Understanding the dynamic relation between wastewater SARS-CoV-2 signal and clinical metrics throughout the pandemic

Variability of Clinical Metrics in Small Population Communities Drive Perceived Wastewater and Environmental Surveillance Data Quality: Ontario, Canada-Wide Study

The emergence of COVID-19 in Canada has led to over 4.9 million cases and 59,000 deaths by May 2024. Traditional clinical surveillance metrics (hospital admissions and clinical laboratory-positive cases) were complemented with wastewater and environmental monitoring (WEM) to monitor SARS-CoV-2 incidence. However, challenges in public health integration of WEM persist due to perceived limitations of WEM data quality, potentially driving inconsistent correlations variability and lead times. This study investigates how factors like population size, WEM measurement magnitude, site isolation status, hospital admissions, and clinical laboratory-positive cases affect WEM data correlations and variability in Ontario. The analysis uncovers a direct relationship between clinical surveillance data and the population size of the surveyed sewersheds, while WEM measurement magnitude was not directly impacted by population size. Higher variability in clinical surveillance data was observed in smaller sewersheds, likely reducing correlation strength for inferring COVID-19 incidence. Population size significantly influenced correlation quality, with thresholds identified at ∼66,000 inhabitants for strong WEM-hospital admissions correlations and ∼68,000 inhabitants for WEM-laboratory-positive cases during waned vaccination periods in Ontario (the Omicron BA.1 wave). During significant vaccination immunization (the Omicron BA.2 wave), these thresholds increased to ∼187,000 and 238,000, respectively. These findings highlight the benefit of WEM for strategic public health monitoring and interventions, especially in smaller communities. This study provides insights for enhancing public health decision making and disease monitoring through WEM, applicable to COVID-19 and potentially other diseases.

The use of wastewater surveillance to estimate SARS-CoV-2 fecal viral shedding pattern and identify time periods with intensified transmission

BACKGROUND

Wastewater-based surveillance is an important tool for monitoring the COVID-19 pandemic. However, it remains challenging to translate wastewater SARS-CoV-2 viral load to infection number, due to unclear shedding patterns in wastewater and potential differences between variants.

OBJECTIVES

We utilized comprehensive wastewater surveillance data and estimates of infection prevalence (i.e., the source of the viral shedding) available for New York City (NYC) to characterize SARS-CoV-2 fecal shedding pattern over multiple COVID-19 waves.

METHODS

We collected SARS-CoV-2 viral wastewater measurements in NYC during August 31, 2020 - August 29, 2023 (N = 3794 samples). Combining with estimates of infection prevalence (number of infectious individuals including those not detected as cases), we estimated the time-lag, duration, and per-infection fecal shedding rate for the ancestral/Iota, Delta, and Omicron variants, separately. We also developed a procedure to identify occasions with intensified transmission.

RESULTS

Models suggested fecal viral shedding likely starts around the same time as and lasts slightly longer than respiratory tract shedding. Estimated fecal viral shedding rate was highest during the ancestral/Iota variant wave, at 1.44 (95% CI: 1.35 - 1.53) billion RNA copies in wastewater per day per infection (measured by RT-qPCR), and decreased by around 20% and 50-60% during the Delta wave and Omicron period, respectively. We identified around 200 occasions during which the wastewater SARS-CoV-2 viral load exceeded the expected level in any of the city's 14 sewersheds. These anomalies disproportionally occurred during late January, late April-early May, early August, and from late-November to late-December, with frequencies exceeding the expectation assuming random occurrence (P < 0.05; bootstrapping test).

DISCUSSION

These estimates may be useful in understanding changes in underlying infection rate and help quantify changes in COVID-19 transmission and severity over time. We have also demonstrated that wastewater surveillance data can support the identification of time periods with potentially intensified transmission.

Sewage surveillance revealed the seasonality and prevalence of respiratory syncytial virus and its implications for seasonal immunization strategy in low and middle-income regions of China

