A multistate assessment of population normalization factors for wastewater-based epidemiology of COVID-19

Tracking COVID-19 trends in communities with low population by wastewater-based surveillance

Wastewater-based surveillance (WBS) of SARS-CoV-2 is increasingly recognized as a valuable complement to clinical reporting for estimating COVID-19 infection rates. This acceptance stems from the strong correlation found between wastewater and clinical case data during the early stages of the pandemic. However, the cessation of COVID-19 restrictions, changes in clinical testing requirements by late 2021, and the widespread use of take-home antigen tests have diminished the reliability and volume of clinically reported case counts. This study explores the dynamics between clinical cases and wastewater-based results in a period of transition, focusing on student residential areas within a university campus. We analyzed wastewater from 13 sub-sewersheds, serving populations of 300 to 4000 individuals, three times weekly from December 2021 to June 2022. The analysis revealed two COVID-19 spikes in wastewater data during this time, whereas clinical reports indicated at most a single surge in infections across most communities. Further, in the first infection surge, clinical data plateaued sooner than wastewater trends and, in the second surge, either lagged or were completely absent. Correlations between wastewater SARS-CoV-2 concentrations and the 3-day rolling average of clinical cases were weak in smaller communities (≤1000 people) but improved with larger community sizes (>1000 people). Normalization with PMMoV did not enhance these correlations. Given the challenges in executing widespread and accurate mass clinical testing, our findings advocate for the efficacy of WBS data in reliably forecasting infection surges, even in less populous settings, thereby facilitating swift, informed public health interventions.

Evaluating small extracellular vesicle-based vaccination across heterologous Salmonella strains isolated from wastewater

Salmonella infections pose significant public health challenges worldwide. The diversity of Salmonella strains, particularly those isolated from environmental and clinical sources, necessitates innovative approaches to prevention and treatment. Previous research has shown that small extracellular vesicles (sEVs) produced by macrophages during Salmonella Typhimurium infection can induce robust immune responses when used as a vaccine, offering complete protection in systemic infection models. In this study, we isolated 120 Salmonella strains from qPCR invA-positive wastewater samples collected in Gainesville, FL. These strains underwent enrichment, selection, and biochemical confirmation, followed by serotyping and whole genome sequencing. Two isolates, Salmonella enterica subsp. diarizonae (Diarizonae) and S. enterica serovar Enteritidis, were selected for further analysis based on community prevalence and clinical severity. We also assessed the ability of sEVs produced by S. Typhimurium-infected macrophages to induce immune responses against these heterologous and circulating strains in mice. Immunization with sEVs induced robust antigen-specific SIgA and IgG responses against S. Typhimurium, Enteritidis, and Diarizonae, with high titers observed in sera and fecal samples. Proteomic analysis revealed differential expression of proteins in these strains, including antigenic proteins present in sEVs such as OmpA, FliC, or OmpD. Moreover, this study highlights the role of wastewater-based epidemiology (WBE) as a tool for environmental surveillance, offering a complementary perspective on Salmonella dynamics within a population. Integrating WBE with traditional surveillance methods, along with the promising results of sEV-based vaccination, provides a pragmatic strategy for developing effective preventative measures against Salmonella infections, addressing the diversity of non-typhoidal Salmonella strains.

Evaluation of plasmid pBI143 for its optimal concentration methods, seasonal impact, and potential as a normalization parameter in wastewater-based epidemiology

