Predicting COVID-19 cases across a large university campus using built environment surveillance Article Swipe
Aaron Hinz
,
Jason Moggridge
,
Hanna Ke
,
Alexandra M.A. Hicks
,
Evgueni Doukhanine
,
Michael Fralick
,
Laura Hug
,
Derek R. MacFadden
,
Hebah Mejbel
,
Caroline Nott
,
Ashley Raudanskis
,
Nisha Thampi
,
Alex Wong
,
Rees Kassen
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.3138/jammi-2024-0002
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.3138/jammi-2024-0002
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 and analyzed for the presence of SARS-CoV-2 using quantitative PCR. 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 city wastewater. Building-level cases were modelled using viral copies detected in floor samples as a predictor. 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 buildings. Both floor swab positivity (Spearman r = 0.74, 95% CI 0.53 to 0.87) and city wastewater signal (Spearman r = 0.50, 95% CI 0.18 to 0.73) positively correlated with on-campus COVID-19 cases. In addition, built environment detection was a predictor of cases linked to individual university buildings. Conclusions: Detection of SARS-CoV-2 RNA on floors and viral RNA levels in city-wide 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.
Related Topics
Concepts
Coronavirus disease 2019 (COVID-19)
Poisson regression
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)
2019-20 coronavirus outbreak
University campus
Incidence (geometry)
Wastewater
Population
Environmental health
Veterinary medicine
Medicine
Environmental science
Environmental engineering
Virology
Internal medicine
Engineering
Mathematics
Civil engineering
Outbreak
Geometry
Infectious disease (medical specialty)
Disease
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3138/jammi-2024-0002
- OA Status
- green
- References
- 28
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4405567902
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4405567902Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.3138/jammi-2024-0002Digital Object Identifier
- Title
-
Predicting COVID-19 cases across a large university campus using built environment surveillanceWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2024Year of publication
- Publication date
-
2024-12-19Full publication date if available
- Authors
-
Aaron Hinz, Jason Moggridge, Hanna Ke, Alexandra M.A. Hicks, Evgueni Doukhanine, Michael Fralick, Laura Hug, Derek R. MacFadden, Hebah Mejbel, Caroline Nott, Ashley Raudanskis, Nisha Thampi, Alex Wong, Rees KassenList of authors in order
- Landing page
-
https://doi.org/10.3138/jammi-2024-0002Publisher landing page
- Open access
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YesWhether a free full text is available
- OA status
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greenOpen access status per OpenAlex
- OA URL
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https://www.ncbi.nlm.nih.gov/pmc/articles/12258632Direct OA link when available
- Concepts
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Coronavirus disease 2019 (COVID-19), Poisson regression, Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), 2019-20 coronavirus outbreak, University campus, Incidence (geometry), Wastewater, Population, Environmental health, Veterinary medicine, Medicine, Environmental science, Environmental engineering, Virology, Internal medicine, Engineering, Mathematics, Civil engineering, Outbreak, Geometry, Infectious disease (medical specialty), DiseaseTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
0Total citation count in OpenAlex
- References (count)
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28Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.4.8% | 150 |
| abstract_inverted_index.Both | 155 |
| abstract_inverted_index.Over | 123 |
| abstract_inverted_index.PCR. | 72 |
| abstract_inverted_index.RNA, | 94 |
| abstract_inverted_index.both | 250 |
| abstract_inverted_index.city | 106, 169 |
| abstract_inverted_index.data | 230 |
| abstract_inverted_index.from | 59, 85 |
| abstract_inverted_index.role | 234 |
| abstract_inverted_index.such | 30 |
| abstract_inverted_index.swab | 157 |
| abstract_inverted_index.tool | 11 |
| abstract_inverted_index.used | 77, 240 |
| abstract_inverted_index.were | 55, 110, 141, 216 |
| abstract_inverted_index.with | 146, 183, 219, 242 |
| abstract_inverted_index.0.50, | 175 |
| abstract_inverted_index.0.73) | 180 |
| abstract_inverted_index.0.74, | 162 |
| abstract_inverted_index.0.87) | 167 |
| abstract_inverted_index.2022. | 51 |
| abstract_inverted_index.32.7% | 152 |
| abstract_inverted_index.April | 50 |
| abstract_inverted_index.Built | 15 |
| abstract_inverted_index.Floor | 52 |
| abstract_inverted_index.These | 229 |
| abstract_inverted_index.Wi-Fi | 99 |
