Overview of microbial risks in water distribution networks and their health consequences: quantification, modelling, trends, and future implications
Bibliographic record
Abstract
The water distribution network (WDN) is usually the final physical barrier preventing contamination of the drinking water before it reaches consumers. Because the WDN is at the end of the supply chain, and often with limited online water quality monitoring, the probability of an incident to be detected and remediated in time is low. Microbial risks that can affect the distribution network are: intrusion, cross-connections and backflows, inadequate management of reservoirs, improper main pipe repair and (or) maintenance work, and biofilms. Epidemiological investigations have proven that these risks have been sources of waterborne outbreaks. Increasingly since the 1990s, studies have also indicated that the contribution of these risks to the endemic level of disease is not negligible. To address the increasing health risks associated to WDNs, researchers have developed tools for risk quantification and risk management. This review aims to present the recent advancements in the field involving epidemiological investigations, use of quantitative microbial risk assessment (QMRA) for modelling, risk mitigation, and decision-support. Increasing the awareness of the progress achieved, but also of the limitations and challenges faced, will aid in accelerating the implementation of QMRA tools for WDN risk management and as a decision-support tool.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".