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Background: The unprecedented COVID-19 pandemic has highlighted the strategic value of wastewater-based surveillance (WBS) of SARS-CoV-2. This multisite 28-month-long study focused on WBS for older residents in 12 long-term care facilities (LTCFs) in Edmonton (AB, Canada) by assessing relationships between COVID-19, WBS, and serostatus during the pandemic., Methods: Wastewater samples collected two to three times per week were tested for SARS-CoV-2 using RT-quantitative PCR. The serostatus of antibodies was examined using immunoassays. The data of clinical COVID-19 outbreaks based on extensive testing were obtained from local public health officials. Analyses included calculating correlations between 7-day rolling averages for WBS and COVID-19 cases and investigating whether WBS led or lagged confirmed outbreaks using a multinomial test., Findings: Wastewater results correlated well with clinical COVID-19 infections and outbreaks at participating LTCFs. 1058 (36·0%) of 2936 collected wastewater samples were SARS-CoV-2 positive, compared with 1247 people (resident n=671, staff n=572, and unknown n=4) reporting positive test results of 21 673 clinical samples assessed (5·8%). WBS led clinical testing in 32 (60·4%) confirmed outbreaks, which was significantly different from WBS lagged (12 outbreaks [22·6%, 95% CI 11·3-33·7]). Non-detection of WBS SARS-CoV-2 served as a negative predictor for outbreaks. WBS results attested protective immunity in vaccinated individuals before the omicron wave. A parallel increase in the proportions of positive WBS SARS-CoV-2 and anti-nucleocapsid antibodies underlined that omicron was an immunity-evading variant despite high seropositivity of neutralising antibodies after multiple doses of vaccine., Interpretation: Implementation of WBS could enable targeted clinical investigations and improve cost-effectiveness of COVID-19 outbreak management in LTCFs. WBS and serostatus provided informed dynamic changes of infections and immunity. Critical evidence was that LTCF WBS is an effective early warning system to support rapid public health outbreak management and protect vulnerable older populations., Funding: Canadian Immunity Task Force for COVID-19 and Alberta Health., Competing Interests: Declaration of interests We declare no competing interests., (Copyright © 2024 The Author(s). Published by Elsevier Ltd.. All rights reserved.)