Plasmid pBI143, abundant in the human gut, is a promising human-specific fecal marker. However, studies on its optimal concentration methods, seasonal variations, and potential as a normalization parameter for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the causative agent of coronavirus disease 2019 (COVID-19), remain limited. Among the three concentration methods compared, polyethylene glycol (PEG) precipitation and centrifugation demonstrated comparable efficiencies (9.3 ± 0.6 and 9.2 ± 0.6 log10 copies/L, respectively; n = 8 each), outperforming membrane filtration (8.0 ± 0.6 log10 copies/L; n = 8). PEG precipitation was further applied to quantify pBI143, together with other human-specific fecal markers (crAssphage and pepper mild mottle virus (PMMoV)), in 52 wastewater samples collected weekly over a one year from a wastewater treatment plant in Yamanashi Prefecture, Japan, by quantitative polymerase chain reaction. The higher pBI143 concentrations (9.6 ± 0.5 log10 copies/L) compared to PMMoV (8.2 ± 0.2 log10 copies/L) and crAssphage (8.0 ± 0.2 log10 copies/L) highlighted its potential as a robust marker for human fecal contamination. Unlike PMMoV and crAssphage that remained stable across seasons, pBI143 showed seasonal fluctuations, especially during summer and autumn, suggesting its greater sensitivity to environmental conditions. The study evaluated the suitability of pBI143, crAssphage, and PMMoV for normalizing SARS-CoV-2 concentrations in wastewater; however, non-normalized SARS-CoV-2 concentrations showed the highest correlation with COVID-19 cases (ρ = 0.74), whereas normalization reduced this correlation (PMMoV-normalized, ρ = 0.72; crAssphage-normalized, ρ = 0.70; and pBI143-normalized, ρ = 0.50), likely due to differences in the persistence and structural properties of the markers, indicating that these markers are less effective for SARS-CoV-2 normalization. This study underscores the promising utility of pBI143 in wastewater surveillance but highlights the need for further research across diverse regions to validate its applicability.

Wastewater-Based Epidemiological Surveillance in France: The SUM'EAU Network

Wastewater surveillance is a powerful public health tool which gained global prominence during the COVID-19 pandemic. This article describes the development and implementation of the national wastewater surveillance network in France: SUM'EAU. Preliminary work included defining a sampling strategy, evaluating/optimising analytical methods, launching a call for tenders to select network laboratories and producing wastewater monitoring indicators. SUM'EAU was then deployed in three stages: (i) a pilot study, (ii) the transfer of analytical activities from the National Reference Laboratory to four selected network laboratories, and (iii) the extension of the system to additional sampling sites. Currently, SUM'EAU monitors SARS-CoV-2 across 54 wastewater treatment plants in mainland France. Once a week on business days, 24 h flow-proportional composite samples are collected at plant inlets and transported at 5 °C (±3 °C) to partner laboratories for analysis. The analytical process involves sample concentration, RNA extraction, and digital RT-PCR/q-RT-PCR to detect and quantify the presence of the SARS-CoV-2 genome in wastewater. Subsequently, data are transferred to Santé publique France, the French National Public Health Agency, for analysis and interpretation. While SUM'EAU has been instrumental in monitoring the COVID-19 pandemic and holds significant potential for broader application, securing sustainable funding for its operation remains a major challenge.

Environmental Dissemination of SARS-CoV-2: An Analysis Employing Crassphage and Next-Generation Sequencing Protocols

This study aimed to investigate the dissemination of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in water samples obtained during the coronavirus disease 2019 pandemic period, employing cross-assembly phage (crAssphage) as a fecal contamination biomarker and next-generation sequencing protocols to characterize SARS-CoV-2 variants. Raw wastewater and surface water (stream and sea) samples were collected for over a month in Rio de Janeiro, Brazil. Ultracentrifugation and negatively charged membrane filtration were employed for viral concentration of the wastewater and surface water samples, respectively. Viruses were detected and quantified by (RT-)qPCR applying TaqMan® system protocols. SARS-CoV-2 RNA signals were detected in 92.5% (37/40) of the wastewater samples and in 31.25% (10/32) of the stream water samples, but not in seawater samples. CrAssphage was detected in 100% of the wastewater samples, 93.75% (30/32) of the stream samples, and in 2/4 of the seawater samples. CrAssphage detection and high concentrations in stream surface waters (median 8.95 log10 gc/L) revealed diffuse contamination by domestic wastewater in a region with high sanitary coverage. The correlations detected between SARS-CoV-2 data and the moving averages of clinical cases per capita over the sampling period were moderate to strong when applying a 13-day offset, regardless of normalization by crAssphage data or not. Sequencing of the receptor-binding domain of the spike protein confirmed the detection of SARS-CoV-2, but did not characterize the circulating variant. On the other hand, the whole genome sequencing protocol identified circulation of the Gamma variant, corroborating the sampling period clinical data.

