Detection of mpox virus in wastewater provides forewarning of clinical cases in Canadian cities.

Wastewater-based surveillance (WBS) has shown to be an effective tool in monitoring the spread of SARS-CoV-2 and has helped guide public health actions. Consequently, WBS has expanded to now include the monitoring of mpox virus (MPXV) to contribute to its mitigation efforts. In this study, we demonstrate a unique sample processing and a molecular diagnostic strategy for MPXV detection that can inform on the epidemiological situation of mpox outbreaks through WBS. We conducted WBS for MPXV in 22 Canadian wastewater treatment plants (WWTPs) for 14 weeks. Three MPXV qPCR assays were assessed in this study for the detection of MPXV which include the G2R assays (G2R_WA and G2R_G) developed by the Centers for Disease Control and Prevention (CDC) in 2010, and an in-house-developed assay that we have termed G2R_NML. The G2R_NML assay was designed using reference genomes from the 2022 MPXV outbreak and provides a larger qPCR amplicon size to facilitate Sanger sequencing. Results show that all three assays have similar limits of detection and are able to detect the presence of MPXV in wastewater. The G2R_NML assay produced a significantly greater number of Sanger sequence-confirmed MPXV results compared to the CDC G2R assays. Detection of MPXV was possible where provincial surveillance indicated overall low caseloads, and in some sites forewarning of up to several weeks was observed. Overall, this study proposes that WBS of MPXV provides additional information to help fill knowledge gaps in clinical case-surveillance and is potentially an essential component to the management of mpox.

Preparing for Mpox Resurgence: Surveillance Lessons From Outbreaks in Toronto, Canada.

BACKGROUND: With many global jurisdictions, Toronto, Canada, experienced an mpox outbreak in spring/summer 2022. Cases declined following implementation of a large vaccination campaign. A surge in early 2023 led to speculation that asymptomatic and/or undetected local transmission was occurring in the city. METHODS: Mpox cases and positive laboratory results are reported to Toronto Public Health. Epidemic curves and descriptive risk factor summaries for the 2022 and 2023 outbreaks were generated. First- and second-dose vaccination was monitored. Mpox virus wastewater surveillance and whole genome sequencing were conducted to generate hypotheses about the source of the 2023 resurgence. RESULTS: An overall 515 cases were reported in spring/summer 2022 and 17 in the 2022-2023 resurgence. Wastewater data correlated with the timing of cases. Whole genome sequencing showed that 2022-2023 cases were distinct from 2022 cases and closer to sequences from another country, suggesting a new importation as a source. At the start of the resurgence, approximately 16% of first-dose vaccine recipients had completed their second dose. CONCLUSIONS: This investigation demonstrates the importance of ongoing surveillance and preparedness for mpox outbreaks. Undetected local transmission was not a likely source of the 2022-2023 resurgence. Ongoing preexposure vaccine promotion remains important to mitigate disease burden.

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.

Realizing the value in "non-standard" parts of the qPCR standard curve by integrating fundamentals of quantitative microbiology.

The real-time polymerase chain reaction (PCR), commonly known as quantitative PCR (qPCR), is increasingly common in environmental microbiology applications. During the COVID-19 pandemic, qPCR combined with reverse transcription (RT-qPCR) has been used to detect and quantify SARS-CoV-2 in clinical diagnoses and wastewater monitoring of local trends. Estimation of concentrations using qPCR often features a log-linear standard curve model calibrating quantification cycle (Cq) values obtained from underlying fluorescence measurements to standard concentrations. This process works well at high concentrations within a linear dynamic range but has diminishing reliability at low concentrations because it cannot explain "non-standard" data such as Cq values reflecting increasing variability at low concentrations or non-detects that do not yield Cq values at all. Here, fundamental probabilistic modeling concepts from classical quantitative microbiology were integrated into standard curve modeling approaches by reflecting well-understood mechanisms for random error in microbial data. This work showed that data diverging from the log-linear regression model at low concentrations as well as non-detects can be seamlessly integrated into enhanced standard curve analysis. The newly developed model provides improved representation of standard curve data at low concentrations while converging asymptotically upon conventional log-linear regression at high concentrations and adding no fitting parameters. Such modeling facilitates exploration of the effects of various random error mechanisms in experiments generating standard curve data, enables quantification of uncertainty in standard curve parameters, and is an important step toward quantifying uncertainty in qPCR-based concentration estimates. Improving understanding of the random error in qPCR data and standard curve modeling is especially important when low concentrations are of particular interest and inappropriate analysis can unduly affect interpretation, conclusions regarding lab performance, reported concentration estimates, and associated decision-making.

