Objectives. To evaluate equity in wastewater-based infectious disease surveillance, including equity of inclusion, outbreak detection, and disease forecasting.

Methods. We assessed New York State’s wastewater surveillance network using census tract–level data to compare social vulnerability and environmental burden across included, sewered but excluded, and unsewered communities during the scale-up of the network (2020–2024) and under changing coverage scenarios. We modeled outbreak detection equity by estimating outbreak sizes required for 95% confidence in the detection of hypothetical pathogens and assessed forecasting equity using county-level COVID-19 hospitalization predictions in relation to population size, coverage, and vulnerability.

Results. We found similar vulnerability distributions between included and sewered but excluded communities. The 25% most vulnerable communities required median outbreak sizes 3.45 times larger for detection than did the 25% least vulnerable. At medium sensitivity, more than 80% of individuals living in poverty and minoritized populations lived in areas where outbreaks exceeded 10 infections before detection. Forecasting accuracy was lower in smaller (< 20%) than in larger urban (> 60%) counties and increased with population size and network coverage (P < .001).

Conclusions. Wastewater surveillance was equitable in population coverage but not in outbreak detection and forecasting performance.

Public Health Implications. Strategies such as upstream sampling, expanded wastewater treatment plant participation in smaller communities, and improved modeling for low-population areas may reduce inequities and enhance the utility of wastewater surveillance.

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Sequencing genetic material from community wastewater facilitates the study of virus diversity. Diversity can be estimated using the variation in nucleotides and number of variants identified, increasing the information obtained from one wastewater sample. Using 12,290 wastewater sequences of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), we found that virus diversity is a strong indicator of disease transmission, with a significant correlation between average weekly diversity and average weekly new COVID-19 infections. Although the concentration of viruses is the common measure of disease transmission from wastewater, we found that diversity is a better indicator, as it is unhampered by the noise inherent in the wastewater. We show that multiple measures of diversity yield similar results, which suggests that genetic diversity could be useful for increasing the utility of wastewater data.

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Given the rapid growth in investment and methodological innovation in wastewater and environmental surveillance (WES), this special issue of Epidemics convenes work that better understands the transmission dynamics of various pathogens, including SARS-CoV-2, respiratory syncytial virus (RSV), influenza virus, hepatitis A virus, and norovirus. WES has long served as a crucial public health tool, historically acting as a pillar of the polio eradication initiative (Asghar et al., 2014), and later used to monitor trends like illicit drug use in various cities across the globe (González-Mariño et al., 2020, Jaunay et al., 2024, Wang et al., 2022). While traditionally focused on pathogens spread through faecal-oral transmission, the discovery of SARS-CoV-2 RNA in sewage (Medema et al., 2020) during the COVID-19 pandemic sparked a massive expansion of WES (Naughton et al., 2023), cementing the idea that WES should not be restricted to enteric pathogens, as most, if not all, pathogens may be found in wastewater at some concentration (Diamond et al., 2023, Kilaru et al., 2022, Munk et al., 2022). Subsequently, WES was applied to other seasonal respiratory pathogens such as influenza (Zheng et al., 2023), and newly emerging pathogens such as MPOX (Wolfe et al., 2023). This issue’s contributing authors highlight a number of use cases of wastewater surveillance data such as the estimation of the reproductive number (Hill et al., 2025, Ravuri et al., 2025), modelling the spread of infectious disease (Schmid et al., 2025), forecasting hospitalizations (Peng et al., 2025), and improving the control of infectious diseases in correctional facilities and congregate living settings (de-la-Rosa-Martinez et al., 2026). Collectively, the articles in this special issue reinforce the value of WES as a robust, versatile tool for public health, demonstrating its utility from estimating key transmission metrics and forecasting hospitalizations to optimizing surveillance networks for a more equitable and cost-effective approach.

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Background: Wastewater-based epidemiology (WBE) has the potential to produce reliable, efficient, and non-invasive measures of current psychoactive drug use. The aim of this study was to assess the feasibility and validity of using WBE to estimate current cocaine use among university students at a residential campus.

Methods: We analyzed wastewater samples from four locations at a residential university campus during spring semester of 2021, testing for cocaine metabolites in addition to control comparison substances (acetaminophen and caffeine). We simultaneously administered a confidential self-report survey of recent substance use behaviors to a randomized sample of undergraduate students at this university.

