Cholera in the time of war: implications of weak surveillance in Syria for the WHO's preparedness—a comparison of two monitoring systems
Bibliographic record
Abstract
BACKGROUND: Public health breakdown from the Syrian government's targeting of healthcare systems in politically unsympathetic areas has yielded a resurgence of infectious diseases. Suspected cholera recently reappeared but conflict-related constraints impede laboratory confirmation. Given the government's previous under-reporting of infectious outbreaks and the reliance of the WHO on government reporting, we sought to assess the reliability of current surveillance systems. METHODS: We compared weekly surveillance reports of waterborne diseases from the Syrian government's (WHO-associated) Early Warning and Response System (EWARS), based in Damascus, and the independent, non-governmental Early Warning and Response Network (EWARN) headquartered in Gaziantep, Turkey. We compared raw case rates by EWARS and EWARN and assessed the quality of reporting against the WHO benchmarks. RESULTS: We identified significant under-reporting and delays in the government's surveillance. On average, EWARS reports were published 24 days (range 12-61) after the reference week compared with 11 days (5-21) for EWARN. Average completeness for EWARS was 75% (55-84%), compared with 92% for EWARN (85-99%). Average timeliness for EWARS was 79% (51-100%), compared with 88% for EWARN (70-97%). EWARS made limited use of rapid diagnostic tests, and rates of collection of stool samples for laboratory cholera testing were well below reference levels. CONCLUSIONS: In the context of the current Syrian war, the government's surveillance is inadequate due to lack of access to non-government held territory, an incentive to under-report the consequence of government attacks on health infrastructure, and an impractical insistence on laboratory confirmation. These findings should guide the WHO reform for surveillance in conflict zones.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".