Respiratory syncytial virus (RSV) represents a significant global health threat, with a disproportionately high disease burden in low and middle-income regions (LMIRs). Estimating the prevalence of RSV infection at the community levels remains a huge challenge, particularly in LMIRs where clinical data are scarce. In this study, we first detected RSV RNA in the fecal samples from 300 patients to understand the fecal shedding patterns of RSV. Meanwhile, we conducted sewage surveillance of RSV in four geographically distinct LMIRs in China from April 2023 to June 2024. Real-time reverse transcription quantitative Polymerase Chain Reaction (RT-qPCR) was employed to monitor the dynamics of sewage RSV concentration in a typical sewershed from Yingkou, Xi'an, Nanchang, and Nanning, respectively. Subsequent amplicon sequencing was conducted to understand the genotype and mutations of sewage RSV. Through RT-qPCR, we observed two RSV epidemics that lasted from late April to May and October to February in both Yingkou and Xi'an. For Nanchang, only one RSV epidemic was observed which emerged from September to February. Notably, in Nanning, a prolonged RSV epidemic was observed from August to April, suggesting RSV vaccination in Nanning faced more challenges. Amplicon sequencing revealed that sewage RSV found in four LMIRs is genetically distinct, highlighting the need for local initiatives for wastewater monitoring of RSV. This study filled the gaps in previous assessment of suitability of RSV vaccination in LMIRs based on clinical surveillance, demonstrating the effectiveness of wastewater surveillance in guiding public health interventions.

The Crucial Role of Laboratory Medicine in Addressing Future Public Health Infectious Threats: Insights Gained from the COVID-19 Pandemic

Laboratory testing has played a pivotal role throughout the coronavirus disease 2019 (COVID-19) pandemic, exemplifying the importance of in vitro diagnostics in addressing public health threats posed by outbreaks of infectious diseases. This article aims to present key insights from our expertise, derived from evidence gathered during the COVID-19 pandemic, to inform strategies for managing future infectious challenges. Current scientific evidence underscores that patient sample testing not only allows to diagnose an acute severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection, but also supports outbreak prediction, improved control measures, anticipation of pressure on the healthcare system, mitigation of adverse clinical outcomes, and early detection of emerging SARS-CoV-2 variants. Additionally, wastewater monitoring has emerged as a powerful tool for forecasting disease burden, including both prevalence and severity. Collectively, these findings underscore the value of diagnostic testing and wastewater surveillance in guiding healthcare planning and optimizing resource allocation during the COVID-19 pandemic, offering a valid framework to be applied to future public health threats, especially to any potential outbreak of "Disease X" that may emerge in the future.

Co-occurrence of SARS-CoV-2 variants in rivers and sewage in India and Brazil

The genomic monitoring of SARS-CoV-2 variants of concern (VOCs) in riverine and sewage water has been widely used as an epidemiological tool worldwide. But its utility for epidemiological assessments still needs to be evaluated in some areas. Our study encompassed thirteen Brazilian rivers spanning a vast urban expanse across the states of Rio de Janeiro, São Paulo, and Paraná. The sampled rivers in Rio de Janeiro are heavily contaminated with sewage. Meanwhile, the Indian samples were all wastewater before joining the water bodies from urban regions (Andhra Pradesh and Telangana). The viral copies were quantified using quantitative polymerase chain reaction (qPCR) in all examined samples (N = 91). The abundance of viral particles varied from 567 to 85,700,000 copies/ml. Subsequently, Illumina CovidSeq was applied to identify the major variants. In Brazil, while a single SARS-CoV-2 VOC was identified for just a few samples (6/50, 12 %), most samples harbored multiple VOCs (44/50, 88 %). In India only one probed sample had a single variant identified. Gamma (2021) and Omicron (2021 and 2022) were the most abundant variants. Delta and Omicron genetic material were detected in Rio de Janeiro city rivers before Brazil's first cases of these variants. Several negative samples in the Real-Time RT-PCR (qPCR) turned out to have SARS-CoV-2 sequences suggesting CovidSeq was more sensitive than RT-PCR for virus detection in environmental samples. Sewage surveillance holds promise for early detection of emerging variants driving pandemic waves, exemplified by the Delta and Omicron variants, potentially offering a preemptive advantage over clinical sample reports.

Sewer transport conditions and their role in the decay of endogenous SARS-CoV-2 and pepper mild mottle virus from source to collection

