A Review of Outbreaks of Waterborne Disease Associated with Ships: Evidence for Risk Management
Bibliographic record
Abstract
OBJECTIVE: The organization of water supply to and on ships differs considerably from that of water supply on land. Risks of contamination can arise from source water at the port or during loading, storage, or distribution on the ship. The purpose of this article is to review documented outbreaks of waterborne diseases associated with passenger, cargo, fishing, and naval ships to identify contributing factors so that similar outbreaks can be prevented in the future. METHODS: The authors reviewed 21 reported outbreaks of waterborne diseases associated with ships. For each outbreak, data on pathogens/toxins, type of ship, factors contributing to outbreaks, mortality and morbidity, and remedial action are presented. RESULTS: The findings of this review show that the majority of reported outbreaks were associated with passenger ships and that more than 6,400 people were affected. Waterborne outbreaks due to Enterotoxigenic Escherichia coli, noroviruses, Salmonella spp, Shigella sp, Cryptosporidium sp, and Giardia lamblia occurred on ships. Enterotoxigenic E. coli was the pathogen most frequently associated with outbreaks. One outbreak of chemical water poisoning also occurred on a ship. Risk factors included contaminated port water, inadequate treatment, improper loading techniques, poor design and maintenance of storage tanks, ingress of contamination during repair and maintenance, cross-connections, back siphonage, and insufficient residual disinfectant. CONCLUSIONS: Waterborne disease outbreaks on ships can be prevented. The factors contributing to outbreaks emphasize the need for hygienic handling of water along the supply chain from source to consumption. A comprehensive approach to water safety on ships is essential. This may be achieved by the adoption of Water Safety Plans that cover design, construction, operation, and routine inspection and maintenance.
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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.009 | 0.012 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".