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 (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.

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.

Bloodstream infections are a major cause of morbidity and mortality worldwide. Early administration of appropriate antimicrobial therapy can improve patient survival and prevent antimicrobial resistance (AMR). Whole genome sequencing (WGS) can provide information for pathogen identification, AMR prediction and sequence typing earlier than current phenotypic diagnostic methods.WGS was performed on 97 clinical blood specimens and matched culture isolate pairs. Specimen/isolate pairs were MLST sequence-typed and further characterization was performed on Streptococcus species.WGS correctly identified 91.7% of clinical specimens and 93.2% of matched isolates representing 35 different microbial species. MLST types were assigned for 89.9% of matched cultures and 21.7% of blood specimens, with higher success for blood culture specimens extracted within 3 days (52% characterized) than 7 days (9.3%).This study demonstrates the potential use of WGS for identification and characterization of pathogens directly from blood culture specimens to facilitate timely initiation of appropriate antimicrobial therapies.

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.

Résumé Contexte : Les variants préoccupants du coronavirus 2 du syndrome respiratoire aigu sévère (SRAS-CoV-2) sont associés à une augmentation de l’infectivité, de la gravité et de la mortalité de la maladie à coronavirus 2019 (COVID-19) et sont de plus en plus souvent détectés dans le cadre de la surveillance clinique et de la surveillance des eaux usées au Canada et dans le reste du monde. Dans cette étude, nous présentons un essai de réaction en chaîne quantitative de la polymérase après la transcription inverse en temps réel (RT-qPCR) pour la détection de la mutation D377Y du gène N associée au variant Delta du SRAS-CoV-2 dans les eaux usées. Méthodes : Des échantillons d’eaux usées (n = 980) ont été prélevés dans six villes et 17 collectivités rurales du Canada de juillet à novembre 2021 et ont fait l’objet d’un dépistage de la mutation D377Y. Résultats : Le variant Delta a été détecté dans toutes les grandes villes canadiennes et les régions éloignées du Nord, ainsi que dans la moitié des collectivités rurales du sud. La sensibilité et la spécificité de cet essai étaient suffisantes pour détecter et quantifier le variant Delta dans les eaux usées afin de contribuer à un dépistage et à une surveillance rapides au niveau de la population. Conclusion : Cette étude démontre un nouveau test RT-qPCR rentable pour suivre la

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.

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.

Abstract Wastewater-based surveillance of SARS-CoV-2 RNA has been implemented at building, neighbourhood, and city levels throughout the world. Implementation strategies and analysis methods differ, but they all aim to provide rapid and reliable information about community COVID-19 health states. A viable and sustainable SARS-CoV-2 surveillance network must not only provide reliable and timely information about COVID-19 trends, but also provide for scalability as well as accurate detection of known or unknown emerging variants. Emergence of the SARS-CoV-2 variant of concern Omicron in late Fall 2021 presented an excellent opportunity to benchmark individual and aggregated data outputs of the Ontario Wastewater Surveillance Initiative in Canada; this public health-integrated surveillance network monitors wastewaters from over 10 million people across major population centres of the province. We demonstrate that this coordinated approach provides excellent situational awareness, comparing favourably with traditional clinical surveillance measures. Thus, aggregated datasets compiled from multiple wastewater-based surveillance nodes can provide sufficient sensitivity (i.e., early indication of increasing and decreasing incidence of SARS-CoV-2) and specificity (i.e., allele frequency estimation of emerging variants) with which to make informed public health decisions at regional- and state-levels.

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.

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.

The newly emerging variants of SARS-CoV-2 from South Africa (B.1.351/501Y.V2) and Brazil (P.1/501Y.V3) have led to a higher infection rate and reinfection of COVID-19 patients. We found that the mutations K417N, E484K, and N501Y within the receptor-binding domains (RBDs) of the virus could confer ~2-fold higher binding affinity to the human receptor, angiotensin converting enzyme 2 (ACE2), compared to the wildtype RBD. The mutated version of RBD also completely abolishes the binding of bamlanivimab, a therapeutic antibody, in vitro. Detailed analysis shows that the ~10-fold gain of binding affinity between ACE2 and Y501-RBD, which also exits in the high contagious variant B.1.1.7/501Y.V1 from the United Kingdom, is compromised by additional introduction of the K417/N/T mutation. Mutation of E484K leads to the loss of bamlanivimab binding to RBD, although this mutation does not affect the binding between RBD and ACE2.

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.