Impact of education on the knowledge, attitude and practice of disposal of expired and unused medications among pharmacy students.

OBJECTIVE: To assess the impact of education among pharmacy students on their level of knowledge, attitude and practices in the disposal of expired and unused medications. MATERIALS AND METHODS: A pre-test post-test study without control group was conducted in which a total of 573 students pursuing Bachelor of Pharmacy and Doctor of Pharmacy programmes were enrolled. The impact of education provided was analysed with the aid of a previously validated KAP questionnaire. Data on KAP was analysed using SPSS software 24.0. RESULTS AND DISCUSSION: The knowledge, attitude and practice of the total students improved from 74.17±17.063 to 91.06±11.87, 75.92±19.26 to 91.73±12.318 and 43.11±22.77 to 82.06±16.149 respectively. The improvement with respect to knowledge and attitude was significantly high among Doctor of Pharmacy students when compared to Bachelor of Pharmacy, with P value<0.001 for both domains. CONCLUSION: The present study concludes on the effectiveness of educational intervention in generating a positive impact on the student population with respective to adequate disposal practices to be followed for unused and expired medications.

Real-time quantitative reverse transcription polymerase chain reaction detection of SARS-CoV-2 Delta variant in Canadian wastewater.

Background: Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants of concern are associated with increased infectivity, severity, and mortality of coronavirus disease 2019 (COVID-19) and have been increasingly detected in clinical and wastewater surveillance in Canada and internationally. In this study, we present a real-time quantitative reverse transcription polymerase chain reaction (RT-qPCR) assay for detection of the N gene D377Y mutation associated with the SARS-CoV-2 Delta variant in wastewater. Methods: Wastewater samples (n=980) were collected from six cities and 17 rural communities across Canada from July to November 2021 and screened for the D377Y mutation. Results: The Delta variant was detected in all major Canadian cities and northern remote regions, and half of the southern rural communities. The sensitivity and specificity of this assay were sufficient for detection and quantitation of the Delta variant in wastewater to aid in rapid population-level screening and surveillance. Conclusion: This study demonstrates a novel cost-effective RT-qPCR assay for tracking the spread of the SARS-CoV-2 Delta variant. This rapid assay can be easily integrated into current wastewater surveillance programs to aid in population-level variant tracking.

Coupling wastewater-based epidemiological surveillance and modelling of SARS-COV-2/COVID-19: Practical applications at the Public Health Agency of Canada.

Wastewater-based surveillance (WBS) of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) offers a complementary tool for clinical surveillance to detect and monitor coronavirus disease 2019 (COVID-19). Since both symptomatic and asymptomatic individuals infected with SARS-CoV-2 can shed the virus through the fecal route, WBS has the potential to measure community prevalence of COVID-19 without restrictions from healthcare-seeking behaviours and clinical testing capacity. During the Omicron wave, the limited capacity of clinical testing to identify COVID-19 cases in many jurisdictions highlighted the utility of WBS to estimate disease prevalence and inform public health strategies; however, there is a plethora of in-sewage, environmental and laboratory factors that can influence WBS outcomes. The implementation of WBS, therefore, requires a comprehensive framework to outline a pipeline that accounts for these complex and nuanced factors. This article reviews the framework of the national WBS conducted at the Public Health Agency of Canada to present WBS methods used in Canada to track and monitor SARS-CoV-2. In particular, we focus on five Canadian cities-Vancouver, Edmonton, Toronto, Montréal and Halifax-whose wastewater signals are analyzed by a mathematical model to provide case forecasts and reproduction number estimates. The goal of this work is to share our insights on approaches to implement WBS. Importantly, the national WBS system has implications beyond COVID-19, as a similar framework can be applied to monitor other infectious disease pathogens or antimicrobial resistance in the community.

Statistical framework to support the epidemiological interpretation of SARS-CoV-2 concentration in municipal wastewater.

The ribonucleic acid (RNA) of the severe acute respiratory syndrome coronavirus 2 (SARS-Cov-2) is detectable in municipal wastewater as infected individuals can shed the virus in their feces. Viral concentration in wastewater can inform the severity of the COVID-19 pandemic but observations can be noisy and sparse and hence hamper the epidemiological interpretation. Motivated by a Canadian nationwide wastewater surveillance data set, unlike previous studies, we propose a novel Bayesian statistical framework based on the theories of functional data analysis to tackle the challenges embedded in the longitudinal wastewater monitoring data. By employing this framework to analyze the large-scale data set from the nationwide wastewater surveillance program covering 15 sampling sites across Canada, we successfully detect the true trends of viral concentration out of noisy and sparsely observed viral concentrations, and accurately forecast the future trajectory of viral concentrations in wastewater. Along with the excellent performance assessment using simulated data, this study shows that the proposed novel framework is a useful statistical tool and has a significant potential in supporting the epidemiological interpretation of noisy viral concentration measurements from wastewater samples in a real-life setting.

Using historical tropical cyclone climate datasets to examine wind speed recurrence for coastal Australia.

Likelihood estimates of extreme winds, including those from tropical cyclones (TCs) at certain locations are used to inform wind load standards for structural design. Here, wind speed average recurrence intervals (ARIs) determined from TC climate data dating back to the 1970s in two quantile-quantile adjusted reanalysis datasets (ERA5 and BARRA [1990]), and best-track observations for context, were compared with Standardized ARIs (AS/NZS) across seven tropical and two subtropical Australian inland coastal regions. The novelty of this work lies in determining TC-wind speed ARIs from a range of datasets that are not typically used to evaluate this metric. Inherent differences between the data used to determine the Standard ARIs (large sample size allow for larger extrapolations; GEV function) and TC data ARIs (smaller sample size and less certain data; the more asymptotic Lognormal/Weibull functions are used) led to the use of different extreme value functions. Results indicated that although these are two distinct ways of determining design wind speeds, when they are considered equivalent, there was a moderate reproduction of the ARI curves with respect to the Standard in both reanalysis datasets, suggesting that similar analyses using climate model products can provide useful information on these types of metrics with some caveats. Trends in TC wind strength affecting coastal Australia were also analyzed, indicating a potential slight downtrend in tropical West coast TC wind strength and slight uptrend for tropical East coast TC wind strength, noting considerable uncertainty given the short time period and limitations of data quality including over longer time periods. Such trends are not only limited to the relationship between TC intensity and anthropogenic warming, but also to regional changes in TC frequency and track direction. This could lead to significant trends emerging in regional Australian TC wind gust strength before several decades of warming have occurred. It is hoped that climate models can provide both longer-term and a more homogenous base for these types of evaluations and subsequent projections with respect to climate change simulations.

Coupling Wastewater-Based Epidemiological Surveillance and Modelling of SARS-COV-2/COVID-19: Practical Applications at the Public Health Agency of Canada

Wastewater-based surveillance (WBS) of SARS-CoV-2 offers a complementary tool for clinical surveillance to detect and monitor Coronavirus Disease 2019 (COVID-19). Since both symptomatic and asymptomatic individuals infected with SARS-CoV-2 can shed the virus through the fecal route, WBS has the potential to measure community prevalence of COVID-19 without restrictions from healthcare-seeking behaviors and clinical testing capacity. During the Omicron wave, the limited capacity of clinical testing to identify COVID-19 cases in many jurisdictions highlighted the utility of WBS to estimate disease prevalence and inform public health strategies. However, there is a plethora of in-sewage, environmental and laboratory factors that can influence WBS outputs. The implementation of WBS therefore requires a comprehensive framework to outline an analysis pipeline that accounts for these complex and nuanced factors. This article reviews the framework of the national WBS conducted at the Public Health Agency of Canada to present WBS methods used in Canada to track and monitor SARS-CoV-2. In particular, we focus on five Canadian cities - Vancouver, Edmonton, Toronto, Montreal and Halifax - whose wastewater signals are analyzed by a mathematical model to provide case forecasts and reproduction number estimates. This work provides insights on approaches to implement WBS at the national scale in an accurate and efficient manner. Importantly, the national WBS system has implications beyond COVID-19, as a similar framework can be applied to monitor other infectious disease pathogens or antimicrobial resistance in the community.

A wastewater-based epidemic model for SARS-CoV-2 with application to three Canadian cities.

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.