This study presents a comprehensive analysis of the decay patterns of endogenous SARS-CoV-2 and Pepper mild mottle virus (PMMoV) within wastewaters spiked with stool from infected patients expressing COVID-19 symptoms, and hence explores the decay of endogenous SARS-CoV-2 and PMMoV targets in wastewaters from source to collection of the sample. Stool samples from infected patients were used as endogenous viral material to more accurately mirror real-world decay processes compared to more traditionally used lab-propagated spike-ins. As such, this study includes data on early decay stages of endogenous viral targets in wastewaters that are typically overlooked when performing decay studies on wastewaters harvested from wastewater treatment plants that contain already-degraded endogenous material. The two distinct sewer transport conditions of dynamic suspended sewer transport and bed and near-bed sewer transport were simulated in this study at temperatures of 4 °C, 12 °C and 20 °C to elucidate decay under these two dominant transport conditions within wastewater infrastructure. The dynamic suspended sewer transport was simulated over 35 h, representing typical flow conditions, whereas bed and near-bed transport extended to 60 days to reflect the prolonged settling of solids in sewer systems during reduced flow periods. In dynamic suspended sewer transport, no decay was observed for SARS-CoV-2, PMMoV, or total RNA over the 35-h period, and temperature ranging from 4 °C to 20 °C had no noticeable effect. Conversely, experiments simulating bed and near-bed transport conditions revealed significant decreases in SARS-CoV-2 and total RNA concentrations by day 2, and PMMoV concentrations by day 3. Only PMMoV exhibited a clear trend of increasing decay constant with higher temperatures, suggesting that while temperature influences decay dynamics, its impact may be less significant than previously assumed, particularly for endogenous RNA that is bound to dissolved organic matter in wastewater. First order decay models were inadequate for accurately fitting decay curves of SARS-CoV-2, PMMoV, and total RNA in bed and near-bed transport conditions. F-tests confirmed the superior fit of the two-phase decay model compared to first order decay models across temperatures of 4 °C-20 °C. Finally, and most importantly, total RNA normalization emerged as an appropriate approach for correcting the time decay of SARS-CoV-2 exposed to bed and near-bed transport conditions. These findings highlight the importance of considering decay from the point of entry in the sewers, sewer transport conditions, and normalization strategies when assessing and modelling the impact of viral decay rates in wastewater systems. This study also emphasizes the need for ongoing research into the diverse and multifaceted factors that influence these decay rates, which is crucial for accurate public health monitoring and response strategies.

Estimating epidemic trajectories of SARS-CoV-2 and influenza A virus based on wastewater monitoring and a novel machine learning algorithm

The COVID-19 pandemic has altered the circulation of non-SARS-CoV-2 respiratory viruses. In this study, we carried out wastewater surveillance of SARS-CoV-2 and influenza A virus (IAV) in three key port cities in China through real-time quantitative PCR (RT-qPCR). Next, a novel machine learning algorithm (MLA) based on Gaussian model and random forest model was used to predict the epidemic trajectories of SARS-CoV-2 and IAV. The results showed that from February 2023 to January 2024, three port cities experienced two waves of SARS-CoV-2 infection, which peaked in late-May and late-August 2023, respectively. Two waves of IAV were observed in the spring and winter of 2023, respectively with considerable variations in terms of onset/offset date and duration. Furthermore, we employed MLA to extract the key features of epidemic trajectories of SARS-CoV-2 and IAV from February 3rd, to October 15th, 2023, and thereby predicted the epidemic trends of SARS-CoV-2 and IAV from October 16th, 2023 to April 22nd, 2024, which showed high consistency with the observed values. These collective findings offer an important understanding of SARS-CoV-2 and IAV epidemics, suggesting that wastewater surveillance together with MLA emerges as a powerful tool for risk assessment of respiratory viral diseases and improving public health preparedness.

Effect of SARS-CoV-2 shedding rate distribution of individuals during their disease days on the estimation of the number of infected people. Application of wastewater-based epidemiology to the city of Thessaloniki, Greece

During the COVID-19 pandemic, wastewater-based epidemiology has proved to be an important tool for monitoring the spread of a disease in a population. Indeed, wastewater surveillance was successfully used as a complementary approach to support public health monitoring schemes and decision-making policies. An essential feature for the estimation of a disease transmission using wastewater data is the distribution of viral shedding rate of individuals in their personal human wastes as a function of the days of their infection. Several candidate shapes for this function have been proposed in literature for SARS-CoV-2. The purpose of the present work is to explore the proposed function shapes and examine their significance on analyzing wastewater SARS-CoV-2 shedding rate data. For this purpose, a simple model is employed applying to medical surveillance and wastewater data of the city of Thessaloniki during a period of Omicron variant domination in 2022. The distribution shapes are normalized with respect to the total virus shedding and then their basic features are investigated. Detailed analysis reveals that the main parameter determining the results of the model is the difference between the day of maximum shedding rate and the day of infection reporting. Since the latter is not part of the distribution shape, the major feature of the distribution affecting the estimation of the number of infected people is the day of maximum shedding rate with respect to the initial infection day. On the contrary, the duration of shedding (total number of disease days) as well as the exact shape of the distribution are by far less important. The incorporation of such wastewater surveillance models in conventional epidemiological models - based on recorded disease transmission data- may improve predictions for disease spread during outbreaks.