Results: Self-reported survey estimates of cocaine use and point estimates of cocaine use derived from wastewater-based epidemiology are similar, but the survey is imprecise with a wide CI, and agreement is sensitive to key WBE assumptions; thus, results are consistent but not conclusive. The self-report survey results indicated 0.13% of respondents were regular cocaine users, which is equivalent to the estimate of 0.12% of students using cocaine as measured through WBE. This prevalence is also in line with the 0.14% National American College Health Association (NACHA) survey during the same semester.

Conclusions: WBE shows promise as a complementary approach for estimating current cocaine use among students on a residential campus; with current data the WBE point estimate is similar to the survey point estimate, but uncertainty in both measures (especially the survey) requires further research.

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The COVID-19 pandemic prompted a rapid expansion of wastewater-based surveillance in New York State (NYS). Pilot studies were initiated in 2023 to assess the use of this system for the surveillance of hepatitis A virus (HAV) and other pathogens of public health interest. A known cause of outbreaks in the US associated with contaminated food products and transmission between injection drug users, HAV is present in feces for weeks before the onset of symptoms. However, the use of wastewater surveillance as an early warning system has not been assessed outside of the outbreak setting. We compare clinical HAV surveillance with quantitative testing of wastewater samples for HAV RNA from four counties in NYS between September 2022 and November 2023, a period of relatively low HAV incidence. There was a significantly higher mean concentration of HAV RNA in wastewater from sewersheds in districts with reported HAV cases, relative to those without (267 vs. 21 gene copies per microliter, p < 0.05). For 91 % of HAV cases, HAV RNA was detected in the wastewater from the same county between HAV exposure onset and diagnosis, and new HAV RNA detection in wastewater occurred, on average, 41 days before case diagnosis. Our findings demonstrate that wastewater surveillance may provide early warning of case clusters at the county level in low-incidence settings and may allow for detection of otherwise missed asymptomatic or mild illness. Expansion of testing to include all sewersheds in each county may further improve the sensitivity for identifying locations for targeted HAV intervention.

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Emerging infectious diseases present a serious challenge for local health departments, and wastewater testing for pathogen surveillance is one approach to improve response preparedness. Sample collection is routinely conducted at wastewater treatment plants, and samples from wastewater treatment plants can conveniently be used for infectious disease surveillance. The sensitivity of wastewater surveillance for detecting emerging pathogens, however, is lower in high-population areas. In response to a paralytic polio case in Rockland County, the New York State wastewater surveillance network implemented wastewater surveillance in the immediate and neighboring counties, and additional wastewater surveillance at sampling points upstream from the wastewater treatment plants where positive samples were detected. The purpose of this paper is to describe the lessons learned from the rapid emergency deployment of upstream sampling in response to the poliovirus outbreak. Sampling upstream from the treatment plants at manholes in the system reduced the total sampled population in the primary treatment plant serving Rockland County from roughly 201,000 to sub-area populations ranging from roughly 3100 to 78,300, enabling more precise identification of where polio transmission was occurring. Detections across several of these upstream areas confirmed that poliovirus was not isolated to a single individual case or limited to a specific community. Maintaining upstream sampling was challenged by a lack of dedicated staff and funding. Implementation of upstream sampling highlighted the importance of preparation, including underlying data of the sewer infrastructure, coordination between government agencies, and readiness with sampling equipment. As the threat of emerging infectious diseases increases, due in part to increased vaccine hesitancy, well-planned upstream wastewater sampling strategies by health departments should be considered as additional outbreak response tools.

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Background

The effective reproduction number (Rt) is a dynamic indicator of current disease spread risk. Wastewater measurements of viral concentrations are known to correlate with clinical measures of diseases and have been incorporated into methods for estimating the Rt.

Methods

We review wastewater-based methods to estimate the Rt for SARS-CoV-2 based on similarity to the reference case-based Rt, ease of use, and computational requirements. Using wastewater data collected between August 1, 2022, and February 20, 2024, from 205 wastewater treatment plants across New York State, we fit eight wastewater Rt models identified from the literature. Each model is compared to the Rt estimated from case data for New York at the sewershed (wastewater treatment plant catchment area), county, and state levels.

Results

We find a high degree of similarity across all eight methods despite differences in model parameters and approach. Further, two methods based on the common measures of percent change and linear fit reproduced the Rt from case data very well and a GLM accurately predicted case data. Model output varied between spatial scales with some models more closely estimating sewershed Rt values than county Rt values. Similarity to clinical models was also highly correlated with the proportion of the population served by sewer in the surveilled communities (r = 0.77).