Background: Asymptomatic SARS-CoV-2 infection in children is highly prevalent but its acute and chronic implications have been minimally described., Methods: In this controlled case-ascertained household transmission study, we recruited asymptomatic children <18 years with SARS-CoV-2 nucleic acid testing performed at 12 tertiary care pediatric institutions in Canada and the United States. We attempted to recruit all test-positive children and 1 to 3 test-negative, site-matched controls. After 14 days' follow-up we assessed the clinical (ie, symptomatic) and combined (ie, test-positive, or symptomatic) secondary attack rates (SARs) among household contacts. Additionally, post-COVID-19 condition (PCC) was assessed in SARS-CoV-2-positive participating children after 90 days' follow-up., Results: A total of 111 test-positive and 256 SARS-CoV-2 test-negative asymptomatic children were enrolled between January 2021 and April 2022. After 14 days, excluding households with co-primary cases, the clinical SAR among household contacts of SARS-CoV-2-positive and -negative index children was 10.6% (19/179; 95% CI: 6.5%-16.1%) and 2.0% (13/663; 95% CI: 1.0%-3.3%), respectively (relative risk = 5.4; 95% CI: 2.7-10.7). In households with a SARS-CoV-2-positive index child, age <5 years, being pre-symptomatic (ie, developed symptoms after test), and testing positive during Omicron and Delta circulation periods (vs earlier) were associated with increased clinical and combined SARs among household contacts. Among 77 asymptomatic SARS-CoV-2-infected children with 90-day follow-up, 6 (7.8%; 95% CI: 2.9%-16.2%) reported PCC., Conclusions: Asymptomatic SARS-CoV-2-infected children, especially those <5 years, are important contributors to household transmission, with 1 in 10 exposed household contacts developing symptomatic illness within 14 days. Asymptomatic SARS-CoV-2-infected children may develop PCC., Competing Interests: Potential conflicts of interest . A. F. reports grants from the University of Calgary Eyes High Postdoctoral Fellowship and Canadian Institutes of Health Research Banting Postdoctoral Fellowship, and support for meetings and/or travel from a Trainee Travel Grant (Pediatric Academic Societies and Trainee travel grant), Alberta Children's Hospital Association, University of Calgary. L. A. reports grants from Pfizer. M. W. reports support for meetings and/or travel from US Acute Care Solutions (USACS), stock and a board of directors position for EM Device Lab. N. K. reports a leadership or fiduciary role for the Pediatric Emergency Care Applied Research, Pediatric Emergency Research Network; research funding from the National Institutes of Health (NIH), Health Resources and Services Administration (HRSA), and Patient-Centered Outcomes Research Institute (PCORI). R. M. reports royalties or licenses for Affinivax; consulting fees from GSK, Affinivax, and Merck; payment for lectures and support for meetings and/or travel from GSK; patents planned, issued, or pending for Boston Children's Hospital; being a member of the Affinivax board of directors; and stock options for Affinivax and Corner Therapeutics. S. M. M. reports grants from Sanofi, Merck, Pfizer National Minority Quality Forum (US); support for meetings and/or travel from Sanofi, Merck, GSK, Pfizer, CSL, Biosciences Association of Manitoba (BAM), and the National Minority Quality Forum (US); and participation on a Data and Safety Monitoring Board or Advisory board for Sanofi. T. A. F. reports grants from the National Heart, Lung, and Blood Institute (NHLBI) and leadership or fiduciary roles for the Society of Pediatric Research and the Pediatric Academic Societies. All other authors report no potential conflicts. All authors have submitted the ICMJE Form for Disclosure of Potential Conflicts of Interest. Conflicts that the editors consider relevant to the content of the manuscript have been disclosed., (© The Author(s) 2024. Published by Oxford University Press on behalf of Infectious Diseases Society of America. All rights reserved. For commercial re-use, please contact reprints@oup.com for reprints and translation rights for reprints. All other permissions can be obtained through our RightsLink service via the Permissions link on the article page on our site—for further information please contact journals.permissions@oup.com.)

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., (© 2024 The Authors. Statistics in Medicine published by John Wiley & Sons Ltd.)

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., Competing Interests: The authors declare no conflict of interest.

Wastewater-based surveillance is a valuable approach for monitoring COVID-19 at community level. Monitoring SARS-CoV-2 variants of concern (VOC) in wastewater has become increasingly relevant when clinical testing capacity and case-based surveillance are limited. In this study, we ascertained the turnover of six VOC in Alberta wastewater from May 2020 to May 2022. Wastewater samples from nine wastewater treatment plants across Alberta were analysed using VOC-specific RT-qPCR assays. The performance of the RT-qPCR assays in identifying VOC in wastewater was evaluated against next generation sequencing. The relative abundance of each VOC in wastewater was compared to positivity rate in COVID-19 testing. VOC-specific RT-qPCR assays performed comparatively well against next generation sequencing; concordance rates ranged from 89% to 98% for detection of Alpha, Beta, Gamma, Omicron BA.1 and Omicron BA.2, with a slightly lower rate of 85% for Delta ( p <  0.01). Elevated relative abundance of Alpha, Delta, Omicron BA.1 and BA.2 were each associated with increased COVID-19 positivity rate. Alpha, Delta and Omicron BA.2 reached 90% relative abundance in wastewater within 80, 111 and 62 days after their initial detection, respectively. Omicron BA.1 increased more rapidly, reaching a 90% relative abundance in wastewater after 35 days. Our results from VOC surveillance in wastewater correspond with clinical observations that Omicron is the VOC with highest disease burden over the shortest period in Alberta to date. The findings suggest that changes in relative abundance of a VOC in wastewater can be used as a supplementary indicator to track and perhaps predict COVID-19 burden in a population.

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., Competing Interests: Declaration of competing interest The authors have declared no conflicts of interest., (Copyright © 2023. Published by Elsevier B.V.)

