2303. Detection of COVID-19 Outbreaks in Hospitals Using Built Environment Testing for SARS-CoV-2
Bibliographic record
Abstract
Abstract Background Environmental testing for SARS-CoV-2 including assessment of wastewater and the built environment is a useful tool for population-level surveillance for COVID-19. Detection of SARS-CoV-2 on the floor of healthcare facilities has been strongly associated with cases of COVID-19 (1,2). By introducing routine floor-swabbing in hospitals, we may be able to predict outbreaks earlier allowing for additional control measures. Methods We implemented floor swabbing surveillance for SARS-CoV-2 to aid the identification of COVID-19 cases and outbreaks in hospitals. Swabs were taken weekly at eight hospital in-patient wards in healthcare worker-only (HCW) areas at two hospitals in Ontario, Canada, for a 39-week period (July 2022 to March 2023). HCW cases and outbreaks were managed as per usual processes at the facilities. A logistic regression model with ward-level random intercepts was developed using weekly viral copies (VC) to predict a contemporaneous outbreak in the same ward in the same week. Grouped 5-fold cross-validation was used to evaluate model outbreak discrimination. Results SARS-CoV-2 RNA was detected on 537 of 760 collected swabs (71%). Hospital A had more frequent detection and higher levels of SARS-CoV-2 (swab positivity = 90% [95% CI: 85%-93%], mean VC = 23, [19-29]) than Hospital B (swab positivity = 60% [55%-64%], mean VC = 7.9 [6.5-9.7]) (Figure 1). There were seven outbreaks at Hospital A and four at Hospital B. Outbreaks at both hospitals consisted of mostly patient cases (Hospital A: 95%, Hospital B: 82%). The odds ratio of outbreak for every unit increase in viral copies (log-transformed) was 21.0 [5.6-79]. The cross-validated area under the receiver operating curve for SARS-CoV-2 viral copies for predicting a contemporaneous outbreak (Figure 2) was 0.86 [95%CI 0.82 – 0.9]. Figure 1. Distribution of SARS-CoV-2 copies (plus one) values from PCR testing of floor swabs, stratified by hospital and outbreak status at time of sampling. Jittered points show the copies plus one values for each individual swab; boxplots show the median, IQR, and range of these values by site (indicated by colors), in outbreak and non-outbreak periods (indicated on y-axis). Figure 2. Cross-validation receiver operating characteristic (ROC) curves (with mean ROC in blue) for predicting contemporaneous outbreaks from SARS-CoV-2 viral copies. Conclusion Detection of SARS-CoV-2 on floors in HCW-only areas is associated with COVID-19 outbreaks in those hospital wards. Despite swabbing exclusively in HCW-only areas, outbreaks were driven by patient cases at both hospitals. These results support the potential role for built environment sampling to support hospital COVID-19 outbreak identification and may fill gaps in traditional clinical surveillance methods. Disclosures Evgueni Doukhanine, MSc, DNA Genotek: DNA Genotek provided sampling swabs in-kind for this study in an unrestricted fashion. Michael Fralick, MD, ProofDx: Advisor/Consultant
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".