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.

The Use of Wastewater Surveillance to Estimate SARS-CoV-2 Fecal Viral Shedding Pattern and Identify Time Periods with Intensified Transmission

Background

Wastewater-based surveillance is an important tool for monitoring the COVID-19 pandemic. However, it remains challenging to translate wastewater SARS-CoV-2 viral load to infection number, due to unclear shedding patterns in wastewater and potential differences between variants.

Objectives

We utilized comprehensive wastewater surveillance data and estimates of infection prevalence (i.e., the source of the viral shedding) available for New York City (NYC) to characterize SARS-CoV-2 fecal shedding pattern over multiple COVID-19 waves.

Methods

We collected SARS-CoV-2 viral wastewater measurements in NYC during August 31, 2020 - August 29, 2023 ( N = 3794 samples). Combining with estimates of infection prevalence (number of infectious individuals including those not detected as cases), we estimated the time-lag, duration, and per-infection fecal shedding rate for the ancestral/Iota, Delta, and Omicron variants, separately. We also developed a procedure to identify occasions with intensified transmission.

Results

Models suggested fecal viral shedding likely starts around the same time as and lasts slightly longer than respiratory tract shedding. Estimated fecal viral shedding rate was highest during the ancestral/Iota variant wave, at 1.44 (95% CI: 1.35 - 1.53) billion RNA copies in wastewater per day per infection (measured by RT-qPCR), and decreased by ∼20% and 50-60% during the Delta wave and Omicron period, respectively. We identified around 200 occasions during which the wastewater SARS-CoV-2 viral load exceeded the expected level in any of 14 sewersheds. These anomalies disproportionally occurred during late January, late April - early May, early August, and from late-November to late-December, with frequencies exceeding the expectation assuming random occurrence ( P < 0.05; bootstrapping test).

Discussion

These estimates may be useful in understanding changes in underlying infection rate and help quantify changes in COVID-19 transmission and severity over time. We have also demonstrated that wastewater surveillance data can support the identification of time periods with potentially intensified transmission.

SARS-CoV-2 viral titer measurements in Ontario, Canada wastewaters throughout the COVID-19 pandemic

During the COVID-19 pandemic, the Province of Ontario, Canada, launched a wastewater surveillance program to monitor SARS-CoV-2, inspired by the early work and successful forecasts of COVID-19 waves in the city of Ottawa, Ontario. This manuscript presents a dataset from January 1, 2021, to March 31, 2023, with RT-qPCR results for SARS-CoV-2 genes and PMMoV from 107 sites across all 34 public health units in Ontario, covering 72% of the province's and 26.2% of Canada's population. Sampling occurred 2-7 times weekly, including geographical coordinates, serviced populations, physico-chemical water characteristics, and flowrates. In doing so, this manuscript ensures data availability and metadata preservation to support future research and epidemic preparedness through detailed analyses and modeling. The dataset has been crucial for public health in tracking disease locally, especially with the rise of the Omicron variant and the decline in clinical testing, highlighting wastewater-based surveillance's role in estimating disease incidence in Ontario.

Dynamic SARS-CoV-2 surveillance model combining seroprevalence and wastewater concentrations for post-vaccine disease burden estimates

BACKGROUND

Despite wide scale assessments, it remains unclear how large-scale severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) vaccination affected the wastewater concentration of the virus or the overall disease burden as measured by hospitalization rates.

METHODS

We used weekly SARS-CoV-2 wastewater concentration with a stratified random sampling of seroprevalence, and linked vaccination and hospitalization data, from April 2021-August 2021 in Jefferson County, Kentucky (USA). Our susceptible ( S ), vaccinated ( V ), variant-specific infected (I 1 andI 2 ), recovered ( R ), and seropositive ( T ) model ( S V I 2 R T ) tracked prevalence longitudinally. This was related to wastewater concentration.

RESULTS

Here we show the 64% county vaccination rate translate into about a 61% decrease in SARS-CoV-2 incidence. The estimated effect of SARS-CoV-2 Delta variant emergence is a 24-fold increase of infection counts, which correspond to an over 9-fold increase in wastewater concentration. Hospitalization burden and wastewater concentration have the strongest correlation (r = 0.95) at 1 week lag.

CONCLUSIONS

Our study underscores the importance of continuing environmental surveillance post-vaccine and provides a proof-of-concept for environmental epidemiology monitoring of infectious disease for future pandemic preparedness.

