Qualitative and quantitative detection of SARS-CoV-2 RNA in untreated wastewater in Western Cape Province, South Africa
Bibliographic record
Abstract
Recent studies have shown that the detection of SARS-CoV-2 genetic material in wastewater may provide the basis for a surveillance system to track the environmental dissemination of this virus in communities. An effective wastewater-based epidemiology (WBE) system may prove critical in South Africa (SA), where health systems infrastructure, testing capacity, personal protective equipment and human resource capacity are constrained. In this proof-of-concept study, we investigated the potential of SARS-CoV-2 RNA surveillance in untreated wastewater as the basis for a system to monitor COVID-19 prevalence in the population, an early warning system for increased transmission, and a monitoring system to assess the effectiveness of interventions. The laboratory confirmed the presence (qualitative analysis) and determined the RNA copy number of SARS-CoV-2 viral RNA by reverse transcription polymerase chain reaction (quantitative) analysis from 24-hour composite samples collected on 18 June 2020 from five wastewater treatment plants in Western Cape Province, SA. The study has shown that a WBE system for monitoring the status and trends of COVID-19 mass infection in SA is viable, and its development and implementation may facilitate the rapid identification of hotspots for evidence-informed interventions.
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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 | 0.001 |
| 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".