Wastewater-based surveillance (WBS) has been established as a powerful tool that can guide health policy at multiple levels of government. However, this approach has not been well assessed at more granular scales, including large work sites such as University campuses. Between August 2021 and April 2022, we explored the occurrence of SARS-CoV-2 RNA in wastewater using qPCR assays from multiple complimentary sewer catchments and residential buildings spanning the University of Calgary's campus and how this compared to levels from the municipal wastewater treatment plant servicing the campus. Real-time contact tracing data was used to evaluate an association between wastewater SARS-CoV-2 burden and clinically confirmed cases and to assess the potential of WBS as a tool for disease monitoring across worksites. Concentrations of wastewater SARS-CoV-2 N1 and N2 RNA varied significantly across six sampling sites - regardless of several normalization strategies - with certain catchments consistently demonstrating values 1-2 orders higher than the others. Relative to clinical cases identified in specific sewersheds, WBS provided one-week leading indicator. Additionally, our comprehensive monitoring strategy enabled an estimation of the total burden of SARS-CoV-2 for the campus per capita, which was significantly lower than the surrounding community (p≤0.001). Allele-specific qPCR assays confirmed that variants across campus were representative of the community at large, and at no time did emerging variants first debut on campus. This study demonstrates how WBS can be efficiently applied to locate hotspots of disease activity at a very granular scale, and predict disease burden across large, complex worksites., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 The Authors. Published by Elsevier Ltd.. All rights reserved.)

The coronavirus disease 2019 (COVID-19) pandemic has placed significant burden on healthcare systems. We compared Clostridioides difficile infection (CDI) epidemiology before and during the pandemic across 71 hospitals participating in the Canadian Nosocomial Infection Surveillance Program. Using an interrupted time series analysis, we showed that CDI rates significantly increased during the COVID-19 pandemic.

With a unique and large size of testing results of 1,842 samples collected from 12 wastewater treatment plants (WWTP) for 14 months through from low to high prevalence of COVID-19, the sensitivity of RT-qPCR detection of SARS-CoV-2 RNA in wastewater that correspond to the communities was computed by using Probit analysis. This study determined the number of new COVID-19 cases per 100,000 population required to detect SARS-CoV-2 RNA in wastewater at defined probabilities and provided an evidence-based framework of wastewater-based epidemiology surveillance (WBE). Input data were positive and negative test results of SARS-CoV-2 RNA in wastewater samples and the corresponding new COVID-19 case rates per 100,000 population served by each WWTP. The analyses determined that RT-qPCR-based SARS-CoV-2 RNA detection threshold at 50%, 80% and 99% probability required a median of 8 (range: 4-19), 18 (9-43), and 38 (17-97) of new COVID-19 cases /100,000, respectively. Namely, the positive detection rate at 50%, 80% and 99% probability were 0.01%, 0.02%, and 0.04% averagely for new cases in the population. This study improves understanding of the performance of WBE SARS-CoV-2 RNA detection using the large datasets and prolonged study period. Estimated COVID-19 burden at a community level that would result in a positive detection of SARS-CoV-2 in wastewater is critical to support WBE application as a supplementary warning/monitoring system for COVID-19 prevention and control., (Copyright © 2022. Published by Elsevier B.V.)

Wastewater-based SARS-CoV-2 surveillance enables unbiased and comprehensive monitoring of defined sewersheds. We performed real-time monitoring of hospital wastewater that differentiated Delta and Omicron variants within total SARS-CoV-2-RNA, enabling correlation to COVID-19 cases from three tertiary-care facilities with >2100 inpatient beds in Calgary, Canada. RNA was extracted from hospital wastewater between August/2021 and January/2022, and SARS-CoV-2 quantified using RT-qPCR. Assays targeting R203M and R203K/G204R established the proportional abundance of Delta and Omicron, respectively. Total and variant-specific SARS-CoV-2 in wastewater was compared to data for variant specific COVID-19 hospitalizations, hospital-acquired infections, and outbreaks. Ninety-six percent (188/196) of wastewater samples were SARS-CoV-2 positive. Total SARS-CoV-2 RNA levels in wastewater increased in tandem with total prevalent cases (Delta plus Omicron). Variant-specific assessments showed this increase to be mainly driven by Omicron. Hospital-acquired cases of COVID-19 were associated with large spikes in wastewater SARS-CoV-2 and levels were significantly increased during outbreaks relative to nonoutbreak periods for total SARS-CoV2, Delta and Omicron. SARS-CoV-2 in hospital wastewater was significantly higher during the Omicron-wave irrespective of outbreaks. Wastewater-based monitoring of SARS-CoV-2 and its variants represents a novel tool for passive COVID-19 infection surveillance, case identification, containment, and potentially to mitigate viral spread in hospitals., (© 2022 The Authors. Journal of Medical Virology published by Wiley Periodicals LLC.)