| abstract_inverted_index.among | 153 |
| abstract_inverted_index.built | 189, 237 |
| abstract_inverted_index.cases | 83, 109, 196, 224, 248 |
| abstract_inverted_index.floor | 89, 117, 131, 156 |
| abstract_inverted_index.model | 79 |
| abstract_inverted_index.study | 40, 126 |
| abstract_inverted_index.swabs | 90, 132, 140 |
| abstract_inverted_index.twice | 57 |
| abstract_inverted_index.using | 70, 112 |
| abstract_inverted_index.viral | 25, 113, 210 |
| abstract_inverted_index.Ottawa | 45 |
| abstract_inverted_index.become | 8 |
| abstract_inverted_index.cases. | 186 |
| abstract_inverted_index.copies | 114 |
| abstract_inverted_index.floors | 208 |
| abstract_inverted_index.levels | 104, 212 |
| abstract_inverted_index.linked | 197 |
| abstract_inverted_index.signal | 171 |
| abstract_inverted_index.usage, | 100 |
| abstract_inverted_index.weekly | 58 |
| abstract_inverted_index.32-week | 125 |
| abstract_inverted_index.Poisson | 74 |
| abstract_inverted_index.between | 46, 149 |
| abstract_inverted_index.campus. | 228 |
| abstract_inverted_index.levels, | 98 |
| abstract_inverted_index.period, | 127 |
| abstract_inverted_index.provide | 19 |
| abstract_inverted_index.ranging | 148 |
| abstract_inverted_index.refined | 22 |
| abstract_inverted_index.samples | 54, 118 |
| abstract_inverted_index.scales. | 254 |
| abstract_inverted_index.suggest | 231 |
| abstract_inverted_index.surface | 53 |
| abstract_inverted_index.COVID-19 | 82, 185, 223, 247 |
| abstract_inverted_index.Methods: | 33 |
| abstract_inverted_index.Overall, | 137 |
| abstract_inverted_index.Results: | 122 |
| abstract_inverted_index.analyzed | 64 |
| abstract_inverted_index.building | 95 |
| abstract_inverted_index.detected | 115 |
| abstract_inverted_index.fraction | 87 |
| abstract_inverted_index.modelled | 111 |
| abstract_inverted_index.positive | 91, 143 |
| abstract_inverted_index.presence | 67 |
| abstract_inverted_index.sampling | 17 |
| abstract_inverted_index.settings | 29 |
| abstract_inverted_index.strongly | 217 |
| abstract_inverted_index.together | 241 |
| abstract_inverted_index.(Spearman | 159, 172 |
| abstract_inverted_index.Detection | 203 |
| abstract_inverted_index.September | 47 |
| abstract_inverted_index.addition, | 188 |
| abstract_inverted_index.buildings | 62 |
| abstract_inverted_index.city-wide | 214 |
| abstract_inverted_index.collected | 56, 129 |
| abstract_inverted_index.conducted | 35 |
| abstract_inverted_index.detection | 23, 191 |
| abstract_inverted_index.incidence | 221 |
| abstract_inverted_index.on-campus | 184 |
| abstract_inverted_index.potential | 233 |
| abstract_inverted_index.predicted | 84 |
| abstract_inverted_index.predictor | 194 |
| abstract_inverted_index.sampling, | 239 |
| abstract_inverted_index.spatially | 21 |
| abstract_inverted_index.SARS-CoV-2 | 4, 69, 93, 102, 205 |
| abstract_inverted_index.University | 43 |
| abstract_inverted_index.associated | 218 |
| abstract_inverted_index.buildings. | 136, 154, 201 |
| abstract_inverted_index.congregate | 28 |
| abstract_inverted_index.correlated | 182 |
| abstract_inverted_index.individual | 199 |
| abstract_inverted_index.invaluable | 10 |
| abstract_inverted_index.positively | 181 |
| abstract_inverted_index.positivity | 147, 158 |
| abstract_inverted_index.predicting | 246 |
| abstract_inverted_index.predictor. | 121 |
| abstract_inverted_index.regression | 75 |
| abstract_inverted_index.university | 61, 135, 200, 227 |
| abstract_inverted_index.wastewater | 6, 170, 215, 243 |
| abstract_inverted_index.Background: | 0 |
| abstract_inverted_index.SARS-CoV-2, | 145 |
| abstract_inverted_index.campus-wide | 81, 251 |
| abstract_inverted_index.environment | 16, 190, 238 |
| abstract_inverted_index.prospective | 37 |
| abstract_inverted_index.wastewater. | 107 |
| abstract_inverted_index.Conclusions: | 202 |
| abstract_inverted_index.quantitative | 71 |
| abstract_inverted_index.surveillance | 2, 26, 39 |
| abstract_inverted_index.Environmental | 1 |
| abstract_inverted_index.complementary | 20 |
| abstract_inverted_index.environmental | 38 |
| abstract_inverted_index.institutional | 236 |
| abstract_inverted_index.surveillance, | 244 |
| abstract_inverted_index.surveillance. | 14 |
| abstract_inverted_index.universities. | 32 |
| abstract_inverted_index.Building-level | 108 |
| abstract_inverted_index.building-level | 253 |
| abstract_inverted_index.population-level | 13 |
| cited_by_percentile_year | |
| countries_distinct_count | 1 |
| institutions_distinct_count | 14 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/6 |
| sustainable_development_goals[0].score | 0.8100000023841858 |
| sustainable_development_goals[0].display_name | Clean water and sanitation |
| citation_normalized_percentile.value | 0.34318693 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |