Monitoring waves of the COVID-19 pandemic: Inferences from WWTPs of different sizes
Bibliographic record
Abstract
Wastewater based epidemiology was employed to track the spread of SARS-CoV-2 within the sewershed areas of 10 wastewater treatment plants (WWTPs) in Catalonia, Spain. A total of 185 WWTPs inflow samples were collected over the period consisting of both the first wave (mid-March to June) and the second wave (July to November). Concentrations of SARS-CoV-2 RNA (N1 and N2 assays) were quantified in these wastewaters as well as those of Human adenoviruses (HAdV) and JC polyomavirus (JCPyV), as indicators of human faecal contamination. SARS-CoV-2 N gene daily loads strongly correlated with the number of cases diagnosed one week after sampling i.e. wastewater levels were a good predictor of cases to be diagnosed in the immediate future. The conditions present at small WWTPs relative to larger WWTPs influence the ability to follow the pandemic. Small WWTPs (<24,000 inhabitants) had lower median loads of SARS-CoV-2 despite similar incidence of infection within the municipalities served by the different WWTP (but not lower loads of HAdV and JCPyV). The lowest incidence resulting in quantifiable SARS-CoV-2 concentration in wastewater differed between WWTP sizes, being 0.11 and 0.82 cases/1000 inhabitants for the large and small sized WWTP respectively.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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 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".