Saliva-based detection of SARS-CoV-2: a bibliometric analysis of global research

Saliva has emerged as a promising noninvasive biofluid for the diagnosis of oral and systemic diseases, including viral infections. During the coronavirus disease 2019 (COVID-19) pandemic, a growing number of studies focused on saliva-based detection of severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2). Taking advantage of the WoS core collection (WoSCC) and CiteSpace, we retrieved 1021 articles related to saliva-based detection of SARS-CoV-2 and conducted a comprehensive bibliometric analysis. We analyzed countries, institutions, authors, cited authors, and cited journals to summarize their contribution and influence and analyzed keywords to explore research hotspots and trends. From 2020 to 2021, research focused on viral transmission via saliva and verification of saliva as a reliable specimen, whereas from 2021 to the present, the focus of research has switched to saliva-based biosensors for SARS-CoV-2 detection. By far, saliva has been verified as a reliable specimen for SARS-CoV-2 detection, although a standardized procedure for saliva sampling and processing is needed. Studies on saliva-based detection of SARS-CoV-2 will promote the development of saliva-based diagnostics and biosensors for viral detection. Collectively, our findings could provide valuable information to help scientists perceive the basic knowledge landscapes on saliva-based detection of SARS-CoV-2, the past and current research hotspots, and future opportunities.

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.

Emergence and spread of SARS-CoV-2 variants of concern in Canada: a retrospective analysis from clinical and wastewater data

BACKGROUND

The spread of SARS-CoV-2 has been studied at unprecedented levels worldwide. In jurisdictions where molecular analysis was performed on large scales, the emergence and competition of numerous SARS-CoV-2lineages have been observed in near real-time. Lineage identification, traditionally performed from clinical samples, can also be determined by sampling wastewater from sewersheds serving populations of interest. Variants of concern (VOCs) and SARS-CoV-2 lineages associated with increased transmissibility and/or severity are of particular interest.

METHOD

Here, we consider clinical and wastewater data sources to assess the emergence and spread of VOCs in Canada retrospectively.

RESULTS

We show that, overall, wastewater-based VOC identification provides similar insights to the surveillance based on clinical samples. Based on clinical data, we observed synchrony in VOC introduction as well as similar emergence speeds across most Canadian provinces despite the large geographical size of the country and differences in provincial public health measures.

CONCLUSION

In particular, it took approximately four months for VOC Alpha and Delta to contribute to half of the incidence. In contrast, VOC Omicron achieved the same contribution in less than one month. This study provides significant benchmarks to enhance planning for future VOCs, and to some extent for future pandemics caused by other pathogens, by quantifying the rate of SARS-CoV-2 VOCs invasion in Canada.

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 identifies start to the pediatric respiratory syncytial virus season in two cities in Ontario, Canada

Introduction

Detection of community respiratory syncytial virus (RSV) infections informs the timing of immunoprophylaxis programs and hospital preparedness for surging pediatric volumes. In many jurisdictions, this relies upon RSV clinical test positivity and hospitalization (RSVH) trends, which are lagging indicators. Wastewater-based surveillance (WBS) may be a novel strategy to accurately identify the start of the RSV season and guide immunoprophylaxis administration and hospital preparedness.

Methods

We compared citywide wastewater samples and pediatric RSVH in Ottawa and Hamilton between August 1, 2022, and March 5, 2023. 24-h composite wastewater samples were collected daily and 5 days a week at the wastewater treatment facilities in Ottawa and Hamilton, Ontario, Canada, respectively. RSV WBS samples were analyzed in real-time for RSV by RT-qPCR.

Results

RSV WBS measurements in both Ottawa and Hamilton showed a lead time of 12 days when comparing the WBS data set to pediatric RSVH data set (Spearman's ρ = 0.90). WBS identify early RSV community transmission and declared the start of the RSV season 36 and 12 days in advance of the provincial RSV season start (October 31) for the city of Ottawa and Hamilton, respectively. The differing RSV start dates in the two cities is likely associated with geographical and regional variation in the incidence of RSV between the cities.

Discussion

Quantifying RSV in municipal wastewater forecasted a 12-day lead time of the pediatric RSVH surge and an earlier season start date compared to the provincial start date. These findings suggest an important role for RSV WBS to inform regional health system preparedness, reduce RSV burden, and understand variations in community-related illness as novel RSV vaccines and monoclonal antibodies become available.

