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.

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.

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.

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.

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.

Rectal microbiota diversity in Kenyan MSM is inversely associated with frequency of receptive anal sex, independent of HIV status.

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.

A Comparison of Real-Time Polymerase Chain Reaction Assays for the Detection of Antimicrobial Resistance Markers and Sequence Typing From Clinical Nucleic Acid Amplification Test Samples and Matched Neisseria gonorrhoeae Culture.

Real-time polymerase chain reaction (PCR) assays to detect antimicrobial resistance-associated mutations were tested on Neisseria gonorrhoeae-positive clinical samples with matched isolates. Of the nucleic acid amplification tests/cultures, 87.7% (64/73), 98.6% (72/73), and 98.4% (62/63) predicted cephalosporin, ciprofloxacin, and azithromycin susceptibilities, respectively. N. gonorrhoeae multiantigen sequence type was correctly predicted for 98.7% (79/80), and 13 of 58 N. gonorrhoeae-negative specimens showed false-positive results.

A Study of the Infant Nasal Microbiome Development over the First Year of Life and in Relation to Their Primary Adult Caregivers Using cpn60 Universal Target (UT) as a Phylogenetic Marker.

Whereas the infant gut microbiome is the subject of intense study, relatively little is known regarding the nares microbiome in newborns and during early life. This study aimed to survey the typical composition and diversity of human anterior nare microflora for developing infants over time, and to explore how these correlate to their primary caregivers. Single nare swabs were collected at five time points over a one-year period for each subject from infant-caregiver pairs. Our study comprised of 50 infants (recruited at 2 weeks, post delivery) and their 50 primary caregivers. Applying the chaperonin-60 (cpn60) universal target (UT) amplicon as our molecular barcoding marker to census survey the microbial communities, we longitudinally surveyed infant nares microbiota at 5 time points over the course of the first year of life. The inter- and intra-subject diversity was catalogued and compared, both longitudinally and relative to their adult primary caregivers. Although within-subject variability over time and inter-subject variability were both observed, the assessment detected only one or two predominant genera for individual infant samples, belonging mainly to phyla Actinobacteria, Firmicutes, and Proteobacteria. Consistent with previously observed microbial population dynamics in other body sites, the diversity of nares microflora increased over the first year of life and infants showed differential operational taxonomic units (OTUs) relative to their matched primary caregiver. The collected evidence also support that both temporal and seasonal changes occur with respect to carriage of potentially pathogenic bacteria (PPBs), which may influence host predisposition to infection. This pilot study surveying paired infant/caregiver nare microbiomes provides novel longitudinal diversity information that is pertinent to better understanding nare microbiome development in infants.