Objective: Both HIV infection and identifying as MSM have been linked to altered rectal microbiota composition, but few studies have studied sexual behavioural associations with rectal microbiota within MSM. In addition, most rectal microbiota studies in MSM have been limited geographically to Europe and North America, and replication of findings in lower and middle-income countries is lacking. Design: A cross-sectional study. Methods: We enrolled MSM from Nairobi, Kenya, and determined their HIV/sexually transmitted infection status. Rectal specimens were obtained for 16s rRNA sequencing of the rectal microbiota, and sexual behaviour was characterized using a standardized questionnaire. Microbiome differences were modelled using nonparametric statistics, Bray-Curtis ecological distance metrics and analyses of differential taxa abundance. Multivariable linear regression was used to model HIV status and recent sexual activity as predictors of alpha diversity, controlling for a range of covariates. Results: Alpha diversity was consistently lower in Kenyan HIV-infected MSM (n = 80), including those on antiretroviral therapy (ART) compared with HIV-uninfected MSM. A statistical trend was observed for clustering of HIV status by Prevotella or Bacteroides dominance (P = 0.13). Several taxa were enriched in HIV-positive men, including Roseburia, Lachnospira, Streptococcus and Granulicatella. Receptive anal sex with several types of sexual partners (paying, regular, casual) was associated with lower Chao1 and Simpson diversity, independent of HIV status, while HIV infection was associated lower Chao1 (P = 0.030) but not Simpson diversity (P = 0.49). Conclusion: Both HIV infection and sexual behaviour were associated with rectal microflora alpha diversity, in particular richness, but not Prevotella spp. dominance, in Kenyan MSM. Associations were more robust for sexual behaviour.

BACKGROUND The incidence of antimicrobial-resistant Neisseria gonorrhoeae (GC) is rising in Canada; however, antimicrobial resistance (AMR) surveillance data are unavailable for infections diagnosed directly from clinical specimens by nucleic acid amplification tests (NAATs), representing over 80% of diagnoses. We developed a set of 10 improved molecular assays for surveillance of GC-AMR and prediction of susceptibilities in NAAT specimens. METHODS Multiplex real-time PCR (RT-PCR) assays were developed to detect SNPs associated with cephalosporin (ponA, porB, mtrR -35delA, penA A311V, penA A501, N513Y, G545S), ciprofloxacin (gyrA S91, parC D86/S87/S88) and azithromycin [23S (A2059G, C2611T), mtrR meningitidis-like promoter] resistance. The assays were validated on 127 gonococcal isolates, 51 non-gonococcal isolates and 50 NAATs with matched culture isolates. SNPs determined from the assay were compared with SNPs determined from in silico analysis of WGS data. MICs were determined for culture isolates using the agar dilution method. RESULTS SNP analysis of the 50 NAAT specimens had 96% agreement with the matched culture RT-PCR analysis. When compared with MICs, presence of penA A311V or penA A501 and two or more other SNPs correlated with decreased susceptibility and presence of three or more other SNPs correlated with intermediate susceptibility to cephalosporins; presence of any associated SNP correlated with ciprofloxacin or azithromycin resistance. NAAT-AMR predictions correlated with matched-culture cephalosporin, ciprofloxacin and azithromycin MICs at 94%, 100% and 98%, respectively. CONCLUSIONS We expanded molecular tests for N. gonorrhoeae AMR prediction by adding new loci and multiplexing reactions to improve surveillance where culture isolates are unavailable.

Background Bacterial STIs increase mucosal inflammation and HIV acquisition risk. However, most data are limited to symptomatic STIs, and more data are required regarding the prevalence and correlates of asymptomatic bacterial STIs among high-risk young women, and how these vary by exposure to sex work. Methods We estimated the prevalence of 6 STIs in urine collected during a cross-sectional study of women aged 14–24 years in Mombasa, Kenya (n=870). Participants were recruited from sex work hotspots, and self-identified as engaged or not engaged in formal sex work. STIs including Ureaplasma urealyticum (UU), Chlamydia trachomatis (CT), Mycoplasma hominis (MH), Trichomonas vaginalis (TV), Mycoplasma genitalium (MG) and Neisseria gonorrhea (NG) were detected using the Seegene Anyplex qPCR panel. The cervicovaginal microbiome and cytokines were also characterized in a subset of 168 participants. Results The prevalence of any STI was 25.1% (95% CI, 22.3–28.0%). The most common STIs were UU (13.2%) and MH (11.3%), followed by CT (6.6%), TV (3.2%), NG (0.3%), and MG (0.5%). U. urealyticum was more prevalent among sex workers compared to non-sex workers (19.7 vs 10.4%, p=0.001). Number of partners positively correlated with UU (P=0.004), while duration of sexual activity was positively correlated with MH (P<0.001) and UU (P=0.023). Recent history of STI treatment did not correlate with any STI. MH was most associated with symptoms, including vaginal discharge, itching, and painful urination (P< 0.001). All STIs except CT were positively correlated with pro-inflammatory cytokines. UU and MH were correlated with vaginal microbiota alpha diversity (P<0.001). Conclusion U. urealyticum and M. hominis were the most prevalent STIs among young women from Mombasa, Kenya, and these organisms were associated with sexual exposure and an inflammatory mucosal milieu that has been linked to increased HIV acquisition. Disclosure No significant relationships.