Correlation between wastewater and COVID-19 case incidence rates in major California sewersheds across three variant periods

Monitoring for COVID-19 through wastewater has been used for adjunctive public health surveillance, with SARS-CoV-2 viral concentrations in wastewater correlating with incident cases in the same sewershed. However, the generalizability of these findings across sewersheds, laboratory methods, and time periods with changing variants and underlying population immunity has not been well described. The California Department of Public Health partnered with six wastewater treatment plants starting in January 2021 to monitor wastewater for SARS-CoV-2, with analyses performed at four laboratories. Using reported PCR-confirmed COVID-19 cases within each sewershed, the relationship between case incidence rates and wastewater concentrations collected over 14 months was evaluated using Spearman's correlation and linear regression. Strong correlations were observed when wastewater concentrations and incidence rates were averaged (10- and 7-day moving window for wastewater and cases, respectively, ρ = 0.73-0.98 for N1 gene target). Correlations remained strong across three time periods with distinct circulating variants and vaccination rates (winter 2020-2021/Alpha, summer 2021/Delta, and winter 2021-2022/Omicron). Linear regression revealed that slopes of associations varied by the dominant variant of concern, sewershed, and laboratory (β = 0.45-1.94). These findings support wastewater surveillance as an adjunctive public health tool to monitor SARS-CoV-2 community trends.

A Potent Recombinant Polyclonal Antibody Therapeutic for Protection Against New Severe Acute Respiratory Syndrome Coronavirus 2 Variants of Concern

Emerging variants of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) possess mutations that prevent antibody therapeutics from maintaining antiviral binding and neutralizing efficacy. Monoclonal antibodies (mAbs) shown to neutralize Wuhan-Hu-1 SARS-CoV-2 (ancestral) strain have reduced potency against newer variants. Plasma-derived polyclonal hyperimmune drugs have improved neutralization breadth compared with mAbs, but lower titers against SARS-CoV-2 require higher dosages for treatment. We previously developed a highly diverse, recombinant polyclonal antibody therapeutic anti-SARS-CoV-2 immunoglobulin hyperimmune (rCIG). rCIG was compared with plasma-derived or mAb standards and showed improved neutralization of SARS-CoV-2 across World Health Organization variants; however, its potency was reduced against some variants relative to ancestral, particularly omicron. Omicron-specific antibody sequences were enriched from yeast expressing rCIG-scFv and exhibited increased binding and neutralization to omicron BA.2 while maintaining ancestral strain binding and neutralization. Polyclonal antibody libraries such as rCIG can be utilized to develop antibody therapeutics against present and future SARS-CoV-2 threats.

Assessment of seasonality and normalization techniques for wastewater-based surveillance in Ontario, Canada

Introduction

Wastewater-based surveillance is at the forefront of monitoring for community prevalence of COVID-19, however, continued uncertainty exists regarding the use of fecal indicators for normalization of the SARS-CoV-2 virus in wastewater. Using three communities in Ontario, sampled from 2021-2023, the seasonality of a viral fecal indicator (pepper mild mottle virus, PMMoV) and the utility of normalization of data to improve correlations with clinical cases was examined.

Methods

Wastewater samples from Warden, the Humber Air Management Facility (AMF), and Kitchener were analyzed for SARS-CoV-2, PMMoV, and crAssphage. The seasonality of PMMoV and flow rates were examined and compared by Season-Trend-Loess decomposition analysis. The effects of normalization using PMMoV, crAssphage, and flow rates were analyzed by comparing the correlations to clinical cases by episode date (CBED) during 2021.

Results

Seasonal analysis demonstrated that PMMoV had similar trends at Humber AMF and Kitchener with peaks in January and April 2022 and low concentrations (troughs) in the summer months. Warden had similar trends but was more sporadic between the peaks and troughs for PMMoV concentrations. Flow demonstrated similar trends but was not correlated to PMMoV concentrations at Humber AMF and was very weak at Kitchener (r = 0.12). Despite the differences among the sewersheds, unnormalized SARS-CoV-2 (raw N1-N2) concentration in wastewater (n = 99-191) was strongly correlated to the CBED in the communities (r = 0.620-0.854) during 2021. Additionally, normalization with PMMoV did not improve the correlations at Warden and significantly reduced the correlations at Humber AMF and Kitchener. Flow normalization (n = 99-191) at Humber AMF and Kitchener and crAssphage normalization (n = 29-57) correlations at all three sites were not significantly different from raw N1-N2 correlations with CBED.

Discussion

Differences in seasonal trends in viral biomarkers caused by differences in sewershed characteristics (flow, input, etc.) may play a role in determining how effective normalization may be for improving correlations (or not). This study highlights the importance of assessing the influence of viral fecal indicators on normalized SARS-CoV-2 or other viruses of concern. Fecal indicators used to normalize the target of interest may help or hinder establishing trends with clinical outcomes of interest in wastewater-based surveillance and needs to be considered carefully across seasons and sites.

