Literature Review of Associations among Attributes of Reported Drinking Water Disease Outbreaks
Bibliographic record
Abstract
Waterborne disease outbreaks attributed to various pathogens and drinking water system characteristics have adversely affected public health worldwide throughout recorded history. Data from drinking water disease outbreak (DWDO) reports of widely varying breadth and depth were synthesized to investigate associations between outbreak attributes and human health impacts. Among 1519 outbreaks described in 475 sources identified during review of the primarily peer-reviewed, English language literature, most occurred in the U.S., the U.K. and Canada (in descending order). The outbreaks are most frequently associated with pathogens of unknown etiology, groundwater and untreated systems, and catchment realm-associated deficiencies (i.e., contamination events). Relative frequencies of outbreaks by various attributes are comparable with those within other DWDO reviews, with water system size and treatment type likely driving most of the (often statistically-significant at p < 0.05) differences in outbreak frequency, case count and attack rate. Temporal analysis suggests that while implementation of surface (drinking) water management policies is associated with decreased disease burden, further strengthening of related policies is needed to address the remaining burden attributed to catchment and distribution realm-associated deficiencies and to groundwater viral and disinfection-only system outbreaks.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 teacher head, 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".