Wastewater-based surveillance (WBS) data normalization is an analyte measurement correction that addresses variations resulting from dilution of fecal discharge by non-sanitary sewage, stormwater or groundwater infiltration. No consensus exists on what WBS normalization parameters result in the strongest correlations and lead time between SARS-CoV-2 WBS data and COVID-19 cases. This study compared flow, population size and biomarker normalization impacts on the correlations and lead times for ten communities in twelve sewersheds in Alberta (Canada) between September 2020 and October 2021 (n = 1024) to determine if normalization by Pepper Mild Mottle Virus (PMMoV) provides any advantages compared to other normalization parameters (e.g., flow, reported and dynamic population sizes, BOD, TSS, NH3, TP). PMMoV concentrations (GC/mL) corresponded with plant influent flows and were highest in the urban centres. SARS-CoV-2 target genes E, N1 and N2 were all negatively associated with wastewater influent pH, while PMMoV was positively associated with temperature. Pooled data analysis showed that normalization increased ρ-values by almost 0.1 and was highest for ammonia, TKN and TP followed by PMMoV. Normalization by other parameters weakened associations. None of the differences were statistically significant. Site-specific correlations showed that normalization of SARS-CoV-2 data by PMMoV only improved correlations significantly in two of the twelve systems; neither were large sewersheds or combined sewer systems. In five systems, normalization by traditional wastewater strength parameters and dynamic population estimates improved correlations. Lead time ranged between 1 and 4 days in both pooled and site-specific comparisons. We recommend that WBS researchers and health departments: a) Investigate WWTP influent properties (e.g., pH) in the WBS planning phase and use at least two parallel approaches for normalization only if shown to provide value; b) Explore normalization by wastewater strength parameters and dynamic population size estimates further; and c) Evaluate purchasing an influent flow meter in small communities to support long-term WBS efforts and WWTP management., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022. Published by Elsevier B.V.)

Wastewater monitoring of SARS-CoV-2 enables early detection and monitoring of the COVID-19 disease burden in communities and can track specific variants of concern. We determined proportions of the Omicron and Delta variants across 30 municipalities covering >75% of the province of Alberta (population 4.5 million), Canada, during November 2021-January 2022. Larger cities Calgary and Edmonton exhibited more rapid emergence of Omicron than did smaller and more remote municipalities. Notable exceptions were Banff, a small international resort town, and Fort McMurray, a medium-sized northern community that has many workers who fly in and out regularly. The integrated wastewater signal revealed that the Omicron variant represented close to 100% of SARS-CoV-2 burden by late December, before the peak in newly diagnosed clinical cases throughout Alberta in mid-January. These findings demonstrate that wastewater monitoring offers early and reliable population-level results for establishing the extent and spread of SARS-CoV-2 variants.

The COVID-19 pandemic has stimulated wastewater-based surveillance, allowing public health to track the epidemic by monitoring the concentration of the genetic fingerprints of SARS-CoV-2 shed in wastewater by infected individuals. Wastewater-based surveillance for COVID-19 is still in its infancy. In particular, the quantitative link between clinical cases observed through traditional surveillance and the signals from viral concentrations in wastewater is still developing and hampers interpretation of the data and actionable public-health decisions. We present a modelling framework that includes both SARS-CoV-2 transmission at the population level and the fate of SARS-CoV-2 RNA particles in the sewage system after faecal shedding by infected persons in the population. Using our mechanistic representation of the combined clinical/wastewater system, we perform exploratory simulations to quantify the effect of surveillance effectiveness, public-health interventions and vaccination on the discordance between clinical and wastewater signals. We also apply our model to surveillance data from three Canadian cities to provide wastewater-informed estimates for the actual prevalence, the effective reproduction number and incidence forecasts. We find that wastewater-based surveillance, paired with this model, can complement clinical surveillance by supporting the estimation of key epidemiological metrics and hence better triangulate the state of an epidemic using this alternative data source., (Crown Copyright © 2022. Published by Elsevier B.V. All rights reserved.)