Predictive potential of SARS-CoV-2 RNA concentration in wastewater to assess the dynamics of COVID-19 clinical outcomes and infections

Coronavirus disease 2019 - caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) -, has triggered a worldwide pandemic resulting in 665 million infections and over 6.5 million deaths as of December 15, 2022. The development of different epidemiological tools have helped predict new outbreaks and assess the behavior of clinical variables in different health contexts. In this study, we aimed to monitor concentrations of SARS-CoV-2 in wastewater as a tool to predict the progression of clinical variables during Waves 3, 5, and 6 of the pandemic in the Spanish city of Xátiva from September 2020 to March 2022. We estimated SARS-CoV-2 RNA concentrations in 195 wastewater samples using the RT-PCR Diagnostic Panel validated by the Center for Disease Control and Prevention. We also compared the trends of several clinical variables (14-day cumulative incidence, positive cases, hospital cases and stays, critical cases and stays, primary care visits, and deaths) for each study wave against wastewater SARS-CoV-2 RNA concentrations using Pearson's product-moment correlations, a two-sided Mann-Whitney U test, and a cross-correlation analysis. We found strong correlations between SARS-CoV-2 concentrations with 14-day cumulative incidence and positive cases over time. Wastewater RNA concentrations showed strong correlations with these variables one and two weeks in advance. There were significant correlations with hospitalizations and critical care during Wave 3 and Wave 6; cross-correlations were stronger for hospitalization stays one week before during Wave 6. No association between vaccination percentages and wastewater viral concentrations was observed. Our findings support wastewater SARS-CoV-2 concentrations as a potential surveillance tool to anticipate infection and epidemiological data such as 14-day cumulative incidence, hospitalizations, and critical care stays. Public health authorities could use this epidemiological tool on a similar population as an aid for health care decision-making during an epidemic outbreak.

Omicron COVID-19 Case Estimates Based on Previous SARS-CoV-2 Wastewater Load, Regional Municipality of Peel, Ontario, Canada

We determined correlations between SARS-CoV-2 load in untreated water and COVID-19 cases and patient hospitalizations before the Omicron variant (September 2020-November 2021) at 2 wastewater treatment plants in the Regional Municipality of Peel, Ontario, Canada. Using pre-Omicron correlations, we estimated incident COVID-19 cases during Omicron outbreaks (November 2021-June 2022). The strongest correlation between wastewater SARS-CoV-2 load and COVID-19 cases occurred 1 day after sampling (r = 0.911). The strongest correlation between wastewater load and COVID-19 patient hospitalizations occurred 4 days after sampling (r = 0.819). At the peak of the Omicron BA.2 outbreak in April 2022, reported COVID-19 cases were underestimated 19-fold because of changes in clinical testing. Wastewater data provided information for local decision-making and are a useful component of COVID-19 surveillance systems.

20-Month monitoring of SARS-CoV-2 in wastewater of Curitiba, in Southern Brazil

The COVID-19 pandemic resulted in the collapse of healthcare systems and led to the development and application of several approaches of wastewater-based epidemiology to monitor infected populations. The main objective of this study was to carry out a SARS-CoV-2 wastewater based surveillance in Curitiba, Southern Brazil Sewage samples were collected weekly for 20 months at the entrance of five treatment plants representing the entire city and quantified by qPCR using the N1 marker. The viral loads were correlated with epidemiological data. The correlation by sampling points showed that the relationship between the viral loads and the number of reported cases was best described by a cross-correlation function, indicating a lag between 7 and 14 days amidst the variables, whereas the data for the entire city presented a higher correlation (0.84) with the number of positive tests at lag 0 (sampling day). The results also suggest that the Omicron VOC resulted in higher titers than the Delta VOC. Overall, our results showed that the approach used was robust as an early warning system, even with the use of different epidemiological indicators or changes in the virus variants in circulation. Therefore, it can contribute to public decision-makers and health interventions, especially in vulnerable and low-income regions with limited clinical testing capacity. Looking toward the future, this approach will contribute to a new look at environmental sanitation and should even induce an increase in sewage coverage rates in emerging countries.