Estimating rates of change to interpret quantitative wastewater surveillance of disease trends

BACKGROUND

Wastewater monitoring data can be used to estimate disease trends to inform public health responses. One commonly estimated metric is the rate of change in pathogen quantity, which typically correlates with clinical surveillance in retrospective analyses. However, the accuracy of rate of change estimation approaches has not previously been evaluated.

OBJECTIVES

We assessed the performance of approaches for estimating rates of change in wastewater pathogen loads by generating synthetic wastewater time series data for which rates of change were known. Each approach was also evaluated on real-world data.

METHODS

Smooth trends and their first derivatives were jointly sampled from Gaussian processes (GP) and independent errors were added to generate synthetic viral load measurements; the range hyperparameter and error variance were varied to produce nine simulation scenarios representing different potential disease patterns. The directions and magnitudes of the rate of change estimates from four estimation approaches (two established and two developed in this work) were compared to the GP first derivative to evaluate classification and quantitative accuracy. Each approach was also implemented for public SARS-CoV-2 wastewater monitoring data collected January 2021-May 2023 at 25 sites in North Carolina, USA.

RESULTS

All four approaches inconsistently identified the correct direction of the trend given by the sign of the GP first derivative. Across all nine simulated disease patterns, between a quarter and a half of all estimates indicated the wrong trend direction, regardless of estimation approach. The proportion of trends classified as plateaus (statistically indistinguishable from zero) for the North Carolina SARS-CoV-2 data varied considerably by estimation method but not by site.

DISCUSSION

Our results suggest that wastewater measurements alone might not provide sufficient data to reliably track disease trends in real-time. Instead, wastewater viral loads could be combined with additional public health surveillance data to improve predictions of other outcomes.

Effective method to mitigate impact of rain or snowmelt sewer flushing events on wastewater-based surveillance measurements

Wastewater-based surveillance (WBS) is increasingly used for monitoring disease targets in wastewaters around the world. This study, performed in Ottawa, Canada, identifies a decrease in SARS-CoV-2 wastewater measurements during snowmelt-induced sewer flushing events. Observations first revealed a correlation between suppressed viral measurements and periods of increased sewage flowrates, air temperatures above 0 °C during winter months, and solids mass flux increases. These correlations suggest that high sewage flowrates from snowmelt events or intense precipitation events lead to the scouring of previously settled solids in sewers and the subsequent entrainment of these solids into the transported wastewaters. Collection of WBS samples during flushing events hence contains a heterogeneous mixture of solids, including resuspended solids with varying degrees of decay. Therefore flushing events can present a challenge for accurately measuring disease target viral signals when using solids-based analytical methods. This study demonstrates that resuspended solids entrained in the wastewaters during flushing events retain PMMoV signal while the SARS-CoV-2 signal is significantly reduced due to the slower decay rate of pepper mild mottle virus (PMMoV) compared to SARS-CoV-2 within wastewaters. Hence current normalization methods using PMMoV are shown to be ineffective in correcting for flushing events and the associated resuspension of settled solids, as the PMMoV signal of settled solids within sewers does not account for the differential decay rates experiences by SARS-CoV-2 signal in settled solids. Instead, this study identifies RNA to PMMoV correction factor as an effective approach to correct for flushing events and to realign SARS-CoV-2 signal with COVID-19 hospital admission rates within communities. As such, the study highlights the key physicochemical parameters necessary to identify flushing events that affect SARS-CoV-2 WBS measurements and introduces a novel RNA to PMMoV correction factor approach for solids-based analysis of SARS-CoV-2 during flushing events, enhancing the accuracy of WBS data for public health decision-making.

