Quantitative analysis of SARS-CoV-2 RNA from wastewater solids in communities with low COVID-19 incidence and prevalence
Bibliographic record
Abstract
Abstract In the absence of an effective vaccine to prevent COVID-19 it is important to be able to track community infections to inform public health interventions aimed at reducing the spread and therefore reduce pressures on health-care units, improve health outcomes and reduce economic uncertainty. Wastewater surveillance has rapidly emerged as a potential tool to effectively monitor community infections for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), through measuring trends of viral RNA signal in wastewater systems. In this study SARS-CoV-2 viral RNA N1 and N2 genes are quantified in solids collected from influent post grit solids (PGS) and primary clarified sludge (PCS) in two water resource recovery facilities (WRRF) serving Canada’s national capital region, i.e., the City of Ottawa, ON (pop. ≈ 1.1M) and the City of Gatineau, QC (pop. ≈ 280K). PCS samples show signal inhibition using RT-ddPCR compared to RT-qPCR, with PGS samples showing similar quantifiable concentrations of RNA using both assays. RT-qPCR shows higher frequency of detection of N1 and N2 genes in PCS (92.7, 90.6%) as compared to PGS samples (79.2, 82.3%). Sampling of PCS may therefore be an effective approach for SARS-CoV-2 viral quantification, especially during periods of declining and low COVID-19 incidence in the community. The pepper mild mottle virus (PMMV) is determined to have a less variable RNA signal in PCS over a three month period for two WRRFs, regardless of environmental conditions, compared to Bacteroides 16S rRNA or human eukaryotic 18S rRNA, making PMMV a potentially useful biomarker for normalization of SARS-CoV-2 signal. PMMV-normalized PCS RNA signal from WRRFs of two cities correlated with the regional public health epidemiological metrics, identifying PCS normalized to a fecal indicator (PMMV) as a potentially effective tool for monitoring trends during decreasing and low-incidence of infection of SARS-Cov-2 in communities.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".