SARS-CoV-2 wastewater surveillance data can predict hospitalizations and ICU admissions

Seasonality of respiratory, enteric, and urinary viruses revealed by wastewater genomic surveillance

Wastewater surveillance can reveal population-level infectious disease burden and emergent public health threats can be reliably assessed through wastewater surveillance. While molecular methods for wastewater monitoring of microorganisms have traditionally relied on PCR-based approaches, next-generation sequencing (NGS) can provide deeper insights via genomic analyses of multiple diverse pathogens. We conducted a year-long sequencing surveillance of 1,408 composite wastewater samples collected from 12 neighborhood-level access points in the greater Tempe area, Arizona, USA, and show that variation in wastewater viruses is driven by seasonal time and location. The temporal dynamics of viruses in wastewater were influenced cyclically, with the most dissimilarity between samples 23 weeks apart (i.e., winter vs summer, spring vs fall). We identified diverse urinary and enteric viruses including polyomaviruses, astroviruses, and noroviruses, and showed that their genotypes/subtypes shifted across seasons. We show that while wastewater data of certain respiratory viruses like severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) strongly correlate with clinical case rates, laboratory-reported case incidences were discordant with surges of high viral load in wastewater for other viruses like human coronavirus 229E. These results demonstrate the utility of wastewater sequencing for informing decision-making in public health.IMPORTANCEWastewater surveillance can provide insights into the spread of pathogens in communities. Advances in next-generation sequencing (NGS) methodologies allow for more precise detection of viruses in wastewater. Long-term wastewater surveillance of viruses is an important tool for public health preparedness. This system can act as a public health observatory that gives real-time early warning for infectious disease outbreaks and improved response times.

Simultaneous detection of influenza A, B and respiratory syncytial virus in wastewater samples by one-step multiplex RT-ddPCR assay

BACKGROUND

After the occurrence of the COVID-19 pandemic, detection of other disseminated respiratory viruses using highly sensitive molecular methods was declared essential for monitoring the spread of health-threatening viruses in communities. The development of multiplex molecular assays are essential for the simultaneous detection of such viruses even at low concentrations. In the present study, a highly sensitive and specific multiplex one-step droplet digital PCR (RT-ddPCR) assay was developed for the simultaneous detection and absolute quantification of influenza A (IAV), influenza B (IBV), respiratory syncytial virus (RSV), and beta-2-microglobulin transcript as an endogenous internal control (IC B2M).

RESULTS

The assay was first evaluated for analytical sensitivity and specificity, linearity, reproducibility, and recovery rates with excellent performance characteristics and then applied to 37 wastewater samples previously evaluated with commercially available and in-house quantitative real-time reverse transcription PCR (RT-qPCR) assays. IAV was detected in 16/37 (43%), IBV in 19/37 (51%), and RSV in 10/37 (27%) of the wastewater samples. Direct comparison of the developed assay with real-time RT-qPCR assays showed statistically significant high agreement in the detection of IAV (kappa Cohen's correlation coefficient: 0.834, p = 0.001) and RSV (kappa: 0.773, p = 0.001) viruses between the two assays, while the results for the detection of IBV (kappa: 0.355, p = 0.27) showed good agreement without statistical significance.

CONCLUSIONS

Overall, the developed one-step multiplex ddPCR assay is cost-effective, highly sensitive and specific, and can simultaneously detect three common respiratory viruses in the complex matrix of wastewater samples even at low concentrations. Due to its high sensitivity and resistance to PCR inhibitors, the developed assay could be further used as an early warning system for wastewater monitoring.

Wastewater tiling amplicon sequencing in sentinel sites reveals longitudinal dynamics of SARS-CoV-2 variants prevalence

The ongoing evolution of SARS-CoV-2 is a significant concern, especially with the decrease in clinical sequencing efforts, which impedes the ability of public health sectors to prepare for the emergence of new variants and potential COVID-19 outbreaks. Wastewater-based epidemiology (WBE) has been proposed as a surveillance program to detect and monitor the SARS-CoV-2 variants being transmitted in communities. However, research is limited in evaluating the effectiveness of wastewater collection at sentinel sites for monitoring disease prevalence and variant dynamics, especially in terms of inferring the epidemic patterns on a broader scale, such as at the state/province level. This study utilized a multiplexed tiling amplicon-based sequencing (ATOPlex) to track the longitudinal dynamics of variant of concern (VOC) in wastewater collected from municipalities in Queensland, Australia, spanning from 2020 to 2022. We demonstrated that wastewater epidemiology measured by ATOPlex exhibited a strong and consistent correlation with the number of daily confirmed cases. The VOC dynamics observed in wastewater closely aligned with the dynamic profile reported by clinical sequencing. Wastewater sequencing has the potential to provide early warning information for emerging variants. These findings suggest that WBE at sentinel sites, coupled with sensitive sequencing methods, provides a reliable and long-term disease surveillance strategy.

Systematic SARS-CoV-2 S-gene sequencing in wastewater samples enables early lineage detection and uncovers rare mutations in Portugal

As the COVID-19 pandemic reached its peak, many countries implemented genomic surveillance systems to track the evolution and transmission of SARS-CoV-2. Transition from the pandemic to the endemic phase prioritized alternative testing strategies to maintain effective epidemic surveillance at the population level, with less intensive sequencing efforts. One such promising approach was Wastewater-Based Surveillance (WBS), which offers non-invasive, cost-effective means for analysing virus trends at the sewershed level. From 2020 onwards, wastewater has been recognized as an instrumental source of information for public health, with national and international authorities exploring options to implement national wastewater surveillance systems and increasingly relying on WBS as early warning of potential pathogen outbreaks. In Portugal, several pioneer projects joined the academia, water utilities and Public Administration around WBS. To validate WBS as an effective genomic surveillance strategy, it is crucial to collect long term performance data. In this work, we present one year of systematic SARS-CoV-2 wastewater surveillance in Portugal, representing 35 % of the mainland population. We employed two complementary methods for lineage determination - allelic discrimination by RT-PCR and S-gene sequencing. This combination allowed us to monitor variant evolution in near-real-time and identify low-frequency mutations. Over the course of this year-long study, spanning from May 2022 to April 2023, we successfully tracked the dominant Omicron sub-lineages, their progression and evolution, which aligned with concurrent clinical surveillance data. Our results underscore the effectiveness of WBS as a tracking system for virus variants, with the ability to unveil mutations undetected via massive sequencing of clinical samples from Portugal, demonstrating the ability of WBS to uncover new mutations and detect rare genetic variants. Our findings emphasize that knowledge of the genetic diversity of SARS-CoV-2 at the population level can be extended far beyond via the combination of routine clinical genomic surveillance with wastewater sequencing and genotyping.

Advances in wastewater analysis revealing the co-circulating viral trends of noroviruses and Omicron subvariants

Co-presence of enveloped and non-enveloped viruses is common both in community circulation and in wastewater. Community surveillance of infections requires robust methods enabling simultaneous quantification of multiple viruses in wastewater. Using enveloped SARS-CoV-2 Omicron subvariants and non-enveloped norovirus (NoV) as examples, this study reports a robust method that integrates electronegative membrane (EM) concentration, viral inactivation, and RNA preservation (VIP) with efficient capture and enrichment of the viral RNA on magnetic (Mag) beads, and direct detection of RNA on the beads. This method provided improved viral recoveries of 80 ± 4 % for SARS-CoV-2 and 72 ± 5 % for Murine NoV. Duplex reverse transcription quantitative polymerase chain reaction (RT-qPCR) assays with newly designed degenerate primer-probe sets offered high PCR efficiencies (90-91 %) for NoV (GI and GII) targets and were able to detect as few as 15 copies of the viral RNA per PCR reaction. This technique, combined with duplex detection of NoV and multiplex detection of Omicron, successfully quantified NoV (GI and GII) and Omicron variants in the same sets of 94 influent wastewater samples collected from two large wastewater systems between July 2022 and June 2023. The wastewater viral RNA results showed temporal changes of both NoV and Omicron variants in the same wastewater systems and revealed an inverse relationship of their emergence. This study demonstrated the importance of a robust analytical platform for simultaneous surveillance of enveloped and non-enveloped viruses in wastewater. The ability to sensitively determine multiple viral pathogens in wastewater will advance applications of wastewater surveillance as a complementary public health tool.

