Municipal Wastewater Surveillance Revealed a High Community Disease Burden of a Rarely Reported and Possibly Subclinical <i>Salmonella enterica</i> Serovar Derby Strain
Bibliographic record
Abstract
Wastewater-based epidemiology (WBE) has been conventionally used to analyze community health via the detection of chemicals, such as legal and illicit drugs; however, municipal wastewater contains microbiological determinants of health and disease as well, including enteric pathogens. Here, we demonstrate that WBE can be used to examine subclinical community salmonellosis patterns. Derby was the most abundant Salmonella serovar detected in Hawaii wastewater over a year-long sampling study, with few corresponding clinical cases. Comparative genomics analyses indicate that the normally rare strain of S . Derby found in wastewater has a unique combination of genes which allow it to persist as a subclinical infection without producing symptoms of severe gastroenteritis. This study shows that WBE can be used to explore trends in community infectious disease patterns which may not be reflected in clinical monitoring, shedding light on overall enteric disease burden and rates of asymptomatic cases.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 |
| 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".