Predicting COVID-19 cases across a large university campus using complementary built environment and wastewater surveillance approaches (preprint)

Background: Environmental surveillance of SARS-CoV-2 via wastewater has become an invaluable tool for population-level surveillance. Built environment sampling may provide complementary spatially-refined detection for viral surveillance in congregate settings such as universities. Methods: We conducted a prospective environmental surveillance study at the University of Ottawa between September 2021 and April 2022. Floor surface samples were collected twice weekly from six university buildings. Samples were analyzed for the presence of SARS-CoV-2 using RT-qPCR. A Poisson regression was used to model the campus-wide COVID-19 cases predicted from the fraction of floor swabs positive for SARS-CoV-2 RNA, building CO 2 levels, Wi-Fi usage, and SARS-CoV-2 RNA levels in regional wastewater. We used a mixed-effects Poisson regression analysis to model building-level cases using viral copies detected in floor samples as a predictor. A random intercepts logistic regression model tested whether floor samples collected in high-traffic areas were more likely to have SARS-CoV-2 present than low-traffic areas. Results: Over the 32-week study period, we collected 554 floor swabs at six university buildings. Overall, 13% of swabs were PCR-positive for SARS-CoV-2, with positivity ranging between 4.8% and 32.7% among university buildings. Both floor swab positivity (Spearman r = 0.74, 95% CI: 0.53-0.87) and regional wastewater signal (Spearman r = 0.50, 95% CI: 0.18-0.73) were positively correlated with on-campus COVID-19 cases. In addition, built environment detection was a predictor of cases linked to individual university buildings (IR log10(copies) + 1 = 17, 95% CI: 7-44). There was no significant difference in detection between floors sampled in high-traffic versus low-traffic areas (OR = 1.3, 95% CI: 0.8-2.1). Conclusions: Detection of SARS-CoV-2 RNA on floors and viral RNA levels found in wastewater were strongly associated with the incidence of COVID-19 cases on a university campus. These data suggest a potential role for institutional built environment sampling, used together with wastewater surveillance, for predicting COVID-19 cases at both campus-wide and building level scales.

Detection of SARS-CoV-2 in Schools Using Built Environment Testing in Ottawa, Canada: A Multi-Facility Prospective Surveillance Study (preprint)

Classroom and staffroom floor swabs across six elementary schools in Ottawa, Canada were tested for SARS-CoV-2. Schools in neighbourhoods with historically elevated COVID-19 burden had lower environmental swab positivity. Environmental test positivity did not correlate with student grade groups, school-level absenteeism, pediatric COVID-19-related hospitalizations, or community SARS-CoV-2 wastewater levels.

Detection of COVID-19 Outbreaks in Long-Term Care Homes Using Built Environment Testing for SARS-CoV-2: A Multicentre Prospective Study (preprint)

Background Environmental surveillance of SARS-CoV-2 via wastewater has become an invaluable tool for population-level surveillance of COVID-19. Built environment sampling may provide a more spatially refined approach for surveillance of COVID-19 in congregate living settings and other high risk settings (e.g., schools, daycares). Methods We conducted a prospective study in 10 long-term care homes (LTCHs) across three cities in Ontario, Canada between September 2021 and May 2022. Floor surfaces were sampled weekly at multiple locations (range 10 to 24 swabs per building) within each building and analyzed for the presence of SARS-CoV-2 using RT-qPCR. The exposure variable was detection of SARS-CoV-2 on floors. The primary outcome was the presence of a COVID-19 outbreak in the week that floor sampling was performed. Results Over the 9-month study period, we collected 3848 swabs at 10 long-term care homes. During the study period, 19 COVID-19 outbreaks occurred with 103 cumulative weeks under outbreak. During outbreak periods, the proportion of floor swabs positive for SARS-CoV-2 was 50% (95% CI: 47-53) with a median quantification cycle of 37.3 (IQR 35.2-38.7). During non-outbreak periods the proportion of floor swabs positive was 18% (95% CI:17-20) with a median quantification cycle of 38.0 (IQR 36.4-39.1). Using the proportion of positive floor swabs for SARS-CoV-2 to predict COVID-19 outbreak status in a given week, the area under the receiver operating curve (AUROC) was 0.85 (95% CI: 0.78-0.92). Using thresholds of [≥]10%, [≥]30%, and [≥]50% of floor swabs positive for SARS-CoV-2 yielded positive predictive values for outbreak of 0.57 (0.49-0.66), 0.73 (0.63-0.81), and 0.73 (0.6-0.83) respectively and negative predictive values of 0.94 (0.87-0.97), 0.85 (0.78-0.9), and 0.75 (0.68-0.81) respectively. Among 8 LTCHs with an outbreak and swabs performed in the antecedent week, 5 had positive floor swabs exceeding 10% at least five days prior to outbreak identification. For 3 of these 5 LTCHs, positivity of floor swabs exceeded 10% more than 10 days before the outbreak being identified. Conclusions Detection of SARS-CoV-2 on floors is strongly associated with COVID-19 outbreaks in LTCHs. These data suggest a potential role for floor sampling in improving early outbreak identification.