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.

Influences of 23 different equations used to calculate gene copies of SARS-CoV-2 during wastewater-based epidemiology

Following the emergence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in late 2019, the use of wastewater-based surveillance (WBS) has increased dramatically along with associated infrastructure globally. However, due to the global nature of its application, and various workflow adaptations (e.g., sample collection, water concentration, RNA extraction kits), numerous methods for back-calculation of gene copies per volume (gc/L) of sewage have also emerged. Many studies have considered the comparability of processing methods (e.g., water concentration, RNA extraction); however, for equations used to calculate gene copies in a wastewater sample and subsequent influences on monitoring viral trends in a community and its association with epidemiological data, less is known. Due to limited information on how many formulas exist for the calculation of SARS-CoV-2 gene copies in wastewater, we initially attempted to quantify how many equations existed in the referred literature. We identified 23 unique equations, which were subsequently applied to an existing wastewater dataset. We observed a range of gene copies based on use of different equations, along with variability of AUC curve values, and results from correlation and regression analyses. Though a number of individual laboratories appear to have independently converged on a similar formula for back-calculation of viral load in wastewater, and share similar relationships with epidemiological data, differential influences of various equations were observed for variation in PCR volumes, RNA extraction volumes, or PCR assay parameters. Such observations highlight challenges when performing comparisons among WBS studies when numerous methodologies and back-calculation methods exist. To facilitate reproducibility among studies, the different gc/L equations were packaged as an R Shiny app, which provides end users the ability to investigate variability within their datasets and support comparisons among studies.

Wastewater-based epidemiology for COVID-19 using dynamic artificial neural networks

Global efforts in vaccination have led to a decrease in COVID-19 mortality but a high circulation of SARS-CoV-2 is still observed in several countries, resulting in some cases of severe lockdowns. In this sense, wastewater-based epidemiology remains a powerful tool for supporting regional health administrations in assessing risk levels and acting accordingly. In this work, a dynamic artificial neural network (DANN) has been developed for predicting the number of COVID-19 hospitalized patients in hospitals in Valladolid (Spain). This model takes as inputs a wastewater epidemiology indicator for COVID-19 (concentration of RNA from SARS-CoV-2 N1 gene reported from Valladolid Wastewater Treatment Plant), vaccination coverage, and past data of hospitalizations. The model considered both the instantaneous values of these variables and their historical evolution. Two study periods were selected (from May 2021 until September 2022 and from September 2022 to July 2023). During the first period, accurate predictions of hospitalizations (with an overall range between 6 and 171) were favored by the correlation of this indicator with N1 concentrations in wastewater (r = 0.43, p < 0.05), showing accurate forecasting for 1 day ahead and 5 days ahead. The second period's retraining strategy maintained the overall accuracy of the model despite lower hospitalizations. Furthermore, risk levels were assigned to each 1 day ahead prediction during the first and second periods, showing agreement with the level measured and reported by regional health authorities in 95 % and 93 % of cases, respectively. These results evidenced the potential of this novel DANN model for predicting COVID-19 hospitalizations based on SARS-CoV-2 wastewater concentrations at a regional scale. The model architecture herein developed can support regional health authorities in COVID-19 risk management based on wastewater-based epidemiology.

A Bayesian framework for modeling COVID-19 case numbers through longitudinal monitoring of SARS-CoV-2 RNA in wastewater

Wastewater-based surveillance has become an important tool for research groups and public health agencies investigating and monitoring the COVID-19 pandemic and other public health emergencies including other pathogens and drug abuse. While there is an emerging body of evidence exploring the possibility of predicting COVID-19 infections from wastewater signals, there remain significant challenges for statistical modeling. Longitudinal observations of viral copies in municipal wastewater can be influenced by noisy datasets and missing values with irregular and sparse samplings. We propose an integrative Bayesian framework to predict daily positive cases from weekly wastewater observations with missing values via functional data analysis techniques. In a unified procedure, the proposed analysis models severe acute respiratory syndrome coronavirus-2 RNA wastewater signals as a realization of a smooth process with error and combines the smooth process with COVID-19 cases to evaluate the prediction of positive cases. We demonstrate that the proposed framework can achieve these objectives with high predictive accuracies through simulated and observed real data.

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.

Spike-Seq: An amplicon-based high-throughput sequencing approach for the sensitive detection and characterization of SARS-CoV-2 genetic variations in environmental samples

Ever since the outbreak of COVID-19 disease in Wuhan, China, different variants of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) have been identified. Wastewater-based epidemiology (WBE), an approach that has been successfully applied in numerous case studies worldwide, offers a cost-effective and rapid way for monitoring trends of SARS-Cov-2 in the community level without selection bias. Despite being a gold-standard procedure, WBE is a challenging approach due to the sample instability and the moderate efficiency of SARS-CoV-2 concentration in wastewater. In the present study, we introduce Spike-Seq, a custom amplicon-based approach for the S gene sequencing of SARS-CoV-2 in wastewater samples, which enables not only the accurate identification of the existing Spike-related genetic markers, but also the estimation of their frequency in the investigated samples. The implementation of Spike-Seq involves the combination of nested PCR-based assays that efficiently amplify the entire nucleotide sequence of the S gene and next-generation sequencing, which enables the variant detection and the estimation of their frequency. In the framework of the current work, Spike-Seq was performed to investigate the mutational profile of SARS-CoV-2 in samples from the Wastewater Treatment Plant (WWTP) of Athens, Greece, which originated from multiple timepoints, ranging from March 2021 until July 2022. Our findings demonstrate that Spike-Seq efficiently detected major genetic markers of B.1.1.7 (Alpha), B.1.617.2 (Delta) as well as B.1.1.529 (Omicron) variants in wastewater samples and provided their frequency levels, showing similar variant distributions with the published clinical data from the National Public Health organization. The presented approach can prove to be a useful tool for the detection of SARS-CoV-2 in challenging wastewater samples and the identification of the existing genetic variants of S gene.

Real-Time On-Site Monitoring of Viruses in Wastewater Using Nanotrap® Particles and RICCA Technologies

Wastewater-based epidemiology (WBE) is an effective and efficient tool for the early detection of infectious disease outbreaks in a community. However, currently available methods are laborious, costly, and time-consuming due to the low concentration of viruses and the presence of matrix chemicals in wastewater that may interfere with molecular analyses. In the present study, we designed a highly sensitive "Quick Poop (wastewater with fecal waste) Sensor" (termed, QPsor) using a joint approach of Nanotrap microbiome particles and RICCA (RNA Isothermal Co-Assisted and Coupled Amplification). Using QPsor, the WBE study showed a strong correlation with standard PEG concentrations and the qPCR technique. Using a closed format for a paper-based lateral flow assay, we were able to demonstrate the potential of our assay as a real-time, point-of-care test by detecting the heat-inactivated SARS-CoV-2 virus in wastewater at concentrations of 100 copies/mL and within one hour. As a proof-of-concept demonstration, we analyzed the presence of viral RNA of the SARS-CoV-2 virus and PMMoV in raw wastewater samples from wastewater treatment plants on-site and within 60 min. The results show that the QPsor method can be an effective tool for disease outbreak detection by combining an AI-enabled case detection model with real-time on-site viral RNA extraction and amplification, especially in the absence of intensive clinical laboratory facilities. The lab-free, lab-quality test capabilities of QPsor for viral prevalence and transmission in the community can contribute to the efficient management of pandemic situations.

Inferring hospital admissions from SARS-CoV-2 virus loads in wastewater in The Netherlands, August 2020 - February 2022

Wastewater-based surveillance enables tracking of SARS-CoV-2 circulation at a local scale in near-real time. Here we investigate the relation between virus loads and the number of hospital admissions in the Netherlands. Inferred virus loads from August 2020 until February 2022 in each of the 344 Dutch municipalities are analysed in a Bayesian multilevel Poisson regression to relate virus loads to daily age-stratified (in groups of 20 years) hospital admissions. Covariates include municipal vaccination coverages stratified by age and dose (first, second, and booster) and prevalence of the circulating coronavirus variants (wildtype, Alpha, Delta, and Omicron (BA.1 and BA.2)). Our model captures the relation between hospital admissions and virus loads well. Estimated hospitalisation rates per 1,000,000 persons per day at a virus load of 1013 particles range from 0.18 (95 % Prediction Interval (PI): 0.046-0.48) in children (0-19 years) to 20.1 (95 % PI: 9.46-36.8) in the oldest age group (80 years and older) in an unvaccinated population with only wildtype SARS-CoV-2 circulation. The analyses indicate a nearly twofold (1.92 (95 % PI: 1.78-2.05)) decrease in the expected number of hospitalisations at a given virus load between the Alpha and the Omicron variant. Our analyses show that virus load estimates in wastewater are closely related to the expected number of hospitalisations and provide an attractive tool to detect increased SARS-CoV-2 circulation at a local scale, even when there are few hospital admissions. Our analyses enable integration of data at the municipality level into meaningful conversion rates to translate virus loads at a local level into expected numbers of hospital admissions, which would allow for a better interpretation of virus loads detected in wastewater.

Beyond linear regression: Modeling COVID-19 clinical cases with wastewater surveillance of SARS-CoV-2 for the city of Athens and Ohio University campus

Wastewater-based surveillance has emerged as a detection tool for population-wide infectious diseases, including coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Infected individuals shed the virus, which can be detected in wastewater using molecular techniques such as reverse transcription-digital polymerase chain reaction (RT-dPCR). This study examined the association between the number of clinical cases and the concentration of SARS-CoV-2 in wastewater beyond linear regression and for various normalizations of viral loads. Viral loads were measured in a total of 446 wastewater samples during the period from August 2021 to April 2022. These samples were collected from nine different locations, with 220 samples taken from four specific sites within the city of Athens and 226 samples from five sites within Ohio University. The correlation between COVID-19 cases and wastewater viral concentrations, which was estimated using the Pearson correlation coefficient, was statistically significant and ranged from 0.6 to 0.9. In addition, time-lagged cross correlation was applied to identify the lag time between clinical and wastewater data, estimated 4 to 7 days. While we also explored the effect on the correlation coefficients of various normalizations of viral loads accounting for procedural loss or amount of fecal material and of estimated lag times, these alternative specifications did not change our substantive conclusions. Additionally, several linear and non-linear regression models were applied to predict the COVID-19 cases given wastewater data as input. The non-linear approach was found to yield the highest R-squared and Pearson correlation and lowest Mean Absolute Error values between the predicted and actual number of COVID-19 cases for both aggregated OHIO Campus and city data. Our results provide support for previous studies on correlation and time lag and new evidence that non-linear models, approximated with artificial neural networks, should be implemented for WBS of contagious diseases.

Predicting COVID-19 hospitalizations: The importance of healthcare hotlines, test positivity rates and vaccination coverage

In this study, we developed a negative binomial regression model for one-week ahead spatio-temporal predictions of the number of COVID-19 hospitalizations in Uppsala County, Sweden. Our model utilized weekly aggregated data on testing, vaccination, and calls to the national healthcare hotline. Variable importance analysis revealed that calls to the national healthcare hotline were the most important contributor to prediction performance when predicting COVID-19 hospitalizations. Our results support the importance of early testing, systematic registration of test results, and the value of healthcare hotline data in predicting hospitalizations. The proposed models may be applied to studies modeling hospitalizations of other viral respiratory infections in space and time assuming count data are overdispersed. Our suggested variable importance analysis enables the calculation of the effects on the predictive performance of each covariate. This can inform decisions about which types of data should be prioritized, thereby facilitating the allocation of healthcare resources.

An interpretative review of the wastewater-based surveillance of the SARS-CoV-2: where do we stand on its presence and concern?

Wastewater-based epidemiology (WBE) has been used for monitoring infectious diseases like polio, hepatitis, etc. since the 1940s. It is also being used for tracking the SARS-CoV-2 at the population level. This article aims to compile and assess the information for the qualitative and quantitative detection of the SARS-CoV-2 in wastewater. Based on the globally published studies, we highlight the importance of monitoring SARS-CoV-2 presence/detection in the wastewater and concurrently emphasize the development of early surveillance techniques. SARS-CoV-2 RNA sheds in the human feces, saliva, sputum and mucus that ultimately reaches to the wastewater and brings viral RNA into it. For the detection of the virus in the wastewater, different detection techniques have been optimized and are in use. These are based on serological, biosensor, targeted PCR, and next generation sequencing for whole genome sequencing or targeted amplicon sequencing. The presence of the SARS-CoV-2 RNA in wastewater could be used as a potential tool for early detection and devising the strategies for eradication of the virus before it is spread in the community. Additionally, with the right and timely understanding of viral behavior in the environment, an accurate and instructive model that leverages WBE-derived data may be created. This might help with the creation of technological tools and doable plans of action to lessen the negative effects of current viral epidemics or future potential outbreaks on public health and the economy. Further work toward whether presence of viral load correlates with its ability to induce infection, still needs evidence. The current increasing incidences of JN.1 variant is a case in point for continued early detection and surveillance, including wastewater.

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

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

Underestimation of SARS-CoV-2 in wastewater due to single or double mutations in the N1 qPCR probe binding region

Wastewater surveillance using RT-qPCR has now been widely adopted to track circulating levels of SARS-CoV-2 virus in many sewersheds. The CDC qPCR assays targeting two regions (N1 and N2) within the N gene are commonly used, but a discrepancy between the two biomarkers has been noticed by independent studies using these methods since late 2021. The reason is presumed to be due to mutations in regions targeted by the N1 qPCR probe. In this study, we systematically investigated and unequivocally confirmed that the underlying reason for this discrepancy was mutations in the N1 probe target, and that a single mutation could cause a significant drop in signal. We first confirmed the proportion of related mutations in wastewater samples (Jan 2021-Dec 2022) using nested PCR and LC-MS. Based on relative proportions of N1 alleles, we separated the wastewater data into four time periods corresponding to different variant waves: Period I (Alpha and Delta waves with 0 mutation), Period II (BA.1/BA.2 waves with a single mutation found in all Omicron strains), Period III (BA.5.2* wave with two mutations), and Period IV (BQ.1* wave with two mutations). Significantly lower N1 copies relative to N2 copies in samples from Periods II-IV compared to those from Period I was observed in wastewater. To further pinpoint the extent to which each mutation impacted N1 quantification, we compared the qPCR response among different synthetic oligomers with corresponding mutations. This study highlighted the impact of even just one or two mutations on qPCR-based wastewater surveillance of SARS-CoV-2.

