ABSTRACT
Background. Universities are interactive communities where frequent contacts between individuals occur, increasing the risk of outbreaks of COVID-19. We embarked upon a real-time wastewater (WW) monitoring program across the University of Calgary (UofC) campus measuring WW SARS-CoV-2 burden relative to levels of disease in the broader surrounding community. Figure 1 The colour scheme shows 6 sewer sub-catchments at the University of Calgary. Auto samplers were deployed at 4 sampling nodes within sub-catchments CR and YA (both residence halls), and UCE and UCS (catchments that include several campus buildings). Figure 2 Log10-transformed abundance (i.e., copies per mL) of nucleocapsid gene (i.e., N1) for SARS-CoV-2 for each sampling location during October 2021 - April 2022. Locations denoted by the same letters (A, B, or C) show no statistical difference (p > 0.05) according to the Wilcoxon rank-sum test. The WWTP sample corresponds to a catchment area covering most of Calgary including the university campus, for which sampling locations CR, UCE, UCS, and UCW are defined in Fig. 1. Methods. From October 2021 - April 2022, WW was collected thrice weekly across UofC campus through 4 individual sewer sampling nodes (Fig. 1) using autosamplers (C.E.C. Analytics, CA). Results from these 4 nodes were compared with community monitoring at Calgary's largest WW treatment plant (WWTP), which received WW from surrounding neighborhoods, and also from UofC. Nucleic acid was extracted from WW for RTqPCR quantification of the N1 nucleocapside gene from SARS-CoV-2 genomic RNA. Qualitative (positive samples defined if cycle threshold < 40) and quantitative statistical analyses were performed using R. Results. Levels of SARS-CoV-2 in WW were significantly lower at all campus monitoring sites relative to the WWTP (Wilcoxon rank-sum test p < 0.05;Fig. 2). The proportion of WW samples that were positive for SARS-CoV-2 was significantly higher for WWTP than at least two campus locations (p < 0.05 for Crowsnest Hall and UCE - University way and campus drive) according to Fischer's exact 2-sided test. The proportion of WW samples with positive WW signals were still higher for WWTP than the other two locations, but statistically not significant (p = 0.216). Among campus locations, the buildings in UCE catchment showed much lower N1 signals than other catchments, likely owing to buildings in this catchment primarily being administration and classroom environments, with lower human-to-human contact and less defecation compared to the other 3 catchments, which include residence hall, a dining area, and/or laboratory spaces. Conclusion. Our results show that SARS-CoV-2 RNA shedding in WW at the U of C is significantly lower than the city-wide signal associated with surrounding neighborhoods. Furthermore, we demonstrate that WW testing at well-defined nodes is a sampling strategy for potentially locating specific places where high transmission of infectious disease occurs.
ABSTRACT
Background. We sought to compareWWSARS-CoV-2 RNA detection across a range of sites and scales using RTqPCR and RTddPCR. Figure. Methods. Composite-24hWW was collected from aWWtreatment plant (WTP;n=18), a neighborhood (Nb1;n=12) and three hospitals;H-1, H-2, and H-3 (3-sites;A-C)(n=84). RNA was extracted using the 4S-silica column method. RTqPCR (QuantStudio5, Thermo Fisher) and RTddPCR (C1000 Thermal Cycler and QX200 Droplet Reader, BioRad) quantified SARS-CoV-2 RNA nucleocapsid (N2, US CDC) and envelope (E Sarbeco, Corman et al 2020) in triplicate. Fisher's exact test was used to compare assay sensitivity. Correlations between modalities and RNA - clinically-confirmed COVID-19 cases (defined by postal code of primary residence using 5-day rolling average) was assessed using Persons correlation. Results. 114 samples were tested (02/23/2021-04/22/2021). SARS-CoV-2-N2 was identified in 90/114 (79%) by RTqPCR and 89/114 (78%) by ddPCR (p=1). SARS-CoV-2 E was found in 72/114 (63%) by RTqPCR and 90/114 (79%) by ddPCR, p=0.01. Correlations between modalities were strongest for N2 relative to E across all sites (see Table). N2 correlated with clinically diagnosed cases for both modalities greater at the level of the WTP (RTqPCR;r=0.8972, p< 0.0001and ddPCR;0.933, p< 0.0001) relative to neighborhood (RTqPCR;r=0.6, p=0.04 and ddPCR;0.60, p=0.04). E correlated to a lesser degree with cases at WTP (RTqPCR;r=0.65, p=0.0035 and ddPCR;0.88, p=< 0.001) and neighborhoods (RTqPCR;r=0.40, p=0.20 and ddPCR;r=0.43, p=0.16). Conclusion. SARS-CoV-2 detection of N2 was similar between RTqPCR and RTddPCR across a range of sites and scales in the sewershed, and this correlated best with clinical cases whereas E detection was superior with ddPCR.