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

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

Leveraging wastewater-based epidemiology to monitor the spread of neglected tropical diseases in African communities

Neglected tropical diseases continue to cause a significant burden worldwide, with Africa accounting for more than one-third of the global burden. Over the past decade, progress has been made in eliminating, controlling, and eradicating these diseases in Africa. By December 2022, 47 out of 54 African countries had eliminated at least one neglected tropical disease, and more countries were close to achieving this milestone. Between 2020 and 2021, there was an 80 million reduction in people requiring intervention. However, continued efforts are needed to manage neglected tropical diseases and address their social and economic burden, as they deepen marginalisation and stigmatisation. Wastewater-based epidemiology involves analyzing wastewater to detect and quantify biomarkers of disease-causing pathogens. This approach can complement current disease surveillance systems in Africa and provide an additional layer of information for monitoring disease spread and detecting outbreaks. This is particularly important in Africa due to limited traditional surveillance methods. Wastewater-based epidemiology also provides a tsunami-like warning system for neglected tropical disease outbreaks and can facilitate timely intervention and optimised resource allocation, providing an unbiased reflection of the community's health compared to traditional surveillance systems. In this review, we highlight the potential of wastewater-based epidemiology as an innovative approach for monitoring neglected tropical disease transmission within African communities and improving existing surveillance systems. Our analysis shows that wastewater-based epidemiology can enhance surveillance of neglected tropical diseases in Africa, improving early detection and management of Buruli ulcers, hookworm infections, ascariasis, schistosomiasis, dengue, chikungunya, echinococcosis, rabies, and cysticercosis for better disease control.

A framework for integrating wastewater-based epidemiology and public health

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

Graphene Multiplexed Sensor for Point-of-Need Viral Wastewater-Based Epidemiology

Wastewater-based epidemiology (WBE) can help mitigate the spread of respiratory infections through the early detection of viruses, pathogens, and other biomarkers in human waste. The need for sample collection, shipping, and testing facilities drives up the cost of WBE and hinders its use for rapid detection and isolation in environments with small populations and in low-resource settings. Given the ubiquitousness and regular outbreaks of respiratory syncytial virus, SARS-CoV-2, and various influenza strains, there is a rising need for a low-cost and easy-to-use biosensing platform to detect these viruses locally before outbreaks can occur and monitor their progression. To this end, we have developed an easy-to-use, cost-effective, multiplexed platform able to detect viral loads in wastewater with several orders of magnitude lower limit of detection than that of mass spectrometry. This is enabled by wafer-scale production and aptamers preattached with linker molecules, producing 44 chips at once. Each chip can simultaneously detect four target analytes using 20 transistors segregated into four sets of five for each analyte to allow for immediate statistical analysis. We show our platform's ability to rapidly detect three virus proteins (SARS-CoV-2, RSV, and Influenza A) and a population normalization molecule (caffeine) in wastewater. Going forward, turning these devices into hand-held systems would enable wastewater epidemiology in low-resource settings and be instrumental for rapid, local outbreak prevention.

Assessment of crAssphage as a biological variable for SARS-CoV-2 data normalization in wastewater surveillance

AIMS

This study aimed to assess the use of cross-assembled phage (crAssphage) as an endogenous control employing a multivariate normalization analysis and its application as a severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) data normalizer.

METHODS AND RESULTS

A total of 188 twelve-hour composite raw sewage samples were obtained from eight wastewater treatment plants (WWTP) during a 1-year monitoring period. Employing the N1 and N2 target regions, SARS-CoV-2 RNA was detected in 94% (177) and 90% (170) of the samples, respectively, with a global median of 5 log10 genomic copies per liter (GC l-1). CrAssphage was detected in 100% of the samples, ranging from 8.29 to 10.43 log10 GC l-1, with a median of 9.46 ± 0.40 log10 GC l-1, presenting both spatial and temporal variabilities.