Conclusions

While not all methods that estimate Rt from wastewater produce the same results, they all provide a way to incorporate wastewater concentration data into epidemic modeling. Our results show that straightforward measures like the percent change can produce similar results of more complex models. Based on the results, researchers and public health officials can select the method that is best for their situation.

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Municipal wastewater harbors diverse RNA viruses, which are responsible for many emerging and reemerging diseases in humans, animals, and plants. Although genomic sequencing can be a high-throughput approach for profiling the RNA virome in wastewater, wastewater processing methods often influence sequencing outcomes. Here, we systematically evaluated two wastewater processing methods, tangential-flow ultrafiltration (TFF) and Nanotrap Microbiome A Particles, for detecting the target RNA virus severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) via amplicon sequencing and characterizing the RNA virome using whole-transcriptome shotgun sequencing. Our results from paired comparison tests showed that the TFF and Nanotrap methods recovered similar SARS-CoV-2 variants at the lineage level (analysis of similarity [ANOSIM] R = −0.012, P = 0.874). Optimizing automated procedures for the Nanotrap method and concentration factors for the TFF method was critical for achieving high-depth and high-breadth coverage of the target virus genome. Notably, the two methods enriched distinct RNA viromes from the same wastewater samples (ANOSIM R = 0.260, P = 0.002), with TFF samples showing 22-fold and 7-fold higher relative abundances of Reoviridae and Coronaviridae, respectively. These differences are likely due to the distinct virus concentration mechanisms employed by each method, which are influenced by liquid-solid partitioning of virus particles and interactions of viral surface proteins with ligands. Our findings underscore the importance of optimizing wastewater processing methods for genomic monitoring and have implications for broader environmental applications.

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Wastewater surveillance presents a novel data stream for local health departments to understand infectious disease risk in their communities. We conducted a survey of local health departments in New York State regarding their knowledge, attitudes, and practices toward wastewater surveillance of SARS-CoV-2. We further conducted in-depth interviews with a number of local health departments to identify specific use cases of wastewater surveillance data. Almost all survey respondents used wastewater surveillance data and reported advantages to wastewater surveillance data, with the most common use being the tracking of disease trends, followed by monitoring variants, detecting diseases early, and assessing public health interventions. Some counties were sharing wastewater surveillance data with hospitals, which use the data for planning purposes. Public health communication was the most commonly reported action taken from wastewater surveillance data. These findings highlight that local health departments generally have favorable attitudes toward wastewater surveillance. However, there is a need for continued training and support for local health departments to apply these data in their practice.

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Wastewater-based epidemiology (WBE) has emerged as a powerful tool in public health surveillance, capable of detecting a wide range of infectious diseases and chemical markers at the community level. (1) During the COVID-19 pandemic, the implementation of WBE accelerated worldwide as it served as an early-warning tool and provided data to complement traditional clinical surveillance approaches. (2−4) As the field evolves, educators and practitioners recognize the importance of incorporating WBE into academic curricula to prepare the next generation of scientists, engineers, and public health professionals to effectively use these tools. (5) Importantly, the success of WBE efforts during the pandemic relied heavily on close collaboration between academic researchers and public health agencies, partnerships that facilitated real-time data interpretation and informed public health decision-making. Teaching students about this collaborative model not only enhances their understanding of WBE but also prepares them to navigate interdisciplinary and stakeholder-engaged approaches across other areas of environmental and public health practice. This Viewpoint summarizes key insights from an April 21, 2025, workshop by the National Science Foundation Research Coordination Network (RCN, Project 2202361) that explored opportunities and challenges of WBE educational initiatives (Table 1).

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Since 2020 wastewater-based surveillance has quickly been established as an effective and cost-efficient tool for monitoring public health. In this Making Waves article, we argue that these programs must be grounded in principles of justice to achieve global water and health equity. Ethics initiatives to date have focused primarily on privacy, legality, and institutionalised research reviews, often, if not exclusively, in North America and Western Europe. We draw from our interdisciplinary, multisectoral, and international expertise and experience to develop a justice-centred framework for wastewater-based surveillance. First, we identify common concerns across diverse surveillance programs including: defining community, transparency and accountability, and uneven geographies. Second, we draw on political theorist Nancy Fraser's framework of justice to evaluate site-specific practices identifying maldistribution, misrecognition, and exclusion. We suggest that Fraser's framework offers a common approach for evaluating just outcomes rather than specific regulations for governing wastewater surveillance across different and unequal contexts.

