sewer systems with larger treatment plants reporting greater capacity for more frequent sampling. Findings provide guidance for outreach activities as well as important insight into treatment plant sampling capacity as it is connected to internal factors such as size and resource availability. These may help public health departments understand the limitations and ability of wastewater surveillance for public health benefit.