Climate change and waterborne diseases in temperate regions: a systematic review
Bibliographic record
Abstract
Risk of waterborne diseases (WBDs) persists in temperate regions. The extent of influence of climate-related factors on the risk of specific WBDs in a changing climate and the projections of future climate scenarios on WBDs in temperate regions are unclear. A systematic review was conducted to identify specific waterborne pathogens and diseases prevalent in temperate region literature and transmission cycle associations with a changing climate. Projections of WBD risk based on future climate scenarios and models used to assess future disease risk were identified. Seventy-five peer-reviewed full-text articles for temperate regions published in the English language were included in this review after a search of Scopus and Web of Science databases from 2010 to 2023. Using thematic analysis, climate-related drivers impacting WBD risk were identified. Risk of WBDs was influenced mostly by weather (rainfall: 22% and heavy rainfall: 19%) across the majority of temperate regions and hydrological (streamflow: 50%) factors in Europe. Future climate scenarios suggest that WBD risk is likely to increase in temperate regions. Given the need to understand changes and potential feedback across fate, transport and exposure pathways, more studies should combine data-driven and process-based models to better assess future risks using model simulations
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.006 | 0.008 |
| 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) | 0.005 | 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".