Estimating COVID-19 cases on a university campus based on Wastewater Surveillance using machine learning regression models

Wastewater Surveillance (WS) is a crucial tool in the management of COVID-19 pandemic. The surveillance is based on enumerating SARS-CoV-2 RNA concentrations in the community's sewage. In this study, we used WS data to develop a regression model for estimating the number of active COVID-19 cases on a university campus. Eight univariate and multivariate regression model types i.e. Linear Regression (LM), Polynomial Regression (PR), Generalised Additive Model (GAM), Locally Estimated Scatterplot Smoothing Regression (LOESS), K Nearest Neighbours Regression (KNN), Support Vector Regression (SVR), Artificial Neural Networks (ANN) and Random Forest (RF) were developed and compared. We found that the multivariate RF regression model, was the most appropriate for predicting the prevalence of COVID-19 infections at both a campus level and hostel-level. We also found that smoothing the normalised SARS-CoV-2 data and employing multivariate modelling, using student population as a second independent variable, significantly improved the performance of the models. The final RF campus level model showed good accuracy when tested using previously unseen data; correlation coefficient of 0.97 and a mean absolute error (MAE) of 20 %. In summary, our non-intrusive approach has the ability to complement projections based on clinical tests, facilitating timely follow-up and response.

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

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

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.

Bayesian monitoring of COVID-19 in Sweden

In an effort to provide regional decision support for the public healthcare, we design a data-driven compartment-based model of COVID-19 in Sweden. From national hospital statistics we derive parameter priors, and we develop linear filtering techniques to drive the simulations given data in the form of daily healthcare demands. We additionally propose a posterior marginal estimator which provides for an improved temporal resolution of the reproduction number estimate as well as supports robustness checks via a parametric bootstrap procedure. From our computational approach we obtain a Bayesian model of predictive value which provides important insight into the progression of the disease, including estimates of the effective reproduction number, the infection fatality rate, and the regional-level immunity. We successfully validate our posterior model against several different sources, including outputs from extensive screening programs. Since our required data in comparison is easy and non-sensitive to collect, we argue that our approach is particularly promising as a tool to support monitoring and decisions within public health. Significance: Using public data from Swedish patient registries we develop a national-scale computational model of COVID-19. The parametrized model produces valuable weekly predictions of healthcare demands at the regional level and validates well against several different sources. We also obtain critical epidemiological insights into the disease progression, including, e.g., reproduction number, immunity and disease fatality estimates. The success of the model hinges on our novel use of filtering techniques which allows us to design an accurate data-driven procedure using data exclusively from healthcare demands, i.e., our approach does not rely on public testing and is therefore very cost-effective.

Wastewater surveillance of the most common circulating respiratory viruses in Athens: The impact of COVID-19 on their seasonality

Due to governments' actions to contain the spread of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), the activity of common circulating respiratory viruses was significantly disrupted after the COVID-19 pandemic and thorough surveillance of respiratory pathogens was considered essential worldwide. Wastewater-based epidemiology has proven to be a valuable tool, that provides complementary information on disease outbreaks and is increasingly used to study the infection dynamics of other viruses, apart from SARS-CoV-2. The aims of the present study were the detection of four commonly circulating respiratory viruses: SARS-CoV-2, influenza A, B and Human Respiratory Syncytial Virus (RSV), the evaluation of the COVID-19 pandemic impact on their seasonality and the determination of the possible common trends in the viral load of these viruses in the wastewater of the Attica region. A standardized and validated concentration and extraction protocol was used, generic for all four viruses, followed by Reverse Transcription quantitative Polymerase Chain Reaction (RT-qPCR) assays. The study proved that there was a prolonged period when all four viruses circulated in the population and an early outbreak of seasonal influenza and RSV in 2022-2023, compared to data from the pre-COVID-19 period. SARS-CoV-2, influenza A and RSV concentrations showed peak levels during December, followed by a slight decline in influenza A concentrations, followed by steady increase of influenza B concentrations in January 2023. SARS-CoV-2 was the dominant virus throughout the whole study period. This is the first study in Greece that investigated the most common circulating viruses simultaneously and in one of the largest timelines, providing crucial information about their infection dynamics during a period when an outbreak of respiratory diseases was declared by the National Public Health Organization. Presented results highlight the establishment of environmental surveillance as a non-invasive and complementary virus outbreak monitoring tool and the importance of influenza A, B and RSV integration into a wastewater-based surveillance system to help in disease management.

Wastewater-based surveillance can be used to model COVID-19-associated workforce absenteeism

Wastewater-based surveillance (WBS) of infectious diseases is a powerful tool for understanding community COVID-19 disease burden and informing public health policy. The potential of WBS for understanding COVID-19's impact in non-healthcare settings has not been explored to the same degree. Here we examined how SARS-CoV-2 measured from municipal wastewater treatment plants (WWTPs) correlates with workforce absenteeism. SARS-CoV-2 RNA N1 and N2 were quantified three times per week by RT-qPCR in samples collected at three WWTPs servicing Calgary and surrounding areas, Canada (1.4 million residents) between June 2020 and March 2022. Wastewater trends were compared to workforce absenteeism using data from the largest employer in the city (>15,000 staff). Absences were classified as being COVID-19-related, COVID-19-confirmed, and unrelated to COVID-19. Poisson regression was performed to generate a prediction model for COVID-19 absenteeism based on wastewater data. SARS-CoV-2 RNA was detected in 95.5 % (85/89) of weeks assessed. During this period 6592 COVID-19-related absences (1896 confirmed) and 4524 unrelated absences COVID-19 cases were recorded. A generalized linear regression using a Poisson distribution was performed to predict COVID-19-confirmed absences out of the total number of absent employees using wastewater data as a leading indicator (P < 0.0001). The Poisson regression with wastewater as a one-week leading signal has an Akaike information criterion (AIC) of 858, compared to a null model (excluding wastewater predictor) with an AIC of 1895. The likelihood-ratio test comparing the model with wastewater signal with the null model shows statistical significance (P < 0.0001). We also assessed the variation of predictions when the regression model was applied to new data, with the predicted values and corresponding confidence intervals closely tracking actual absenteeism data. Wastewater-based surveillance has the potential to be used by employers to anticipate workforce requirements and optimize human resource allocation in response to trackable respiratory illnesses like COVID-19.

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

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

Trends in SARS-CoV-2 clinically confirmed cases and viral load in wastewater: A critical alignment for Padua city (NE Italy)

Since the beginning of the COVID-19 pandemic, wastewater-based epidemiology (WBE) has been depicted as a promising environmental surveillance tool and early warning system. Predictive models for the estimate of COVID-19 cases from wastewater viral loads also earned lot of interest and are currently under development. Hereby a pilot study that compares WBE surveillance data with confirmed cases, total hospitalizations, doses of vaccine administered and predominance of coronavirus variants. Composite 24hrs wastewater samples were collected weekly between September 2021 and July 2022 from Padua wastewater treatment plant. Samples were processed following a previously published method. One-step RT-qPCR was performed for quantification, adapting an Orf1b-nsp14 gene assay. Variant replacement was derived from the monthly bulletins of the Italian National Health Institute. Aggregate data on vaccine doses administered and on COVID-19 prevalence and hospitalizations were retrieved from official reports. Eighty-two samples were processed. Viral loads highlighted 3 major peaks in January, April and July 2022. Quantitation of SARS-CoV-2 in wastewater and clinical surveillance resulted temporally juxtaposable. However, variation of the two curves is not proportional. SARS-CoV-2 showed its highest peak in April, whereas maximum COVID-19 prevalence was achieved in January. Total hospitalizations followed the prevalence trend. Omicron BA.1 started to replace the Delta variant in December 2021. Subsequently, the shift towards Omicron BA.2 occurred between February and April 2022. Finally, BA.4/5 attested around June, somehow preceding the summer peak. Emergence of Omicron BA.1 over Delta could be a possible driver of the increase in both clinical cases and wastewater viral load in January 2022. In late March 2022, Omicron BA.2 replaced BA.1: this reflected in a steep increase of wastewater viral load, but not of COVID-19 confirmed cases. When a dramatic drop in the testing capacity of clinical surveillance occurred, WBE was possibly capable of detecting a substantial increase in viral circulation.

