A multicentre study to predict COVID-19 outbreaks in long-term care homes using wastewater surveillance and environmental surface sampling for SARS-CoV-2
Bibliographic record
Abstract
ABSTRACT Background Floor swabs can be an effective environmental sampling method for proactive SARS-CoV-2 surveillance in congregate settings like long-term care homes (LTCHs). Concurrent assessment of additional variables such as wastewater surveillance data and weather data have the potential to improve the predictive performance of this approach. Methods We analyzed existing data from 5,095 floor swabs collected between August 2021 and January 2023 from 10 LTCHs across three cities in Ontario, Canada: Ottawa, Toronto, and Sault Ste. Marie. Floors were swabbed weekly at each LTCH. Swabs were analyzed using RT-qPCR. Wastewater data was obtained from the Ontario Wastewater Surveillance Consortium’s repository; we included one treatment plant for each city. Weather data was sourced from Environment Canada, with one station selected from each city. Logistic regression, LASSO-penalized logistic regression, Random Forest, and XGBoost were used for COVID-19 outbreak predictions using different subsets of predictors with leave-one-LTCH-out cross-validation. SHAP values were computed for model explainability. Our outcome of interest was a COVID-19 outbreak within an LTCH. Results Over the study period, 25 COVID-19 outbreaks occurred in the participating LTCHs, with a median duration of 30 days and a median of 39 cases per outbreak (range 2 to 196). LASSO generally out-performed logistic regression, Random Forest, and XGBoost. The two variables with the highest SHAP values were log transformed 7-day mean wastewater and log viral copies from floor swabs. Conclusions Incorporating wastewater data and weather data enhanced the ability of floor swab results to predict an outbreak of COVID-19 in an LTCH. Future studies are needed to evaluate how well the model performs when implemented into practice.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".