ABSTRACT
Background. WW surveillance enables real time monitoring of SARS-CoV-2 burden in defined sewer catchment areas. Here, we assessed the occurrence of total, Delta and Omicron SARS-CoV-2 RNA in sewage from three tertiary-care hospitals in Calgary, Canada. Methods. Nucleic acid was extracted from hospital (H) WW using the 4S-silica column method. H-1 and H-2 were assessed via a single autosampler whereas H-3 required three separate monitoring devices (a-c). SARS-CoV-2 RNA was quantified using two RT-qPCR approaches targeting the nucleocapsid gene;N1 and N200 assays, and the R203K/G204R and R203M mutations. Assays were positive if Cq< 40. Cross-correlation function analyses (CCF) was performed to determine the timelagged relationships betweenWWsignal and clinical cases. SARS-CoV-2 RNA abundance was compared to total hospitalized cases, nosocomial-acquired cases, and outbreaks. Statistical analyses were conducted using R. Results. Ninety-six percent (188/196) of WW samples collected between Aug/ 21-Jan/22 were positive for SARS-CoV-2. Omicron rapidly supplanted Delta by mid-December and this correlated with lack of Delta-associated H-transmissions during a period of frequent outbreaks. The CCF analysis showed a positive autocorrelation between the RNA concentration and total cases, where the most dominant cross correlations occurred between -3 and 0 lags (weeks) (Cross-correlation values: 0.75, 0.579, 0.608, 0.528 and 0.746 for H-1, H-2, H-3a, H-3b and H-3c;respectively). VOC-specific assessments showed this positive association only to hold true for Omicron across all hospitals (cross-correlation occurred at lags -2 and 0, CFF value range between 0.648 -0.984). We observed a significant difference in median copies/ ml SARS-CoV-2 N-1 between outbreak-free periods vs outbreaks for H-1 (46 [IQR: 11-150] vs 742 [IQR: 162-1176], P< 0.0001), H-2 (24 [IQR: 6-167] vs 214 [IQR: 57-560], P=0.009) and H-3c (2.32 [IQR: 0-19] vs 129 [IQR: 14-274], P=0.001). Conclusion. WWsurveillance is a powerful tool for early detection andmonitoring of circulating SARS-CoV-2VOCs.Total SARS-CoV-2 andVOC-specificWWsignal correlated with hospitalized prevalent cases of COVID-19 and outbreak occurrence.
ABSTRACT
Background: Nursing home (NH) populations are at higher risk for morbidity and mortality due to COVID-19. A March 2020 NH survey indicated improvements in pandemic planning when compared to a similar survey in 2007. We surveyed NHs to evaluate how well pandemic preparedness plans and infection prevention strategies met the reality of COVID-19. Methods: The first COVID-19 case in Michigan was reported March 10, 2020. In the setting of 46,088 cases and 4,327 deaths statewide as of May 1, we disseminated an online survey to state department-registered NHs to describe their experience of the initial pandemic wave. Responses were collected May 1-12, during which the state averaged 585 cases/day. We were particularly interested in NH preparedness, challenges, testing capacity, and adaptations made. Results: Of 452 NHs contacted, 145 opened the survey and 143 (32%) responded. A majority (68%) indicated that their facility's pandemic response plan addressed > 90% of issues they experienced;29% reported their plan addressed most but not all anticipated concerns (Table 1). As the pandemic evolved, all facilities (100%) provided additional staff education on proper personal protective equipment (PPE) use. 66% reported experiencing shortages of PPE and other supplies. Half of all facilities (50%) lacked sufficient resources to test asymptomatic residents or staff;only 36% were able to test all residents and staff with suspected COVID-19 infection. Half (52%) considered their communication regarding COVID-19 with nearby hospitals “very good.” The majority of facilities (55%) experienced staffing shortages, often relying on remaining staff to work additional hours and/or contracted staff to fill deficits (Table 2). NH staff resignations increased, with 63% of NHs experiencing resignations;staff with greater bedside contact were more likely to leave, including nurses and nurse assistants. Conclusion: While most NHs had a plan to respond to COVID-19 pandemic in March 2020, many facilities experienced a lack of available resources, less than ideal communication lines with local hospitals, lack of testing capacity and insufficient staff. These shortcomings indicate potential high-yield areas of improvement in pandemic preparedness in the NH setting. (Table Presented).