Monkeypox viral nucleic acids detected using both DNA and RNA extraction workflows

Molecular methods have been used to detect human pathogens in wastewater with sampling typically performed at wastewater treatment plants (WWTP) and upstream locations within the sewer system. A wastewater-based surveillance (WBS) program was established at the University of Miami (UM) in 2020, which included measurements of SARS-CoV-2 levels in wastewater from its hospital and within the regional WWTP. In addition to the development of a SARS-CoV-2 quantitative PCR (qPCR) assay, qPCR assays to detect other human pathogens of interest were also developed at UM. Here we report on the use of a modified set of reagents published by the CDC to detect nucleic acids of Monkeypox virus (MPXV) which emerged during May of 2022 to become a concern worldwide. Samples collected from the University hospital and from the regional WWTP were processed through DNA and RNA workflows and analyzed by qPCR to detect a segment of the MPXV CrmB gene. Results show positive detections of MPXV nucleic acids in the hospital and wastewater treatment plant wastewater which coincided with clinical cases in the community and mirrored the overall trend of nationwide MPXV cases reported to the CDC. We recommend the expansion of current WBS programs' methods to detect a broader range of pathogens of concern in wastewater and present evidence that viral RNA in human cells infected by a DNA virus can be detected in wastewater.

Correlative analysis of wastewater trends with clinical cases and hospitalizations through five dominant variant waves of COVID-19

Wastewater-based epidemiology (WBE) has been utilized to track community infections of Coronavirus Disease 2019 (COVID-19) by detecting RNA of the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), within samples collected from wastewater. The correlations between community infections and wastewater measurements of the RNA can potentially change as SARS-CoV-2 evolves into new variations by mutating. This study analyzed SARS-CoV-2 RNA, and indicators of human waste in wastewater from two sewersheds of different scales (University of Miami (UM) campus and Miami-Dade County Central District wastewater treatment plant (CDWWTP)) during five internally defined COVID-19 variant dominant periods (Initial, Pre-Delta, Delta, Omicron and Post-Omicron wave). SARS-CoV-2 RNA quantities were compared against COVID-19 clinical cases and hospitalizations to evaluate correlations with wastewater SARS-CoV-2 RNA. Although correlations between documented clinical cases and hospitalizations were high, prevalence for a given wastewater SARS-CoV-2 level varied depending upon the variant analyzed. The correlative relationship was significantly steeper (more cases per level found in wastewater) for the Omicron-dominated period. For hospitalization, the relationships were steepest for the Initial wave, followed by the Delta wave with flatter slopes during all other waves. Overall results were interpreted in the context of SARS-CoV-2 virulence and vaccination rates among the community.

Multiplex One-Step RT-qPCR Assays for Simultaneous Detection of SARS-CoV-2 and Other Enteric Viruses of Dogs and Cats

The severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) was transmitted from humans to dogs and cats (reverse zoonosis) during the COVID-19 pandemic. SARS-CoV-2 has been detected in fecal samples of infected dogs and cats, indicating potential fecal-oral transmission, environmental contamination, and zoonotic transmission (i.e., spillback). Additionally, gastrointestinal viral infections are prevalent in dogs and cats. In this study, we developed and validated a panel of multiplex one-step reverse transcription-quantitative polymerase chain reaction (RT-qPCR) assays for the simultaneous detection of SARS-CoV-2 and common canine enteric viruses: Canine Enteric Assay_1 (CEA_1) for the detection of canine adenovirus-1, canine enteric coronavirus, canine distemper virus, and canine parvovirus, and CEA_2 for the detection of rotavirus A (RVA), and SARS-CoV-2); or common feline enteric viruses (Feline Enteric Assay_1 (FEA_1) for the detection of feline enteric coronavirus, feline panleukopenia virus, RVA, and SARS-CoV-2). All assays demonstrated high analytical sensitivity, detecting as few as 5-35 genome copies/µL in multiplex format. The repeatability and reproducibility of the multiplex assays were excellent, with coefficient of variation <4%. Among the 58 clinical samples tested, 34.5% were positive for at least one of these viruses, and SARS-CoV-2 was detected in two samples collected from one dog and one cat, respectively. In conclusion, these newly developed one-step multiplex RT-qPCR assays allow for rapid diagnosis of enteric viral infections, including SARS-CoV-2, in dogs and cats.

Structured Ethical Review for Wastewater-Based Testing in Support of Public Health

Wastewater-based testing (WBT) for SARS-CoV-2 has rapidly expanded over the past three years due to its ability to provide a comprehensive measurement of disease prevalence independent of clinical testing. The development and simultaneous application of WBT measured biomarkers for research activities and for the pursuit of public health goals, both areas with well-established ethical frameworks. Currently, WBT practitioners do not employ a standardized ethical review process, introducing the potential for adverse outcomes for WBT professionals and community members. To address this deficiency, an interdisciplinary workshop developed a framework for a structured ethical review of WBT. The workshop employed a consensus approach to create this framework as a set of 11 questions derived from primarily public health guidance. This study retrospectively applied these questions to SARS-CoV-2 monitoring programs covering the emergent phase of the pandemic (3/2020-2/2022 (n = 53)). Of note, 43% of answers highlight a lack of reported information to assess. Therefore, a systematic framework would at a minimum structure the communication of ethical considerations for applications of WBT. Consistent application of an ethical review will also assist in developing a practice of updating approaches and techniques to reflect the concerns held by both those practicing and those being monitored by WBT supported programs.

An exploration of the relationship between wastewater viral signals and COVID-19 hospitalizations in Ottawa, Canada

Monitoring of viral signal in wastewater is considered a useful tool for monitoring the burden of COVID-19, especially during times of limited availability in testing. Studies have shown that COVID-19 hospitalizations are highly correlated with wastewater viral signals and the increases in wastewater viral signals can provide an early warning for increasing hospital admissions. The association is likely nonlinear and time-varying. This project employs a distributed lag nonlinear model (DLNM) (Gasparrini et al., 2010) to study the nonlinear exposure-response delayed association of the COVID-19 hospitalizations and SARS-CoV-2 wastewater viral signals using relevant data from Ottawa, Canada. We consider up to a 15-day time lag from the average of SARS-CoV N1 and N2 gene concentrations to COVID-19 hospitalizations. The expected reduction in hospitalization is adjusted for vaccination efforts. A correlation analysis of the data verifies that COVID-19 hospitalizations are highly correlated with wastewater viral signals with a time-varying relationship. Our DLNM based analysis yields a reasonable estimate of COVID-19 hospitalizations and enhances our understanding of the association of COVID-19 hospitalizations with wastewater viral signals.