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Infectious disease surveillance systems, including wastewater surveillance, can alert communities to the threat of emerging pathogens. We need methods to infer understanding of transmission dynamics from non-detection. We estimate a sensitivity of detection of poliovirus in wastewater to inform the sensitivity of wastewater surveillance for poliovirus using both a clinical epidemiology and fecal shedding approach. We then apply freedom from disease to estimate the sensitivity of the wastewater surveillance network. Estimated sensitivity to detect a single poliovirus infection was low, <11% at most wastewater treatment plants and <3% in most counties. However, the maximum threshold for the number of infections when polio is not detected in wastewater was much lower. Prospective wastewater surveillance can confirm the absence of a polio threat and be escalated in the case of poliovirus detection. These methods can be applied to any emerging or re-emerging pathogen, and improve understanding from wastewater surveillance.

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Large wastewater-based epidemiology (WBE) projects often have wide coverage and multiple sampling sites, necessitating spatial aggregation for data reporting and interpretation. However, the outcome may be impacted by a type of statistical bias called the modifiable areal unit problem (MAUP). In this study, we examined the presence and extent of the MAUP scaling effect on a New York State COVID-19 wastewater surveillance project. Specifically, we investigated three metrics: 1) the difference in wastewater SARS-CoV-2 concentrations between sampling at city-level site (i.e., city's primary wastewater treatment plant influent stream) and at upstream sampling sites; 2) the correlation between WBE data and clinical indicators at the WWTP-level and the more aggregated county-level; and 3) the proportion of population affected by misalignment of COVID-19 community risk levels at different spatial scales. The results showed that the MAUP can have a negative impact on risk perception by masking regions with high wastewater viral load or COVID-19 community risk level. On the other hand, the MAUP improved the correlation between wastewater surveillance and clinical measures by an average of 26.02 %. This is the first study to investigate the MAUP in the context of WBE and may encourage future WBE projects to consider the implications of the MAUP when interpreting and reporting spatial data, ultimately leading to better data representativeness and accuracy.

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Wastewater-based epidemiology (WBE) has been widely implemented around the world as a complementary tool to conventional surveillance techniques to inform and improve public health responses. Currently, wastewater surveillance programs in the U.S. are evaluating integrated approaches to address public health challenges across multiple domains, including substance abuse. In this work, we demonstrated the potential of online solid-phase extraction coupled with liquid chromatography–high-resolution mass spectrometry to support targeted quantification and nontargeted analysis of psychoactive and lifestyle substances as a step toward understanding the operational feasibility of a statewide wastewater surveillance program for substance use assessment in New York. Target screening confirmed 39 substances in influent samples collected from 10 wastewater treatment plants with varying sewershed characteristics and is anticipated to meet the throughput demands as the statewide program scales up to full capacity. Nontarget screening prioritized additional compounds for identification at three confidence levels, including psychoactive substances, such as opioid analgesics, phenethylamines, and cathinone derivatives. Consumption rates of 12 target substances detected in over 80% of wastewater samples were similar to those reported by previous U.S.-based WBE studies despite the uncertainty associated with back-calculations. For selected substances, the relative bias in consumption estimates was sensitive to variations in monitoring frequency, and factors beyond human excretion (e.g., as indicated by the parent-to-metabolite ratios) might also contribute to their prevalence at the sewershed scale. Overall, our study marks the initial phase of refining analytical workflows and data interpretation in preparation for the incorporation of substance use assessment into the statewide wastewater surveillance program in New York.

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Wastewater surveillance provides a cost-effective and non-invasive way to gain an understanding of infectious disease transmission including for COVID-19. We analyzed wastewater samples from one school site in Jefferson County, New York during the 2021–2022 school year. We tested for SARS-CoV-2 RNA once weekly and compared those results with the clinical COVID-19 cases in the school. The amount of SARS-CoV-2 RNA correlated with the number of incident COVID-19 cases, with the best correlation being one day lead time between the wastewater sample and the number of COVID-19 cases. The sensitivity and positive predictive value of wastewater surveillance to correctly identify any COVID-19 cases up to 7 days after a wastewater sample collection ranged from 82–100% and 59–78% respectively, depending upon the amount of SARS-CoV-2 RNA in the sample. The specificity and negative predictive value of wastewater surveillance to correctly identify when the school was without a case of COVID-19 ranged from 67–78% and 70–80%, respectively, depending upon the amount of SARS-CoV-2 RNA in the sample. The lead time observed in this study suggests that transmission might occur within a school before SARS-CoV-2 is identified in wastewater. However, wastewater surveillance should still be considered as a potential means of understanding school-level COVID-19 trends and is a way to enable precision public health approaches tailored to the epidemiologic situation in an individual school.

