Passive sampling for genomic surveillance of SARS-CoV-2 in wastewater resource recovery facility: Insights for pandemic preparedness
Bibliographic record
Abstract
Passive sampling provides a cost-effective alternative to conventional auto-sampling and serves as a valuable approach for wastewater surveillance in resource-limited settings. However, the feasibility of passive sampling for SARS-CoV-2 wastewater genomic surveillance (WWGS) remains underexplored in wastewater resource recovery facility (WWRF). In this study, we collected influent wastewater samples using an autosampler, COSCa-ball and Torpedo passive samplers from inlet of WWRF serving the city of Ottawa, Canada. We enriched, extracted, quantified, and sequenced samples targeting SARS-CoV-2 genome using a widely used ARTIC tiled amplicon approach. Our findings show that daily SARS-CoV-2 RNA levels were similar (p > 0.05) across the sampling methods. Although similar viral RNA was captured from passive samples, we found genomic recovery of SARS-CoV-2 from passive samplers was influenced by targeted sequencing read length, with shorter (300 bp) reads resulting in lower recovery than longer (600 bp) reads. Our study confirmed near-complete recovery of SARS-CoV-2 genomes (≥ 90 %) from the autosampler, COSCa-ball, and Torpedo samplers using longer reads. Genome sequencing parameters such as the number of raw reads, trimmed reads, mapped reads, depth of coverage and % of genome coverage was identical (p > 0.05) among the sampling methods. Genomic analyses showed similar (p > 0.05) single nucleotide variant profiles (SNV) and lineage prevalence across sampling methods, and concordance with the available clinical surveillance. Overall, the findings suggest that passive sampling of wastewater is a viable, cost-effective alternative for population-scale genomic surveillance of SARS-CoV-2 and may allow for surveillance of other pathogens, supporting future pandemic preparedness efforts.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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".