CONCLUSIONS

Although SARS-CoV-2 data normalization employing crAssphage revealed a correlation with clinical cases occurring during the study period, crAssphage normalization by the flow per capita per day of each WWTP increased this correlation, corroborating the importance of normalizing wastewater surveillance data in disease trend monitoring.

A Novel Tiled Amplicon Sequencing Assay Targeting the Tomato Brown Rugose Fruit Virus (ToBRFV) Genome Reveals Widespread Distribution in Municipal Wastewater Treatment Systems in the Province of Ontario, Canada

Tomato Brown Rugose Fruit Virus (ToBRFV) is a plant pathogen that infects important Solanaceae crop species and can dramatically reduce tomato crop yields. The ToBRFV has rapidly spread around the globe due to its ability to escape detection by antiviral host genes which confer resistance to other tobamoviruses in tomato plants. The development of robust and reproducible methods for detecting viruses in the environment aids in the tracking and reduction of pathogen transmission. We detected ToBRFV in municipal wastewater influent (WWI) samples, likely due to its presence in human waste, demonstrating a widespread distribution of ToBRFV in WWI throughout Ontario, Canada. To aid in global ToBRFV surveillance efforts, we developed a tiled amplicon approach to sequence and track the evolution of ToBRFV genomes in municipal WWI. Our assay recovers 95.7% of the 6393 bp ToBRFV RefSeq genome, omitting the terminal 5' and 3' ends. We demonstrate that our sequencing assay is a robust, sensitive, and highly specific method for recovering ToBRFV genomes. Our ToBRFV assay was developed using existing ARTIC Network resources, including primer design, sequencing library prep, and read analysis. Additionally, we adapted our lineage abundance estimation tool, Alcov, to estimate the abundance of ToBRFV clades in samples.

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

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

Correlation of wastewater surveillance data with traditional influenza surveillance measures in Cook County, Illinois, October 2022-April 2023

Influenza is a respiratory illness that can result in serious outcomes, particularly among persons who are immunocompromised, aged <5 years or aged >65 years. Traditional influenza surveillance approaches rely upon syndromic surveillance of emergency departments and public health reporting from clinicians and laboratories. Wastewater surveillance infrastructure developed to monitor SARS-CoV-2 is being used for influenza surveillance in the Chicago area. The goal was to evaluate timeliness and correlations between influenza virus detected through wastewater surveillance and traditional influenza surveillance measures to assess utility of wastewater surveillance for influenza at the county level. Specifically, we measured correlations between influenza virus gene copies in wastewater samples and 1) the number of intensive care unit admissions associated with a diagnosis of influenza, 2) the percentage emergency department (ED) visits for influenza-like-illness, and 3) the percentage of ED visits with influenza diagnosis at discharge2 in Cook County. Influenza concentrations in wastewater were strongly correlated with traditional influenza surveillance measures, particularly for catchment areas serving >100,000 residents. Wastewater indicators lagged traditional influenza surveillance measures by approximately one week when analyzed in cross-correlations. Although wastewater data lagged traditional influenza surveillance measures in this analysis, it can serve as a useful surveillance tool as a complement to syndromic surveillance; it is a form of influenza surveillance that does not rely on healthcare-seeking behavior or reporting by healthcare providers.

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.

Quantitative analysis of SARS-CoV-2 RNA in wastewater and evaluation of sampling frequency during the downward period of a COVID-19 wave in Japan