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COVID-19 saw the expansion of public health tools to manage the pandemic. One tool that saw extensive use was the public health dashboard, web-based visualization tools that communicate information to users in easy-to-read graphics. Dashboards were widely used prior to the pandemic, but COVID-19 saw expanded use and development. To date, dashboards have become an important part of public health surveillance programs around the world helping decisionmakers use data to evaluate different public health metrics including caseloads, hospitalizations, and environmental surveillance results from testing wastewater. Wastewater surveillance provides community-based, spatially relevant data on disease trends within communities to assess the scale of infection in a region, which makes it an excellent candidate for dashboard development to improve public health. We developed a dashboard for New York State's wastewater surveillance program using open-source, reproducible web programming. The dashboard we developed has been used for the COVID-19 response in New York, and our methods can be adapted to other programs and pathogens. We provide:

• descriptions of how the dashboard was developed and maintained

• specific guidance for reproducing our dashboard in other areas and for other pathogens

• fully reproducible code with step-by-step instructions for researchers and professionals to make their own data dashboards

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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.

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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.

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Background: Routine case surveillance data for SARS-CoV-2 are incomplete, unrepresentative, missing key variables of interest, and may be increasingly unreliable for timely surge detection and understanding the true burden of infection.

Methods: We conducted a cross-sectional survey of a representative sample of 1030 New York City (NYC) adult residents ≥18 years on May 7-8, 2022. We estimated the prevalence of SARS-CoV-2 infection during the preceding 14-day period. Respondents were asked about SARS-CoV-2 testing, testing outcomes, COVID-like symptoms, and contact with SARS-CoV-2 cases. SARS-CoV-2 prevalence estimates were age- and sex-adjusted to the 2020 U.S.

Population: We triangulated survey-based prevalence estimates with contemporaneous official SARS-CoV-2 counts of cases, hospitalizations, and deaths, as well as SARS-CoV-2 wastewater concentrations.

Results: We show that 22.1% (95% CI 17.9-26.2%) of respondents had SARS-CoV-2 infection during the two-week study period, corresponding to ~1.5 million adults (95% CI 1.3-1.8 million). The official SARS-CoV-2 case count during the study period is 51,218. Prevalence is estimated at 36.6% (95% CI 28.3-45.8%) among individuals with co-morbidities, 13.7% (95% CI 10.4-17.9%) among those 65+ years, and 15.3% (95% CI 9.6-23.5%) among unvaccinated persons. Among individuals with a SARS-CoV-2 infection, hybrid immunity (history of both vaccination and infection) is 66.2% (95% CI 55.7-76.7%), 44.1% (95% CI 33.0-55.1%) were aware of the antiviral nirmatrelvir/ritonavir, and 15.1% (95% CI 7.1-23.1%) reported receiving it. Hospitalizations, deaths and SARS-CoV-2 virus concentrations in wastewater remained well below that during the BA.1 surge.

Conclusions: Our findings suggest that the true magnitude of NYC's BA.2/BA.2.12.1 surge may have been vastly underestimated by routine case counts and wastewater surveillance. Hybrid immunity, bolstered by the recent BA.1 surge, likely limited the severity of the BA.2/BA.2.12.1 surge.

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Wastewater surveillance proved to be a successful early-warning, population level, trend detection system during the COVID-19 pandemic. Now, it is developing into a versatile tool for public health as laboratories start to use this testing to monitor for other pathogens of concern.

