Campus node-based wastewater surveillance enables COVID-19 case localization and confirms lower SARS-CoV-2 burden relative to the surrounding community.

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.

Wastewater-based surveillance can be used to model COVID-19-associated workforce absenteeism.

Wastewater-based surveillance (WBS) of infectious diseases is a powerful tool for understanding community COVID-19 disease burden and informing public health policy. The potential of WBS for understanding COVID-19's impact in non-healthcare settings has not been explored to the same degree. Here we examined how SARS-CoV-2 measured from municipal wastewater treatment plants (WWTPs) correlates with workforce absenteeism. SARS-CoV-2 RNA N1 and N2 were quantified three times per week by RT-qPCR in samples collected at three WWTPs servicing Calgary and surrounding areas, Canada (1.4 million residents) between June 2020 and March 2022. Wastewater trends were compared to workforce absenteeism using data from the largest employer in the city (>15,000 staff). Absences were classified as being COVID-19-related, COVID-19-confirmed, and unrelated to COVID-19. Poisson regression was performed to generate a prediction model for COVID-19 absenteeism based on wastewater data. SARS-CoV-2 RNA was detected in 95.5 % (85/89) of weeks assessed. During this period 6592 COVID-19-related absences (1896 confirmed) and 4524 unrelated absences COVID-19 cases were recorded. A generalized linear regression using a Poisson distribution was performed to predict COVID-19-confirmed absences out of the total number of absent employees using wastewater data as a leading indicator (P < 0.0001). The Poisson regression with wastewater as a one-week leading signal has an Akaike information criterion (AIC) of 858, compared to a null model (excluding wastewater predictor) with an AIC of 1895. The likelihood-ratio test comparing the model with wastewater signal with the null model shows statistical significance (P < 0.0001). We also assessed the variation of predictions when the regression model was applied to new data, with the predicted values and corresponding confidence intervals closely tracking actual absenteeism data. Wastewater-based surveillance has the potential to be used by employers to anticipate workforce requirements and optimize human resource allocation in response to trackable respiratory illnesses like COVID-19.

Surveillance for SARS-CoV-2 and its variants in wastewater of tertiary care hospitals correlates with increasing case burden and outbreaks.

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.

Tracking Emergence and Spread of SARS-CoV-2 Omicron Variant in Large and Small Communities by Wastewater Monitoring in Alberta, Canada.

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.

Longitudinal SARS-CoV-2 RNA wastewater monitoring across a range of scales correlates with total and regional COVID-19 burden in a well-defined urban population.

Wastewater-based epidemiology (WBE) is an emerging surveillance tool that has been used to monitor the ongoing COVID-19 pandemic by tracking SARS-CoV-2 RNA shed into wastewater. WBE was performed to monitor the occurrence and spread of SARS-CoV-2 from three wastewater treatment plants (WWTP) and six neighborhoods in the city of Calgary, Canada (population 1.44 million). A total of 222 WWTP and 192 neighborhood samples were collected from June 2020 to May 2021, encompassing the end of the first-wave (June 2020), the second-wave (November end to December 2020) and the third-wave of the COVID-19 pandemic (mid-April to May 2021). Flow-weighted 24-hour composite samples were processed to extract RNA that was then analyzed for two SARS-CoV-2-specific regions of the nucleocapsid gene, N1 and N2, using reverse transcription-quantitative polymerase chain reaction (RT-qPCR). Using this approach SARS-CoV-2 RNA was detected in 98.06% (406/414) of wastewater samples. SARS-CoV-2 RNA abundance was compared to clinically diagnosed COVID-19 cases organized by the three-digit postal code of affected individuals' primary residences, enabling correlation analysis at neighborhood, WWTP and city-wide scales. Strong correlations were observed between N1 & N2 gene signals in wastewater and new daily cases for WWTPs and neighborhoods. Similarly, when flow rates at Calgary's three WWTPs were used to normalize observed concentrations of SARS-CoV-2 RNA and combine them into a city-wide signal, this was strongly correlated with regionally diagnosed COVID-19 cases and clinical test percent positivity rate. Linked census data demonstrated disproportionate SARS-CoV-2 in wastewater from areas of the city with lower socioeconomic status and more racialized communities. WBE across a range of urban scales was demonstrated to be an effective mechanism of COVID-19 surveillance.

Real Time Spectroscopic Ellipsometry Analysis of First Stage CuIn1-xGaxSe2 Growth: Indium-Gallium Selenide Co-Evaporation.

Real time spectroscopic ellipsometry (RTSE) has been applied for in-situ monitoring of the first stage of copper indium-gallium diselenide (CIGS) thin film deposition by the three-stage co-evaporation process used for fabrication of high efficiency thin film photovoltaic (PV) devices. The first stage entails the growth of indium-gallium selenide (In1-xGax)2Se3 (IGS) on a substrate of Mo-coated soda lime glass maintained at a temperature of 400 °C. This is a critical stage of CIGS deposition because a large fraction of the final film thickness is deposited, and as a result precise compositional control is desired in order to achieve the optimum performance of the resulting CIGS solar cell. RTSE is sensitive to monolayer level film growth processes and can provide accurate measurements of bulk and surface roughness layer thicknesses. These in turn enable accurate measurements of the bulk layer optical response in the form of the complex dielectric function ε = ε1 - iε2, spectra. Here, RTSE has been used to obtain the (ε1, ε2) spectra at the measurement temperature of 400 °C for IGS thin films of different Ga contents (x) deduced from different ranges of accumulated bulk layer thickness during the deposition process. Applying an analytical expression in common for each of the (ε1, ε2) spectra of these IGS films, oscillator parameters have been obtained in the best fits and these parameters in turn have been fitted with polynomials in x. From the resulting database of polynomial coefficients, the (ε1, ε2) spectra can be generated for any composition of IGS from the single parameter, x. The results have served as an RTSE fingerprint for IGS composition and have provided further structural information beyond simply thicknesses, for example information related to film density and grain size. The deduced IGS structural evolution and the (ε1, ε2) spectra have been interpreted as well in relation to observations from scanning electron microscopy, X-ray diffractometry and energy-dispersive X-ray spectroscopy profiling analyses. Overall the structural, optical and compositional analysis possible by RTSE has assisted in understanding the growth and properties of three stage CIGS absorbers for solar cells and shows future promise for enhancing cell performance through monitoring and control.