A systematic review and meta-analysis of the effects of extreme weather events and other weather-related variables on Cryptosporidium and Giardia in fresh surface waters
Bibliographic record
Abstract
Global climate change is expected to impact drinking water quality through multiple weather-related phenomena. We conducted a systematic review and meta-analysis of the relationship between various weather-related variables and the occurrence and concentration of Cryptosporidium and Giardia in fresh surface waters. We implemented a comprehensive search in four databases, screened 1,228 unique citations for relevance, extracted data from 107 relevant articles, and conducted random-effects meta-analysis on 16 key relationships. The average odds of identifying Cryptosporidium oocysts and Giardia cysts in fresh surface waters was 2.61 (95% CI = 1.63-4.21; I² = 16%) and 2.87 (95% CI = 1.76-4.67; I² = 0%) times higher, respectively, during and after extreme weather events compared to baseline conditions. Similarly, the average concentration of Cryptosporidium and Giardia identified under these conditions was also higher, by approximately 4.38 oocysts/100 L (95% CI = 2.01-9.54; I(2) = 0%) and 2.68 cysts/100 L (95% CI = 1.08-6.55; I² = 48%). Correlation relationships between other weather-related parameters and the density of these pathogens were frequently heterogeneous and indicated low to moderate effects. Meta-regression analyses identified different study-level factors that influenced the variability in these relationships. The results can be used as direct inputs for quantitative microbial risk assessment. Future research is warranted to investigate these effects and potential mitigation strategies in different settings and contexts.
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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 | 0.014 | 0.040 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.027 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".