Expansion of wastewater-based disease surveillance to improve health equity in California's Central Valley: sequential shifts in case-to-wastewater and hospitalization-to-wastewater ratios

Introduction

Over a third of the communities (39%) in the Central Valley of California, a richly diverse and important agricultural region, are classified as disadvantaged-with inadequate access to healthcare, lower socio-economic status, and higher exposure to air and water pollution. The majority of racial and ethnic minorities are also at higher risk of COVID-19 infection, hospitalization, and death according to the Centers for Disease Control and Prevention. Healthy Central Valley Together established a wastewater-based disease surveillance (WDS) program that aims to achieve greater health equity in the region through partnership with Central Valley communities and the Sewer Coronavirus Alert Network. WDS offers a cost-effective strategy to monitor trends in SARS-CoV-2 community infection rates.

Methods

In this study, we evaluated correlations between public health and wastewater data (represented as SARS-CoV-2 target gene copies normalized by pepper mild mottle virus target gene copies) collected for three Central Valley communities over two periods of COVID-19 infection waves between October 2021 and September 2022. Public health data included clinical case counts at county and sewershed scales as well as COVID-19 hospitalization and intensive care unit admissions. Lag-adjusted hospitalization:wastewater ratios were also evaluated as a retrospective metric of disease severity and corollary to hospitalization:case ratios.

Results

Consistent with other studies, strong correlations were found between wastewater and public health data. However, a significant reduction in case:wastewater ratios was observed for all three communities from the first to the second wave of infections, decreasing from an average of 4.7 ± 1.4 over the first infection wave to 0.8 ± 0.4 over the second.

Discussion

The decline in case:wastewater ratios was likely due to reduced clinical testing availability and test seeking behavior, highlighting how WDS can fill data gaps associated with under-reporting of cases. Overall, the hospitalization:wastewater ratios remained more stable through the two waves of infections, averaging 0.5 ± 0.3 and 0.3 ± 0.4 over the first and second waves, respectively.

Patterns of SARS-CoV-2 circulation revealed by a nationwide sewage surveillance programme, the Netherlands, August 2020 to February 2022

BackgroundSurveillance of SARS-CoV-2 in wastewater offers a near real-time tool to track circulation of SARS-CoV-2 at a local scale. However, individual measurements of SARS-CoV-2 in sewage are noisy, inherently variable and can be left-censored.AimWe aimed to infer latent virus loads in a comprehensive sewage surveillance programme that includes all sewage treatment plants (STPs) in the Netherlands and covers 99.6% of the Dutch population.MethodsWe applied a multilevel Bayesian penalised spline model to estimate time- and STP-specific virus loads based on water flow-adjusted SARS-CoV-2 qRT-PCR data for one to four sewage samples per week for each of the more than 300 STPs.ResultsThe model captured the epidemic upsurges and downturns in the Netherlands, despite substantial day-to-day variation in the measurements. Estimated STP virus loads varied by more than two orders of magnitude, from ca 1012 virus particles per 100,000 persons per day in the epidemic trough in August 2020 to almost 1015 per 100,000 in many STPs in January 2022. The timing of epidemics at the local level was slightly shifted between STPs and municipalities, which resulted in less pronounced peaks and troughs at the national level.ConclusionAlthough substantial day-to-day variation is observed in virus load measurements, wastewater-based surveillance of SARS-CoV-2 that is performed at high sampling frequency can track long-term progression of an epidemic at a local scale in near real time.

Contextualizing Wastewater-Based surveillance in the COVID-19 vaccination era

SARS-CoV-2 wastewater-based surveillance (WBS) offers a tool for cost-effective oversight of a population's infections. In the past two years, WBS has proven to be crucial for managing the pandemic across different geographical regions. However, the changing context of the pandemic due to high levels of COVID-19 vaccination warrants a closer examination of its implication towards SARS-CoV-2 WBS. Two main questions were raised: 1) Does vaccination cause shedding of viral signatures without infection? 2) Does vaccination affect the relationship between wastewater and clinical data? To answer, we review historical reports of shedding from viral vaccines in use prior to the COVID-19 pandemic including for polio, rotavirus, influenza and measles infection and provide a perspective on the implications of different COVID-19 vaccination strategies with regard to the potential shedding of viral signatures into the sewershed. Additionally, we reviewed studies that looked into the relationship between wastewater and clinical data and how vaccination campaigns could have affected the relationship. Finally, analyzing wastewater and clinical data from the Netherlands, we observed changes in the relationship concomitant with increasing vaccination coverage and switches in dominant variants of concern. First, that no vaccine-derived shedding is expected from the current commercial pipeline of COVID-19 vaccines that may confound interpretation of WBS data. Secondly, that breakthrough infections from vaccinated individuals contribute significantly to wastewater signals and must be interpreted in light of the changing dynamics of shedding from new variants of concern.