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Sewer systems provide many services to communities that have access to them beyond removal of waste and wastewater. Understanding of these systems’ geographic coverage is essential for wastewater-based epidemiology (WBE), which requires accurate estimates for the population contributing wastewater. Reliable estimates for the boundaries of a sewer service area or sewershed can be used to link upstream populations to wastewater samples taken at treatment plants or other locations within a sewer system. These geographic data are usually managed by public utilities, municipal offices, and some government agencies, however, there are no centralized databases for geographic information on sewer systems in New York State. We created a database for all municipal sewersheds in New York State for the purpose of supporting statewide wastewater surveillance efforts to support public health. We used a combination of public tax records with sewer access information, physical maps, and municipal records to organize and draw digital boundaries compatible with geographic information systems. The methods we employed to create these data will be useful to inform similar efforts in other jurisdictions and the data have many public health applications as well as being informative for water/environmental research and infrastructure projects.

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Wastewater surveillance of SARS-CoV-2 has been shown to be a valuable source of information regarding SARS-CoV-2 transmission and COVID-19 cases. Though the method has been used for several decades to track other infectious diseases, there has not been a comprehensive review outlining all of the pathogens that have been surveilled through wastewater. Herein we identify what infectious diseases have been previously studied via wastewater surveillance prior to the COVID-19 pandemic. Infectious diseases and pathogens were identified in 100 studies of wastewater surveillance across 38 countries, as well as themes of how wastewater surveillance and other measures of disease transmission were linked. Twenty-five separate pathogen families were identified in the included studies, with the majority of studies examining pathogens from the family Picornaviridae, including polio and non-polio enteroviruses. Most studies of wastewater surveillance did not link what was found in the wastewater to other measures of disease transmission. Among those studies that did, the value reported varied by study. Wastewater surveillance should be considered as a potential tool for many infectious diseases. Wastewater surveillance studies can be improved by incorporating other measures of disease transmission at the population-level including disease incidence and hospitalizations.

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What is already known about this topic?

In July 2022, a case of paralytic poliomyelitis was confirmed in an unvaccinated adult Rockland County, New York resident; environmental sampling found evidence of poliovirus transmission.

What is added by this report?

Wastewater testing has identified circulating polioviruses genetically related to virus isolated from the Rockland County patient in at least five New York counties.

What are the implications for public health practice?

Public health efforts to prevent polio should focus on improving coverage with inactivated polio vaccine. Although most persons in the United States are sufficiently immunized, unvaccinated or undervaccinated persons living or working in Kings, Orange, Queens, Rockland, or Sullivan counties, New York should complete the polio vaccination series to prevent additional paralytic cases and curtail transmission.

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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.

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What is already known about this topic?

Sustained poliovirus transmission has been eliminated from the United States for approximately 40 years; vaccines are highly effective in preventing paralysis after exposure.

What is added by this report?

In June 2022, poliovirus was confirmed in an unvaccinated immunocompetent adult resident of New York hospitalized with flaccid lower limb weakness. Vaccine-derived poliovirus type 2 was isolated from the patient and identified from wastewater samples in two neighboring New York counties.

What are the implications for public health practice?

Unvaccinated persons in the United States remain at risk for paralytic poliomyelitis if they are exposed to either wild or vaccine-derived poliovirus; all persons in the United States should stay up to date on recommended poliovirus vaccination.

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Wastewater surveillance is a cost-effective way to monitor pathogen prevalence and transmission patterns in the entire community. Here, we compare 24-hour composite and grab samples collected during September 2020 from several municipalities in New York State to detect SARS-CoV-2. A total of 45 paired samples (90 total samples) from three counties and 14 wastewater treatment plants were available for analysis. The categorical comparison (SARS-CoV-2 genetic material detected and quantifiable, genetic material detected but below the limits of quantification, and genetic material not detected) between the grab and composite samples was quite strong, with 91.1% agreement (kappa P-value < .001). The correlations among the quantifiable grab and composite samples were statistically significant yet modest for SARS2-CoV RNA (Pearson correlation = 0.44, P = .02), crAssphage cDNA (Pearson correlation = 0.36, P = .02), and crAssphage DNA (Pearson correlation = 0.46, P = .002). We found good comparison between grab and 24-hour composite samples for detecting SARS-CoV-2 RNA from municipal wastewater treatment plants. Grab sampling is an efficient and cost-effective method to monitor for the presence of SARS-CoV-2 in the entire community.