Wastewater Monitoring for Infectious Disease: Intentional Relationships between Academia, the Private Sector, and Local Health Departments for Public Health Preparedness

The public health emergency caused by the COVID-19 pandemic stimulated stakeholders from diverse disciplines and institutions to establish new collaborations to produce informed public health responses to the disease. Wastewater-based epidemiology for COVID-19 grew quickly during the pandemic and required the rapid implementation of such collaborations. The objective of this article is to describe the challenges and results of new relationships developed in Detroit, MI, USA among a medical school and an engineering college at an academic institution (Wayne State University), the local health department (Detroit Health Department), and an environmental services company (LimnoTech) to utilize markers of the COVID-19 virus, SARS-CoV-2, in wastewater for the goal of managing COVID-19 outbreaks. Our collaborative team resolved questions related to sewershed selection, communication of results, and public health responses and addressed technical challenges that included ground-truthing the sewer maps, overcoming supply chain issues, improving the speed and sensitivity of measurements, and training new personnel to deal with a new disease under pandemic conditions. Recognition of our complementary roles and clear communication among the partners enabled city-wide wastewater data to inform public health responses within a few months of the availability of funding in 2020, and to make improvements in sensitivity and understanding to be made as the pandemic progressed and evolved. As a result, the outbreaks of COVID-19 in Detroit in fall and winter 2021-2022 (corresponding to Delta and Omicron variant outbreaks) were tracked in 20 sewersheds. Data comparing community- and hospital-associated sewersheds indicate a one- to two-week advance warning in the community of subsequent peaks in viral markers in hospital sewersheds. The new institutional relationships impelled by the pandemic provide a good basis for continuing collaborations to utilize wastewater-based human and pathogen data for improving the public health in the future.

Long-term study on wastewater SARS-CoV-2 surveillance across United Arab Emirates

Wastewater-based epidemiology (WBE) demonstrates an efficient tool to monitor and predict SARS-CoV-2 community distribution. Many countries across the world have adopted the technique, however, most of these studies were conducted for a short duration with a limited sampling size. In this study, long-term reliability and quantification of wastewater SARS-CoV-2 surveillance is reported via analyzing 16,858 samples collected from 453 different locations across the United Arab Emirates (UAE) from May 2020 to June 2022. The collected composite samples were first incubated at 60 °C followed by filtration, concentration, and then RNA extraction using commercially available kits. The extracted RNA was then analyzed by one-step RT-qPCR and RT-ddPCR, and the data was compared to the reported clinical cases. The average positivity rate in the wastewater samples was found to be 60.61 % (8.41-96.77 %), however, the positivity rate obtained from the RT-ddPCR was significantly higher than the RT-qPCR suggesting higher sensitivity of RT-ddPCR. Time-lagged correlation analysis indicated an increase in positive cases in the wastewater samples when the clinical positive cases declined suggesting that wastewater data are highly affected by the unreported asymptomatic, pre-symptomatic and recovering individuals. The weekly SARS-CoV-2 viral count in the wastewater samples are positively correlated with the diagnosed new clinical cases throughout the studied period and the studied locations. Viral count in wastewater peaked approximately one to two weeks prior to the peaks appearing in active clinical cases indicating that wastewater viral concentrations are effective in predicting clinical cases. Overall, this study further confirms the long-term sensitivity and robust approach of WBE to detect trends in SARS-CoV-2 spread and helps contribute to pandemic management.

Wastewater-based epidemiology predicts COVID-19-induced weekly new hospital admissions in over 150 USA counties

Although the coronavirus disease (COVID-19) emergency status is easing, the COVID-19 pandemic continues to affect healthcare systems globally. It is crucial to have a reliable and population-wide prediction tool for estimating COVID-19-induced hospital admissions. We evaluated the feasibility of using wastewater-based epidemiology (WBE) to predict COVID-19-induced weekly new hospitalizations in 159 counties across 45 states in the United States of America (USA), covering a population of nearly 100 million. Using county-level weekly wastewater surveillance data (over 20 months), WBE-based models were established through the random forest algorithm. WBE-based models accurately predicted the county-level weekly new admissions, allowing a preparation window of 1-4 weeks. In real applications, periodically updated WBE-based models showed good accuracy and transferability, with mean absolute error within 4-6 patients/100k population for upcoming weekly new hospitalization numbers. Our study demonstrated the potential of using WBE as an effective method to provide early warnings for healthcare systems.

An alternative method for monitoring and interpreting influenza A in communities using wastewater surveillance

Seasonal influenza is an annual public health challenge that strains healthcare systems, yet population-level prevalence remains under-reported using standard clinical surveillance methods. Wastewater surveillance (WWS) of influenza A can allow for reliable flu surveillance within a community by leveraging existing severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) WWS networks regardless of the sample type (primary sludge vs. primary influent) using an RT-qPCR-based viral RNA detection method for both targets. Additionally, current influenza A outbreaks disproportionately affect the pediatric population. In this study, we show the utility of interpreting influenza A WWS data with elementary student absenteeism due to illness to selectively interpret disease spread in the pediatric population. Our results show that the highest statistically significant correlation (Rs = 0.96, p = 0.011) occurred between influenza A WWS data and elementary school absences due to illness. This correlation coefficient is notably higher than the correlations observed between influenza A WWS data and influenza A clinical case data (Rs = 0.79, p = 0.036). This method can be combined with a suite of pathogen data from wastewater to provide a robust system for determining the causative agents of diseases that are strongly symptomatic in children to infer pediatric outbreaks within communities.

COVID-19 Case and Mortality Surveillance using Daily SARS-CoV-2 in Wastewater Samples adjusting for Meteorological Conditions and Sample pH

Background

Wastewater monitoring is increasingly used for community surveillance of infectious diseases, especially after the COVID-19 pandemic as the genomic footprints of pathogens shed by infected individuals can be traced in the environment. However, detection and concentration of pathogens in the environmental samples and their efficacy in predicting infectious diseases can be influenced by meteorological conditions and quality of samples.

Objectives

This research examines whether meteorological conditions and sample pH affect SARS-CoV-2 concentrations in wastewater samples, and whether the association of SARS-CoV-2 with COVID-19 cases and mortality improves when adjusted for meteorological conditions and sample pH value in Miami-Dade County, FL.

Methods

Daily wastewater samples were collected from Miami-Dade Wastewater Treatment Plant in Key Biscayne, Florida from August 2021 to August 2022. The samples were analyzed for pH and spiked with OC43. RNA was extracted from the concentrated wastewater sample and SARS-CoV-2 was quantified using qPCR. COVID-19 and mortality data were acquired from the Centers for Disease Control and Prevention (CDC) and meteorological data from the National Climatic Data Center. COVID-19 case and mortality rates were modelled with respect to time-lagged wastewater SARS-CoV-2 adjusting for meteorological conditions, and sample pH value and OC43 recovery.

Results

Temperature, dew point, pH values and OC43 recovery showed significant associations with wastewater SARS-CoV-2. Time-lagged wastewater SARS-CoV-2 showed significant associations with COVID-19 case and mortality incidence rates. This association improved when wastewater SARS-CoV-2 levels were adjusted for (or instrumented on) meteorological conditions, OC43 recovery, and sample pH. A 0.47% change in COVID-19 case incidence rate was associated with 1% change in wastewater SARS-CoV-2 (β ~ 0.47; 95% CI = 0.29 - 0.64; p < 0.001). A 0.12 % change in COVID-19 mortality rate was associated with 1 % change in SARS-CoV-2 in wastewater 44 days prior. A 0.07% decline in COVID-19 mortality rate was associated with a unit increase in ambient temperature 28 days prior.

Discussion

Time lagged wastewater SARS-CoV-2 (and its adjustment for sample pH and RNA recovery) and meteorological conditions can be used for the surveillance of COVID-19 case and mortality. These findings can be extrapolated to improve the surveillance of other infectious diseases by proactive measurements of infectious agent(s) in the wastewater samples, adjusting for meteorological conditions and sample pH value.

