Weaponizing water as an instrument of war in Syria: Impact on diarrhoeal disease in Idlib and Aleppo governorates, 2011–2019
Bibliographic record
Abstract
OBJECTIVES: Investigate the weaponization of water during the Syrian conflict and the correlation of attacks on water, sanitation, and hygiene (WASH) infrastructure in Idlib and Aleppo governorates with trends in waterborne diseases reported by Early Warning and Response surveillance systems. METHODS: We reviewed literature and databases to obtain information on attacks on WASH in Aleppo and Idlib governorates between 2011 and 2019. We plotted weekly trends in waterborne diseases from two surveillance systems operational in Aleppo and Idlib governorates between 2015 and early 2020. RESULTS: The literature review noted several attacks on water and related infrastructure in both governorates, suggesting that WASH infrastructure was weaponized by state and non-state actors. Most interference with WASH in the Aleppo governorate occurred before 2019 and in the Idlib governorate in the summer of 2020. Other acute diarrhea represented >90% of cases of diarrhea; children under 5 years contributed 50% of cases. There was substantial evidence (p < 0.001) of an overall upward trend in cases of diarrheal disease. CONCLUSIONS: Though no direct correlation can be drawn between the weaponization of WASH and the burden of waterborne infections due to multiple confounders, this research introduces important concepts on attacks on WASH and their potential impacts on waterborne diseases.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".