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A residential building's wastewater presents a potential non-invasive method of surveilling numerous infectious diseases, including SARS-CoV-2. We analyzed wastewater from 16 different residential locations at Syracuse University (Syracuse, NY, USA) during fall semester 2020, testing for SARS-CoV-2 RNA twice weekly and compared the presence of clinical COVID-19 cases to detection of the viral RNA in wastewater. The sensitivity of wastewater surveillance to correctly identify dormitories with a case of COVID-19 ranged from 95% (95% confidence interval [CI] = 76-100%) on the same day as the case was diagnosed to 73% (95% CI = 53-92%), with 7 days lead time of wastewater. The positive predictive value ranged from 20% (95% CI = 13-30%) on the same day as the case was diagnosed to 50% (95% CI = 40-60%) with 7 days lead time. The specificity of wastewater surveillance to correctly identify dormitories without a case of COVID-19 ranged from 60% (95% CI = 52-67%) on the day of the wastewater sample to 67% (95% CI = 58-74%) with 7 days lead time. The negative predictive value ranged from 99% (95% CI = 95-100%) on the day of the wastewater sample to 84% (95% CI = 77-91%) with 7 days lead time. Wastewater surveillance for SARS-CoV-2 at the building level is highly accurate in determining if residents have a COVID-19 infection. Particular benefit is derived from negative wastewater results that can confirm a building is COVID-19 free.

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Infectious disease surveillance is vitally important to maintaining health security, but these efforts are challenged by the pace at which new pathogens emerge. Wastewater surveillance can rapidly obtain population-level estimates of disease transmission, and we leverage freedom from disease principles to make use of nondetection of SARS-CoV-2 in wastewater to estimate the probability that a community is free from SARS-CoV-2 transmission. From wastewater surveillance of 24 treatment plants across upstate New York from May through December of 2020, trends in the intensity of SARS-CoV-2 in wastewater correlate with trends in COVID-19 incidence and test positivity (⍴ > 0.5), with the greatest correlation observed for active cases and a 3-day lead time between wastewater sample date and clinical test date. No COVID-19 cases were reported 35% of the time the week of a nondetection of SARS-CoV-2 in wastewater. Compared to the United States Centers for Disease Control and Prevention levels of transmission risk, transmission risk was low (no community spared) 50% of the time following nondetection, and transmission risk was moderate or lower (low community spread) 92% of the time following nondetection. Wastewater surveillance can demonstrate the geographic extent of the transmission of emerging pathogens, confirming that transmission risk is either absent or low and alerting of an increase in transmission. If a statewide wastewater surveillance platform had been in place prior to the onset of the COVID-19 pandemic, policymakers would have been able to complement the representative nature of wastewater samples to individual testing, likely resulting in more precise public health interventions and policies.

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Response to the COVID-19 (coronavirus disease 2019) pandemic saw an unprecedented uptake in bottom-up efforts to incorporate community wastewater testing to inform public health. While not a new strategy, various specialized scientific advancements were achieved to establish links between wastewater concentrations of SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2) and public health outcomes. Maximizing public health benefit requires collaboration among a broad range of disciplinary experts, each bringing their own historical context to the central goal of protecting human health. One challenge has been a lack of shared terminology. Standardized terminology would provide common ground for this rapidly growing field. Based on the review herein, we recommend categorical usage of the term ‘wastewater-based epidemiology’ to describe the science of relating microbes, chemicals or other analytes in wastewater to public health. We further recommend the term ‘wastewater surveillance’ to describe continuous monitoring of health outcomes (either microbes or chemicals) via wastewater. We suggest that ‘wastewater tracking’ and ‘wastewater tracing’ be used in more narrow ways, specifically when trying to find the source of a health risk. Finally, we suggest that the phrase ‘wastewater monitoring’ be abandoned, except in rare circumstances when ensuring wastewater discharge is safe from a public health perspective.