Multiplex RT-qPCR assay (N200) to detect and estimate prevalence of multiple SARS-CoV-2 Variants of Concern in wastewater

Wastewater-based surveillance (WBS) has become an effective tool around the globe for indirect monitoring of COVID-19 in communities. Quantities of viral fragments of SARS-CoV-2 in wastewater are related to numbers of clinical cases of COVID-19 reported within the corresponding sewershed. Variants of Concern (VOCs) have been detected in wastewater by use of reverse transcription quantitative polymerase chain reaction (RT-qPCR) or sequencing. A multiplex RT-qPCR assay to detect and estimate the prevalence of multiple VOCs, including Omicron/Alpha, Beta, Gamma, and Delta, in wastewater RNA extracts was developed and validated. The probe-based multiplex assay, named "N200" focuses on amino acids 199-202, a region of the N gene that contains several mutations that are associated with variants of SARS- CoV-2 within a single amplicon. Each of the probes in the N200 assay are specific to the targeted mutations and worked equally well in single- and multi-plex modes. To estimate prevalence of each VOC, the abundance of the targeted mutation was compared with a non- mutated region within the same amplified region. The N200 assay was applied to monitor frequencies of VOCs in wastewater extracts from six sewersheds in Ontario, Canada collected between December 1, 2021, and January 4, 2022. Using the N200 assay, the replacement of the Delta variant along with the introduction and rapid dominance of the Omicron variant were monitored in near real-time, as they occurred nearly simultaneously at all six locations. The N200 assay is robust and efficient for wastewater surveillance can be adopted into VOC monitoring programs or replace more laborious assays currently being used to monitor SARS- CoV-2 and its VOCs.

A Sensitive and Rapid Wastewater Test for SARS-COV-2 and Its Use for the Early Detection of a Cluster of Cases in a Remote Community.

Throughout the coronavirus disease 2019 (COVID-19) pandemic, wastewater surveillance has been used to monitor trends in severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) prevalence in the community. A major challenge in establishing wastewater surveillance programs, especially in remote areas, is the need for a well-equipped laboratory for sample analysis. Currently, no options exist for rapid, sensitive, mobile, and easy-to-use wastewater tests for SARS-CoV-2. The performance of the GeneXpert system, which offers cartridge-based, rapid molecular clinical testing for SARS-CoV-2 in a portable platform, was evaluated using wastewater as the input. The GeneXpert demonstrated a SARS-CoV-2 limit of detection in wastewater below 32 copies/mL with a sample processing time of less than an hour. Using wastewater samples collected from multiple sites across Canada during February and March 2021, a high overall agreement (97.8%) was observed between the GeneXpert assay and laboratory-developed tests regarding the presence or absence of SARS-CoV-2. Additionally, with the use of centrifugal filters, the detection threshold of the GeneXpert system was improved to <10 copies/mL in wastewater. Finally, to support on-site wastewater surveillance, GeneXpert testing was implemented in Yellowknife, a remote community in Northern Canada, where its use successfully alerted public health authorities to undetected transmission of COVID-19. The identification of SARS-CoV-2 in wastewater triggered clinical testing of recent travelers and identification of new COVID-19 cases/clusters. Taken together, these results suggest that GeneXpert is a viable option for surveillance of SARS-CoV-2 in wastewater in locations that do not have access to established testing laboratories. IMPORTANCE Wastewater-based surveillance is a powerful tool that provides an unbiased measure of COVID-19 prevalence in a community. This work describes a sensitive wastewater rapid test for SARS-CoV-2 based on a widely distributed technology, the GeneXpert. The advantages of an easy-to-use wastewater test for SARS-CoV-2 are clear: it supports surveillance in remote communities, improves access to testing, and provides faster results allowing for an immediate public health response. The application of wastewater rapid testing in a remote community facilitated the detection of a COVID-19 cluster and triggered public health action, clearly demonstrating the utility of this technology. Wastewater surveillance will become increasingly important in the postvaccination pandemic landscape as individuals with asymptomatic/mild infections continue transmitting SARS-CoV-2 but are unlikely to be tested.

RT-qPCR detection of SARS-CoV-2 mutations S 69-70 del, S N501Y and N D3L associated with variants of concern in Canadian wastewater samples.

