A scoping review of human pathogens detected in untreated human wastewater and sludge
Bibliographic record
Abstract
Wastewater monitoring is an approach to identify the presence or abundance of pathogens within a population. The objective of this scoping review (ScR) was to identify and characterize research on human pathogens and antimicrobial resistance detected in untreated human wastewater and sludge. A search was conducted up to March 2023 and standard ScR methodology was followed. This ScR included 1,722 articles, of which 56.5% were published after the emergence of COVID-19. Viruses and bacteria were commonly investigated, while research on protozoa, helminths, and fungi was infrequent. Articles prior to 2019 were dominated by research on pathogens transmitted through fecal-oral or waterborne pathways, whereas more recent articles have explored the detection of pathogens transmitted through other pathways such as respiratory and vector-borne. There was variation in sampling, samples, and sample processing across studies. The current evidence suggests that wastewater monitoring could be applied to a range of pathogens as a public health tool to detect an emerging pathogen and understand the burden and spread of disease to inform decision-making. Further development and refinement of the methods to identify and interpret wastewater signals for different prioritized pathogens are needed to develop standards on when, why, and how to monitor effectively.
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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 | 0.006 | 0.021 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.014 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".