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Wastewater surveillance of SARS-CoV-2 RNA is increasingly being incorporated into public health efforts to respond to the COVID-19 pandemic. In order to obtain the maximum benefit from these efforts, approaches to wastewater monitoring need to be rapid, sensitive, and relatable to relevant epidemiological parameters. In this study, we present an ultracentrifugation-based method for the concentration of SARS-CoV-2 wastewater RNA and use crAssphage, a bacteriophage specific to the human gut, to help account for RNA loss during transit in the wastewater system and sample processing. With these methods, we were able to detect, and sometimes quantify, SARS-CoV-2 RNA from 20 mL wastewater samples within as little as 4.5 hours. Using known concentrations of bovine coronavirus RNA and deactivated SARS-CoV-2, we estimate recovery rates of approximately 7-12% of viral RNA using our method. Results from 24 sewersheds across Upstate New York during the spring and summer of 2020 suggested that stronger signals of SARS-CoV-2 RNA from wastewater may be indicative of greater COVID-19 incidence in the represented service area approximately one week in advance. SARS-CoV-2 wastewater RNA was quantifiable in some service areas with daily positives tests of less than 1 per 10,000 people or when weekly positive test rates within a sewershed were as low as 1.7%. crAssphage DNA concentrations were significantly lower during periods of high flow in almost all areas studied. After accounting for flow rate and population served, crAssphage levels per capita were estimated to be about 1.35 × 1011 and 2.42 × 108 genome copies per day for DNA and RNA, respectively. A negative relationship between per capita crAssphage RNA and service area size was also observed likely reflecting degradation of RNA over long transit times. Our results reinforce the potential for wastewater surveillance to be used as a tool to supplement understanding of infectious disease transmission obtained by traditional testing and highlight the potential for crAssphage co-detection to improve interpretations of wastewater surveillance data.

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Wastewater surveillance for the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is an emerging approach to help identify the risk of a coronavirus disease (COVID-19) outbreak. This tool can contribute to public health surveillance at both community (wastewater treatment system) and institutional (e.g., colleges, prisons, and nursing homes) scales. This paper explores the successes, challenges, and lessons learned from initial wastewater surveillance efforts at colleges and university systems to inform future research, development and implementation. We present the experiences of 25 college and university systems in the United States that monitored campus wastewater for SARS-CoV-2 during the fall 2020 academic period. We describe the broad range of approaches, findings, resources, and impacts from these initial efforts. These institutions range in size, social and political geographies, and include both public and private institutions. Our analysis suggests that wastewater monitoring at colleges requires consideration of local information needs, sewage infrastructure, resources for sampling and analysis, college and community dynamics, approaches to interpretation and communication of results, and follow-up actions. Most colleges reported that a learning process of experimentation, evaluation, and adaptation was key to progress. This process requires ongoing collaboration among diverse stakeholders including decision-makers, researchers, faculty, facilities staff, students, and community members.

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Wastewater entering sewer networks represents a unique source of pooled epidemiological information. In this study, we coupled online solid-phase extraction with liquid chromatography-high resolution mass spectrometry to achieve high-throughput analysis of health and lifestyle-related substances in untreated municipal wastewater during the coronavirus disease 2019 (COVID-19) pandemic. Twenty-six substances were identified and quantified in influent samples collected from six wastewater treatment plants during the COVID-19 pandemic in central New York. Over a 12 week sampling period, the mean summed consumption rate of six major substance groups (i.e., antidepressants, antiepileptics, antihistamines, antihypertensives, synthetic opioids, and central nervous system stimulants) correlated with disparities in household income, marital status, and age of the contributing populations as well as the detection frequency of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) RNA in wastewater and the COVID-19 test positivity in the studied sewersheds. Nontarget screening revealed the covariation of piperine, a nontarget substance, with SARS-CoV-2 RNA in wastewater collected from one of the sewersheds. Overall, this proof-of-the-concept study demonstrated the utility of high-throughput wastewater analysis for assessing the population-level substance use patterns during a public health crisis such as COVID-19.

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Understanding the full extent of the COVID-19 pandemic is an ongoing challenge for public-health officials. Any epidemiological indicator has biases and limitations. Diagnostic testing capacity may be insufficient; hospitalizations lag infections by weeks and do not report on people with mild or asymptomatic disease. Experience with other viral diseases has shown that monitoring sewage for traces of a pathogen enables effective surveillance of entire communities, providing a sensitive signal of whether the pathogen is present in the population and whether transmission is increasing or declining. Researchers around the world are now pursuing the same approach for COVID-19 with the hope that wastewater data can supplement current measures of its prevalence. The novel coronavirus, SARS-CoV-2, has already been detected in wastewater1,2. In this issue, Peccia et al.3demonstrate that concentrations of SARS-CoV-2 RNA in primary sewage sludge tracked COVID-19 cases and hospital admissions during the early weeks of the outbreak in the New Haven, Connecticut, area. Departing from traditional methods of examining wastewater, they report a high-resolution dataset generated from sewage sludge rather than influent and apply statistical analysis to infer the lead time their data may provide over epidemiological indicators. Their results strengthen the evidence that wastewater monitoring could be a powerful tool in tracking the spread of COVID-19.

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