SARS-CoV-2 variants of concern (VoC) have been increasingly detected in clinical surveillance in Canada and internationally. These VoC are associated with higher transmissibility rates and in some cases, increased mortality. In this work we present a national wastewater survey of the distribution of three SARS-CoV-2 mutations found in the B.1.1.7 (alpha), B.1.351 (beta), and P.1 (gamma) VoC, namely the S-gene 69-70 deletion, N501Y mutation, and N-gene D3L. RT-qPCR allelic discrimination assays were sufficiently sensitive and specific for detection and relative quantitation of SARS-CoV-2 variants in wastewater to allow for rapid population-level screening and surveillance. We tested 261 samples collected from 5 Canadian cities (Vancouver, Edmonton, Toronto, Montreal, and Halifax) and 6 communities in the Northwest Territories from February 16th to March 28th, 2021. VoC were not detected in the Territorial communities, suggesting the absence of VoC SARS-CoV-2 cases in those communities. Percentage of variant remained low throughout the study period in the majority of the sites tested, however the Toronto sites showed a marked increase from ~25% to ~75% over the study period. The results of this study highlight the utility of population level molecular surveillance of SARS-CoV-2 VoC using wastewater. Wastewater monitoring for VoC can be a powerful tool in informing public health responses, including monitoring trends independent of clinical surveillance and providing early warning to communities.

Comparison of approaches to quantify SARS-CoV-2 in wastewater using RT-qPCR: Results and implications from a collaborative inter-laboratory study in Canada.

Detection of SARS-CoV-2 RNA in wastewater is a promising tool for informing public health decisions during the COVID-19 pandemic. However, approaches for its analysis by use of reverse transcription quantitative polymerase chain reaction (RT-qPCR) are still far from standardized globally. To characterize inter- and intra-laboratory variability among results when using various methods deployed across Canada, aliquots from a real wastewater sample were spiked with surrogates of SARS-CoV-2 (gamma-radiation inactivated SARS-CoV-2 and human coronavirus strain 229E [HCoV-229E]) at low and high levels then provided "blind" to eight laboratories. Concentration estimates reported by individual laboratories were consistently within a 1.0-log10 range for aliquots of the same spiked condition. All laboratories distinguished between low- and high-spikes for both surrogates. As expected, greater variability was observed in the results amongst laboratories than within individual laboratories, but SARS-CoV-2 RNA concentration estimates for each spiked condition remained mostly within 1.0-log10 ranges. The no-spike wastewater aliquots provided yielded non-detects or trace levels (<20 gene copies/mL) of SARS-CoV-2 RNA. Detections appear linked to methods that included or focused on the solids fraction of the wastewater matrix and might represent in-situ SARS-CoV-2 to the wastewater sample. HCoV-229E RNA was not detected in the no-spike aliquots. Overall, all methods yielded comparable results at the conditions tested. Partitioning behavior of SARS-CoV-2 and spiked surrogates in wastewater should be considered to evaluate method effectiveness. A consistent method and laboratory to explore wastewater SARS-CoV-2 temporal trends for a given system, with appropriate quality control protocols and documented in adequate detail should succeed.

A sensitive and rapid wastewater test for SARS-COV-2 and its use for the early detection of a cluster of cases in a remote community

Throughout the COVID-19 pandemic, wastewater surveillance has been used to monitor trends in SARS-CoV-2 prevalence in the community. A major challenge in establishing wastewater surveillance programs, especially in remote areas, is the need for a well-equipped laboratory for sample analysis. Currently, no options exist for rapid, sensitive, mobile, and easy-to-use wastewater tests for SARS-CoV-2. The performance of the GeneXpert System, which offers cartridge-based, rapid molecular clinical testing for SARS-CoV-2 in a portable platform, was evaluated using wastewater as the input. The GeneXpert demonstrated a SARS-CoV-2 limit of detection in wastewater below 32 copies/mL with a sample processing time of less than an hour. Using wastewater samples collected from multiple sites across Canada during February and March 2021, a high overall agreement (97.8%) was observed between the GeneXpert assay and laboratory-developed tests regarding the presence or absence of SARS-CoV-2. Additionally, with the use of centrifugal filters the detection threshold of the GeneXpert system was improved to <10 copies/mL in wastewater. Finally, to support on-site wastewater surveillance, GeneXpert testing was implemented in Yellowknife, a remote community in Northern Canada where its use successfully alerted public health authorities to undetected transmission of COVID-19. The identification of SARS-CoV-2 in wastewater triggered clinical testing of recent travelers and identification of new COVID-19 cases/clusters. Taken together, these results suggest the GeneXpert is a viable option for surveillance of SARS-CoV-2 in wastewater in locations that do not have access to established testing laboratories.

A Wastewater-Based Epidemic Model for SARS-CoV-2 with Application to Three Canadian Cities

1The 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.