Wastewater-based epidemiology (WBE) is a practical approach for detecting the presence of SARS-CoV-2 infections and assessing the epidemic trend of the coronavirus disease 2019 (COVID-19). The purpose of this study was to evaluate the minimum sampling frequency required to properly identify the COVID-19 trend during the downward epidemic period when using a highly sensitive RNA detection method. WBE was conducted using the Efficient and Practical virus Identification System with ENhanced Sensitivity for Solids (EPISENS-S), a highly sensitive SARS-CoV-2 RNA detection method, at nine neighboring wastewater treatment plants (WWTPs). These WWTPs were in the same prefecture in Japan, and they had different sewer types, sampling methods, and sampling frequencies. The overall detection rate of SARS-CoV-2 RNA was 97.8 % during the entire study period when the geometric means of new COVID-19 cases per 100,000 inhabitants were between 3.3 and 7.7 in each WWTP. The maximum SARS-CoV-2 RNA concentration in wastewater was 2.14 × 104 copies/L, which corresponded to pepper mild mottle virus (PMMoV)-normalized concentrations of 6.54 × 10-3. We evaluated the effect of sampling frequencies on the probability of a significant correlation with the number of newly reported COVID-19 cases by hypothetically reducing the sampling frequency in the same dataset. When the wastewater sampling frequency occurred 5, 3, 2, and 1 times per week, these results exhibited significant correlations of 100 % (5/5), 89 % (8/9), 85 % (23/27), and 48 % (13/27), respectively. To achieve significant correlation with a high probability of over 85 %, a minimum sampling frequency of twice per week is required, even if sampling methods and sewer types are different. WBE using the EPISENS-S method and a sampling frequency of more than twice a week can be used to properly monitor COVID-19 wave epidemic trends, even during downward periods.

Simultaneous detection and quantification of multiple pathogen targets in wastewater

Wastewater-based epidemiology has emerged as a critical tool for public health surveillance, building on decades of environmental surveillance work for pathogens such as poliovirus. Work to date has been limited to monitoring a single pathogen or small numbers of pathogens in targeted studies; however, few studies consider simultaneous quantitative analysis of a wide variety of pathogens, which could greatly increase the utility of wastewater surveillance. We developed a novel quantitative multi-pathogen surveillance approach (35 pathogen targets including bacteria, viruses, protozoa, and helminths) using TaqMan Array Cards (TAC) and applied the method on concentrated wastewater samples collected at four wastewater treatment plants in Atlanta, GA from February to October of 2020. From sewersheds serving approximately 2 million people, we detected a wide range of targets including many we expected to find in wastewater (e.g., enterotoxigenic E. coli and Giardia in 97% of 29 samples at stable concentrations) as well as unexpected targets including Strongyloides stercoralis (a human threadworm rarely observed in the USA). Other notable detections included SARS-CoV-2, but also several pathogen targets that are not commonly included in wastewater surveillance like Acanthamoeba spp., Balantidium coli, Entamoeba histolytica, astrovirus, norovirus, and sapovirus. Our data suggest broad utility in expanding the scope of enteric pathogen surveillance in wastewaters, with potential for application in a variety of settings where pathogen quantification in fecal waste streams can inform public health surveillance and selection of control measures to limit infections.

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.

Separating signal from noise in wastewater data: An algorithm to identify community-level COVID-19 surges in real time

Wastewater monitoring has provided health officials with early warnings for new COVID-19 outbreaks, but to date, no approach has been validated to distinguish signal (sustained surges) from noise (background variability) in wastewater data to alert officials to the need for heightened public health response. We analyzed 62 wk of data from 19 sites participating in the North Carolina Wastewater Monitoring Network to characterize wastewater metrics around the Delta and Omicron surges. We found that wastewater data identified outbreaks 4 to 5 d before case data (reported on the earlier of the symptom start date or test collection date), on average. At most sites, correlations between wastewater and case data were similar regardless of how wastewater concentrations were normalized and whether calculated with county-level or sewershed-level cases, suggesting that officials may not need to geospatially align case data with sewershed boundaries to gain insights into disease transmission. Although wastewater trend lines captured clear differences in the Delta versus Omicron surge trajectories, no single wastewater metric (detectability, percent change, or flow-population normalized viral concentrations) reliably signaled when these surges started. After iteratively examining different combinations of these three metrics, we developed the Covid-SURGE (Signaling Unprecedented Rises in Groupwide Exposure) algorithm, which identifies unprecedented signals in the wastewater data. With a true positive rate of 82%, a false positive rate of 7%, and strong performance during both surges and in small and large sites, our algorithm provides public health officials with an automated way to flag community-level COVID-19 surges in real time.