Wastewater-based epidemiology: current uses and future opportunities as a public health surveillance tool
Bibliographic record
Abstract
Wastewater-based epidemiology (WBE) seeks to use biological or chemical indicators in sewage to provide information on the overall health of a community. This paper provides an overview of the range of applications of WBE over the past two decades, how it has been used to inform public health responses, and considerations for more integrated approaches to WBE based on a review of the literature. The review finds that WBE has been used extensively around the world for the estimation of consumption patterns of illicit drugs and other substances, but a range of novel applications also exist. As a result of the COVID-19 pandemic, many communities used WBE for the first time as a complementary public health surveillance tool, monitoring trends in SARS-CoV-2 prevalence in large cities, and for micro-surveillance on a more targeted level. WBE may continue to be a useful public health surveillance tool in the future; however, several limitations and challenges exist. Consideration of how information obtained through WBE can be used to inform public health responses is essential to understanding the potential costs and benefits compared with conventional public health surveillance techniques.
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 | 0.009 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".