Effects of socio-economic factors on elementary school student COVID-19 infections in Ontario, Canada (preprint)

Background: The prevalence of SARS-CoV-2 infections in Ontario is disproportionately concentrated in areas with lower-income and racialized groups. We examined whether school-level and area-level socio-economic factors were associated with elementary school student infections in Ontario. Methods: We performed multi-level modeling analyses using data from the Ministry of Education on school-based infections in Ontario in the 2020-21 school year and on school-level demographics, the Ontario Marginalization Index, and census data to estimate the variability of the cumulative incidence of SARS-CoV-2 infections amongst elementary school students attributable to individual schools (school level, Level 1) and forward sortation areas (FSAs) of schools (area level, Level 2). We explored whether socio-economic factors within individual schools and/or factors common to schools within FSAs predicted the incidence of elementary school student infections. Results: At the school level, the proportion of students from low-income households within a school was positively related with the cumulative incidence of SARS-CoV-2 elementary school student infections ({beta}=.083,p<0.001). At the area level, the dimensions of FSA marginalization were significantly related with cumulative incidence. Ethnic concentration ({beta}=.454,p<0.001), residential instability ({beta}=.356,p<0.001), and material deprivation ({beta}=.212,p<0.001) were positively related. Area-related variables were more likely to explain variance in cumulative incidence than school-related variables (58% versus 1%, respectively). Interpretation: Socio-economic characteristics of the geographic location of schools were more important in determining the cumulative incidence of SARS-CoV-2 elementary school student infections than individual school characteristics. Given inequitable effects of protracted education disruption, schools in marginalized areas should be prioritized for infection prevention measures and education continuity and recovery plans.

Background incidence rates of adverse events of special interest related to COVID-19 vaccines in Ontario, Canada, 2015 to 2020, to inform COVID-19 vaccine safety surveillance (preprint)

Background: Background incidence rates are critical in pharmacovigilance to facilitate identification of vaccine safety signals. We estimated background incidence rates of nine adverse events of special interest related to COVID-19 vaccines in Ontario, Canada. Methods: We conducted a population-based retrospective observational study using linked health administrative databases for hospitalizations and emergency department visits among Ontario residents. We estimated incidence rates of Bells palsy, idiopathic thrombocytopenia, febrile convulsions, acute disseminated encephalomyelitis, myocarditis, pericarditis, Kawasaki disease, Guillain-Barre syndrome, and transverse myelitis during five pre-pandemic years (2015-2019) and 2020. Results: The average annual population was 14 million across all age groups with 51% female. The pre-pandemic mean annual rates per 100,000 population during 2015-2019 were 43.9 for idiopathic thrombocytopenia, 27.8 for Bells palsy, 25.0 for febrile convulsions, 22.8 for acute disseminated encephalomyelitis, 11.3 for myocarditis/pericarditis, 8.6 for pericarditis, 2.9 for myocarditis, 1.9 for Guillain-Barre syndrome, 1.7 for transverse myelitis, and 1.6 for Kawasaki disease. Females had higher rates of acute disseminated encephalomyelitis and transverse myelitis while males had higher rates of myocarditis, pericarditis, and Guillain-Barre syndrome. Bells palsy, acute disseminated encephalomyelitis, and Guillain-Barre syndrome increased with age. The mean rates of myocarditis and/or pericarditis increased with age up to 79 years; males had higher rates than females: from 12-59 years for myocarditis and 12 years and older for pericarditis. Febrile convulsions and Kawasaki disease were predominantly childhood diseases and generally decreased with age. Conclusions: Our estimated background rates will permit estimating numbers of expected events for these conditions and facilitate detection of potential safety signals following COVID-19 vaccination.

Predicting SARS-CoV-2 infections for children and youth with single symptom screening (preprint)

Symptom-based SARS-CoV-2 screening and testing decisions in children have important implications on daycare and school exclusion policies. Single symptoms account for a substantial volume of testing and disruption to in-person learning and childcare, yet their predictive value is unclear, given the clinical overlap with other circulating respiratory viruses and non-infectious etiologies. We aimed to determine the relative frequency and predictive value of single symptoms for paediatric SARS-CoV-2 infections from an Ottawa COVID-19 assessment centre from October 2020 through April 2021. Overall, 46.3% (n=10,688) of pediatric encounters were for single symptoms, and 2.7% of these tested positive. The most common presenting single symptoms were rhinorrhea (31.8%), cough (17.4%) and fever (14.0%). Among children with high-risk exposures children in each age group, the following single symptoms had a higher proportion of positive SARS-CoV-2 cases compared to no symptoms; fever and fatigue (0-4 years); fever, cough, headache, and rhinorrhea (5-12 years); fever, loss of taste or smell, headache, rhinorrhea, sore throat, and cough (13-17 years). There was no evidence that the single symptom of either rhinorrhea or cough predicted SARS-CoV-2 infections among 0-4 year olds, despite accounting for a large volume (61.1%) of single symptom presentations in the absence of high-risk exposures. Symptom-based screening needs to be responsive to changes in evidence and local factors, including the expected resurgence of other respiratory viruses following relaxation of social distancing/masking, to reduce infection-related risks in schools and daycare settings.