An improved method for determining frequency of multiple variants of SARS-CoV-2 in wastewater using qPCR assays

Wastewater-based surveillance has become an effective tool around the globe for indirect monitoring of COVID-19 in communities. Variants of Concern (VOCs) have been detected in wastewater by use of reverse transcription polymerase chain reaction (RT-PCR) or whole genome sequencing (WGS). Rapid, reliable RT-PCR assays continue to be needed to determine the relative frequencies of VOCs and sub-lineages in wastewater-based surveillance programs. The presence of multiple mutations in a single region of the N-gene allowed for the design of a single amplicon, multiple probe assay, that can distinguish among several VOCs in wastewater RNA extracts. This approach which multiplexes probes designed to target mutations associated with specific VOC's along with an intra-amplicon universal probe (non-mutated region) was validated in singleplex and multiplex. The prevalence of each mutation (i.e. VOC) is estimated by comparing the abundance of the targeted mutation with a non-mutated and highly conserved region within the same amplicon. This is advantageous for the accurate and rapid estimation of variant frequencies in wastewater. The N200 assay was applied to monitor frequencies of VOCs in wastewater extracts from several communities in Ontario, Canada in near real time from November 28, 2021 to January 4, 2022. This includes the period of the rapid replacement of the Delta variant with the introduction of the Omicron variant in these Ontario communities in early December 2021. The frequency estimates using this assay were highly reflective of clinical WGS estimates for the same communities. This style of qPCR assay, which simultaneously measures signal from a non-mutated comparator probe and multiple mutation-specific probes contained within a single qPCR amplicon, can be applied to future assay development for rapid and accurate estimations of variant frequencies.

Human viral nucleic acids concentrations in wastewater solids from Central and Coastal California USA

We measured concentrations of SARS-CoV-2, influenza A and B virus, respiratory syncytial virus (RSV), mpox virus, human metapneumovirus, norovirus GII, and pepper mild mottle virus nucleic acids in wastewater solids at twelve wastewater treatment plants in Central California, USA. Measurements were made daily for up to two years, depending on the wastewater treatment plant. Measurements were made using digital droplet (reverse-transcription-) polymerase chain reaction (RT-PCR) following best practices for making environmental molecular biology measurements. These data can be used to better understand disease occurrence in communities contributing to the wastewater.

Recent progress on wastewater-based epidemiology for COVID-19 surveillance: A systematic review of analytical procedures and epidemiological modeling

On March 11, 2020, the World Health Organization declared the coronavirus disease 2019 (COVID-19), whose causative agent is the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), a pandemic. This virus is predominantly transmitted via respiratory droplets and shed via sputum, saliva, urine, and stool. Wastewater-based epidemiology (WBE) has been able to monitor the circulation of viral pathogens in the population. This tool demands both in-lab and computational work to be meaningful for, among other purposes, the prediction of outbreaks. In this context, we present a systematic review that organizes and discusses laboratory procedures for SARS-CoV-2 RNA quantification from a wastewater matrix, along with modeling techniques applied to the development of WBE for COVID-19 surveillance. The goal of this review is to present the current panorama of WBE operational aspects as well as to identify current challenges related to it. Our review was conducted in a reproducible manner by following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines for systematic reviews. We identified a lack of standardization in wastewater analytical procedures. Regardless, the reverse transcription-quantitative polymerase chain reaction (RT-qPCR) approach was the most reported technique employed to detect and quantify viral RNA in wastewater samples. As a more convenient sample matrix, we suggest the solid portion of wastewater to be considered in future investigations due to its higher viral load compared to the liquid fraction. Regarding the epidemiological modeling, the data-driven approach was consistently used for the prediction of variables associated with outbreaks. Future efforts should also be directed toward the development of rapid, more economical, portable, and accurate detection devices.

Structured Ethical Review for Wastewater-Based Testing

Wastewater-based testing (WBT) for SARS-CoV-2 has rapidly expanded over the past three years due to its ability to provide a comprehensive measurement of disease prevalence independent of clinical testing. The development and simultaneous application of the field blurred the boundary between measuring biomarkers for research activities and for pursuit of public health goals, both areas with well-established ethical frameworks. Currently, WBT practitioners do not employ a standardized ethical review process (or associated data management safeguards), introducing the potential for adverse outcomes for WBT professionals and community members. To address this deficiency, an interdisciplinary group developed a framework for a structured ethical review of WBT. The workshop employed a consensus approach to create this framework as a set of 11-questions derived from primarily public health guidance because of the common exemption of wastewater samples to human subject research considerations. This study retrospectively applied the set of questions to peer- reviewed published reports on SARS-CoV-2 monitoring campaigns covering the emergent phase of the pandemic from March 2020 to February 2022 (n=53). Overall, 43% of the responses to the questions were unable to be assessed because of lack of reported information. It is therefore hypothesized that a systematic framework would at a minimum improve the communication of key ethical considerations for the application of WBT. Consistent application of a standardized ethical review will also assist in developing an engaged practice of critically applying and updating approaches and techniques to reflect the concerns held by both those practicing and being monitored by WBT supported campaigns.

Abstract Figure

Synopsis

Development of a structured ethical review facilitates retrospective analysis of published studies and drafted scenarios in the context of wastewater-based testing.

A wastewater-based risk index for SARS-CoV-2 infections among three cities on the Canadian Prairie

Wastewater surveillance (WWS) is useful to better understand the spreading of coronavirus disease 2019 (COVID-19) in communities, which can help design and implement suitable mitigation measures. The main objective of this study was to develop the Wastewater Viral Load Risk Index (WWVLRI) for three Saskatchewan cities to offer a simple metric to interpret WWS. The index was developed by considering relationships between reproduction number, clinical data, daily per capita concentrations of virus particles in wastewater, and weekly viral load change rate. Trends of daily per capita concentrations of SARS-CoV-2 in wastewater for Saskatoon, Prince Albert, and North Battleford were similar during the pandemic, suggesting that per capita viral load can be useful to quantitatively compare wastewater signals among cities and develop an effective and comprehensible WWVLRI. The effective reproduction number (R_t) and the daily per capita efficiency adjusted viral load thresholds of 85 × 10⁶ and 200 × 10⁶ N2 gene counts (gc)/population day (pd) were determined. These values with rates of change were used to categorize the potential for COVID-19 outbreaks and subsequent declines. The weekly average was considered 'low risk' when the per capita viral load was 85 × 10⁶ N2 gc/pd. A 'medium risk' occurs when the per capita copies were between 85 × 10⁶ and 200 × 10⁶ N2 gc/pd. with a rate of change <100 %. The start of an outbreak is indicated by a 'medium-high' risk classification when the week-over-week rate of change was >100 %, and the absolute magnitude of concentrations of viral particles was >85 × 10⁶ N2 gc/pd. Lastly, a 'high risk' occurs when the viral load exceeds 200 × 10⁶ N2 gc/pd. This methodology provides a valuable resource for decision-makers and health authorities, specifically given the limitation of COVID-19 surveillance based on clinical data.

Wastewater early warning system for SARS-CoV-2 outbreaks and variants in a Coruña, Spain

Wastewater-based epidemiology has been widely used as a cost-effective method for tracking the COVID-19 pandemic at the community level. Here we describe COVIDBENS, a wastewater surveillance program running from June 2020 to March 2022 in the wastewater treatment plant of Bens in A Coruña (Spain). The main goal of this work was to provide an effective early warning tool based in wastewater epidemiology to help in decision-making at both the social and public health levels. RT-qPCR procedures and Illumina sequencing were used to weekly monitor the viral load and to detect SARS-CoV-2 mutations in wastewater, respectively. In addition, own statistical models were applied to estimate the real number of infected people and the frequency of each emerging variant circulating in the community, which considerable improved the surveillance strategy. Our analysis detected 6 viral load waves in A Coruña with concentrations between 10³ and 10⁶ SARS-CoV-2 RNA copies/L. Our system was able to anticipate community outbreaks during the pandemic with 8-36 days in advance with respect to clinical reports and, to detect the emergence of new SARS-CoV-2 variants in A Coruña such as Alpha (B.1.1.7), Delta (B.1.617.2), and Omicron (B.1.1.529 and BA.2) in wastewater with 42, 30, and 27 days, respectively, before the health system did. Data generated here helped local authorities and health managers to give a faster and more efficient response to the pandemic situation, and also allowed important industrial companies to adapt their production to each situation. The wastewater-based epidemiology program developed in our metropolitan area of A Coruña (Spain) during the SARS-CoV-2 pandemic served as a powerful early warning system combining statistical models with mutations and viral load monitoring in wastewater over time.

