Implications of SARS‐CoV‐2 on current and future operation and management of wastewater systems
Bibliographic record
Abstract
While researchers have acknowledged the potential role of environmental scientists, engineers, and industrial hygienists during this pandemic, the role of the water utility professional is often overlooked. The wastewater sector is critical to public health protection and employs collection and treatment system workers who perform tasks with high potential for exposures to biological agents. While various technical guidances and reports have initially provided direction to the water sector, the rapidly growing body of research publications necessitates the constant review of these papers and data synthesis. This paper presents the latest findings and highlights their implications from a water and wastewater utility operation and management perspective. PRACTITIONER POINTS: Extrapolation from SARS-CoV-1 and MERS-CoV, as well as other surrogates, has helped predicting SARS-CoV-2 behavior and risk management. Data from treated wastewater effluent suggest that current processes are sufficient for SARS-CoV-2 control. Scientific evidence supports the possibility of fecal-oral transmission for SARS-CoV-2. Limited evidence supports the potential survival of infective SARS-CoV-2 on surfaces and in aerosols and the efficacy of control measures at reducing transmission. Protective practices and PPE can protect workers from SARS-CoV-2 and other pathogens found in wastewater.
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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".