Wastewater surveillance of SARS-CoV-2 has become a promising tool to estimate population-level changes in community infections and the prevalence of COVID-19 disease. Although many studies have reported the detection and quantification of SARS-CoV-2 in wastewater, remarkable variation remains in the methodology. In this study, we validated a molecular testing method by concentrating viruses from wastewater using ultrafiltration and detecting SARS-CoV-2 using one-step RT-qPCR assay. The following parameters were optimized including sample storage condition, wastewater pH, RNA extraction and RT-qPCR assay by quantification of SARS-CoV-2 or spiked human coronavirus strain 229E (hCoV-229E). Wastewater samples stored at 4 °C after collection showed significantly enhanced detection of SARS-CoV-2 with approximately 2-3 PCR-cycle threshold (Ct) values less when compared to samples stored at -20 °C. Pre-adjustment of the wastewater pH to 9.6 to aid virus desorption followed by pH readjustment to neutral after solid removal significantly increased the recovery of spiked hCoV-229E. Of the five commercially available RNA isolation kits evaluated, the MagMAX-96 viral RNA isolation kit showed the best recovery of hCoV-229E (50.1 ± 20.1%). Compared with two-step RT-qPCR, one-step RT-qPCR improved sensitivity for SARS-CoV-2 detection. Salmon DNA was included for monitoring PCR inhibition and pepper mild mottle virus (PMMoV), a fecal indicator indigenous to wastewater, was used to normalize SARS-CoV-2 levels in wastewater. Our method for molecular detection of SARS-CoV-2 in wastewater provides a useful tool for public health surveillance of COVID-19., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2021 The Authors. Published by Elsevier B.V. All rights reserved.)

Introduction: The COVID-19 pandemic has an excessive impact on residents in long-term care facilities (LTCF), causing high morbidity and mortality. Early detection of presymptomatic and asymptomatic COVID-19 cases supports the timely implementation of effective outbreak control measures but repetitive screening of residents and staff incurs costs and discomfort. Administration of vaccines is key to controlling the pandemic but the robustness and longevity of the antibody response, correlation of neutralising antibodies with commercial antibody assays, and the efficacy of current vaccines for emerging COVID-19 variants require further study. We propose to monitor SARS-CoV-2 in site-specific sewage as an early warning system for COVID-19 in LTCF and to study the immune response of the staff and residents in LTCF to COVID-19 vaccines., Methods and Analysis: The study includes two parts: (1) detection and quantification of SARS-CoV-2 in LTCF site-specific sewage samples using a molecular assay followed by notification of Public Health within 24 hours as an early warning system for appropriate outbreak investigation and control measures and cost-benefit analyses of the system and (2) testing for SARS-CoV-2 antibodies among staff and residents in LTCF at various time points before and after COVID-19 vaccination using commercial assays and neutralising antibody testing performed at a reference laboratory., Ethics and Dissemination: Ethics approval was obtained from the University of Alberta Health Research Ethics Board with considerations to minimise risk and discomforts for the participants. Early recognition of a COVID-19 case in an LTCF might prevent further transmission in residents and staff. There was no direct benefit identified to the participants of the immunity study. Anticipated dissemination of information includes a summary report to the immunity study participants, sharing of study data with the scientific community through the Canadian COVID-19 Immunity Task Force, and prompt dissemination of study results in meeting abstracts and manuscripts in peer-reviewed journals., Competing Interests: Competing interests: SD acts as a content expert for Roche on Arbovirus and respiratory viruses for Johnson and Johnson (Janssen)—companies not involved with this study., (© Author(s) (or their employer(s)) 2021. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.)