Towards efficient and targeted sampling of primary respiratory diseases from wastewater in congregate settings for seniors: Empowering high-risk demographics with prospective health threat data
Bibliographic record
Abstract
Respiratory disease outbreaks with overlapping symptomology in long-term care and congregate living facilities can have disproportionately negative impacts on the health and well-being of residents. Wastewater surveillance of SARS-CoV-2 demonstrated efficacy as an early outbreak warning for congregate facilities allowing for the implementation of effective non-pharmaceutical interventions. Assays that concomitantly target multiple respiratory pathogens exist for clinical diagnosis; however, challenges remain in the implementation of similar multi-pathogen surveillance from wastewater in terms of specificity, sensitivity and connections to clinical data. Herein, RT-qPCR multiplex assays were developed, combining detection of SARS-CoV-2, influenza and respiratory syncytial virus (RSV) into a single assay, reducing time and cost per sample. Data were analyzed in context of single pathogen detection sensitivity and known outbreaks at 1 long-term care facility, 4 retirement homes and 1 community site in Peterborough, ON, Canada. Analyses focused on 8 outbreak periods (SARS-CoV-2 (6); influenza (1); RSV (1)), 2 suspected influenza outbreaks, and parallel respiratory outbreaks. Wastewater signals for pathogens correlated with reported outbreak periods at facilities, while relative sensitivity was reduced, multiplex assay results had comparable signal trends to that of single pathogen assays. Among SARS-CoV-2 outbreaks, wastewater signals were detected ∼ 3-4 days prior to outbreaks. For influenza and RSV outbreaks, consistent wastewater signals were detected 3 and 12 days prior, respectively. A multiplexed assay approach allowed for identification of parallel respiratory pathogen outbreaks with overlapping symptomology. These findings support wastewater surveillance and efficiencies of multiplexing respiratory virus detection without losing signal detection for ongoing reduced-cost monitoring programs.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".