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

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

Monitoring of COVID-19 in wastewater across the Eastern Upper Peninsula of Michigan

Wastewater-based epidemiology is being used as a tool to monitor the spread of COVID-19 and provide an early warning for the presence or increase of clinical cases in a community. The majority of wastewater-based epidemiology for COVID-19 tracking has been utilized in sewersheds that service populations in the tens-to-hundreds of thousands. Few studies have been conducted to assess the usefulness of wastewater in predicting COVID-19 clinical cases specifically in rural areas. This study collected samples from 16 locations across the Eastern Upper Peninsula of Michigan from June to December 2021. Sampling locations included 12 rural municipalities, a Tribal housing community and casino, a public university, three municipalities that also contained a prison, and a small island with heavy tourist traffic. Samples were analyzed for SARS-CoV-2 N1, N2, and variant gene copies using reverse transcriptase droplet digital polymerase chain reaction (RT-ddPCR). Wastewater N1 and N2 gene copies and clinical case counts were correlated to determine if wastewater results were predictive of clinical cases. Significant correlation between N1 and N2 gene copies and clinical cases was found for all sites (⍴= 0.89 to 0.48). N1 and N2 wastewater results were predictive of clinical case trends within 0-7 days. The Delta variant was detected in the Pickford and St. Ignace samples more than 12-days prior to the first reported Delta clinical cases in their respective counties. Locations with low correlation could be attributed to their high rates of tourism. This is further supported by the high correlation seen in the public university, which is a closed population. Long-term wastewater monitoring over a large, rural geographic area is useful for informing the public of potential outbreaks in the community regardless of asymptomatic cases and access to clinical testing.

Wastewater Surveillance Can Function as an Early Warning System for COVID-19 in Low-Incidence Settings

Introduction: During the first two years of the COVID-19 pandemic, Australia implemented a series of international and interstate border restrictions. The state of Queensland experienced limited COVID-19 transmission and relied on lockdowns to stem any emerging COVID-19 outbreaks. However, early detection of new outbreaks was difficult. In this paper, we describe the wastewater surveillance program for SARS-CoV-2 in Queensland, Australia, and report two case studies in which we aimed to assess the potential for this program to provide early warning of new community transmission of COVID-19. Both case studies involved clusters of localised transmission, one originating in a Brisbane suburb (Brisbane Inner West) in July–August 2021, and the other originating in Cairns, North Queensland in February–March 2021. Materials and Methods: Publicly available COVID-19 case data derived from the notifiable conditions (NoCs) registry from the Queensland Health data portal were cleaned and merged spatially with the wastewater surveillance data using statistical area 2 (SA2) codes. The positive predictive value and negative predictive value of wastewater detection for predicting the presence of COVID-19 reported cases were calculated for the two case study sites. Results: Early warnings for local transmission of SARS-CoV-2 through wastewater surveillance were noted in both the Brisbane Inner West cluster and the Cairns cluster. The positive predictive value of wastewater detection for the presence of notified cases of COVID-19 in Brisbane Inner West and Cairns were 71.4% and 50%, respectively. The negative predictive value for Brisbane Inner West and Cairns were 94.7% and 100%, respectively. Conclusions: Our findings highlight the utility of wastewater surveillance as an early warning tool in low COVID-19 transmission settings.

Statistical Analysis of SARS-CoV-2 Using Wastewater-Based Data of Stockholm, Sweden

An approach based on wastewater epidemiology can be used to monitor the COVID-19 pandemic by assessing the gene copy number of SARS-CoV-2 in wastewater. In the present study, we statistically analyzed such data from six inlets of three wastewater treatment plants, covering six regions of Stockholm, Sweden, collected over an approximate year period (week 16 of 2020 to week 22 of 2021). SARS-CoV-2 gene copy number and population-based biomarker PMMoV, as well as clinical data, such as the number of positive cases, intensive care unit numbers, and deaths, were analyzed statistically using correlations and principal component analysis (PCA). Despite the population differences, the PCA for the Stockholm dataset showed that the case numbers are well grouped across wastewater treatment plants. Furthermore, when considering the data from the whole of Stockholm, the wastewater characteristics (flow rate m³/day, PMMoV Ct value, and SARS-CoV gene copy number) were significantly correlated with the public health agency's report of SARS-CoV-2 infection rates (0.419 to 0.95, p-value < 0.01). However, while the PCA results showed that the case numbers for each wastewater treatment plant were well grouped concerning PC1 (37.3%) and PC2 (19.67%), the results from the correlation analysis for the individual wastewater treatment plants showed varied trends. SARS-CoV-2 fluctuations can be accurately predicted through statistical analyses of wastewater-based epidemiology, as demonstrated in this study.

Quantification of SARS-CoV-2 in wastewater samples from hospitals treating COVID-19 patients during the first wave of the pandemic in Brazil

The COVID-19 pandemic has caused a global health crisis, and wastewater-based epidemiology (WBE) has emerged as an important tool to assist public health decision-making. Recent studies have shown that the SARS-CoV-2 RNA concentration in wastewater samples is a reliable indicator of the severity of the pandemic for large populations. However, few studies have established a strong correlation between the number of infected people and the viral concentration in wastewater due to variations in viral shedding over time, viral decay, infiltration, and inflow. Herein we present the relationship between the number of COVID-19-positive patients and the viral concentration in wastewater samples from three different hospitals (A, B, and C) in the city of Belo Horizonte, Minas Gerais, Brazil. A positive and strong correlation between wastewater SARS-CoV-2 concentration and the number of confirmed cases was observed for Hospital B for both regions of the N gene (R = 0.89 and 0.77 for N1 and N2, respectively), while samples from Hospitals A and C showed low and moderate correlations, respectively. Even though the effects of viral decay and infiltration were minimized in our study, the variability of viral shedding throughout the infection period and feces dilution due to water usage for different activities in the hospitals could have affected the viral concentrations. These effects were prominent in Hospital A, which had the smallest sewershed population size, and where no correlation between the number of defecations from COVID-19 patients and viral concentration in wastewater was observed. Although we could not determine trends in the number of infected patients through SARS-CoV-2 concentrations in hospitals' wastewater samples, our results suggest that wastewater monitoring can be efficient for the detection of infected individuals at a local level, complementing clinical data.

Model training periods impact estimation of COVID-19 incidence from wastewater viral loads

Wastewater-based epidemiology (WBE) has been deployed broadly as an early warning tool for emerging COVID-19 outbreaks. WBE can inform targeted interventions and identify communities with high transmission, enabling quick and effective responses. As the wastewater (WW) becomes an increasingly important indicator for COVID-19 transmission, more robust methods and metrics are needed to guide public health decision-making. This research aimed to develop and implement a mathematical framework to infer incident cases of COVID-19 from SARS-CoV-2 levels measured in WW. We propose a classification scheme to assess the adequacy of model training periods based on clinical testing rates and assess the sensitivity of model predictions to training periods. A testing period is classified as adequate when the rate of change in testing is greater than the rate of change in cases. We present a Bayesian deconvolution and linear regression model to estimate COVID-19 cases from WW data. The effective reproductive number is estimated from reconstructed cases using WW. The proposed modeling framework was applied to three Northern California communities served by distinct WW treatment plants. The results showed that training periods with adequate testing are essential to provide accurate projections of COVID-19 incidence.