Syndromic Surveillance of Communicable Diseases in Mobile Clinics During the Arbaeenia Mass Gathering in Wassit Governorate, Iraq, in 2014: Cross-Sectional Study
Bibliographic record
Abstract
BACKGROUND: Arbaeenia is the largest religious mass gathering organized annually in Karbala city, Iraq, and is attended by 8-14 million people. Outbreaks of communicable diseases are a significant risk due to overcrowding and potential food and water contamination. Syndromic surveillance is often used for rapid detection and response to disease outbreaks. OBJECTIVE: This study was conducted to identify the main communicable diseases syndromes among pilgrims during the Arbaeenia mass gathering in Wassit governorate, Iraq, in 2014. METHODS: This cross-sectional study was conducted in the 40 mobile clinics established within Wassit governorates along the road to Karbala during the Arbaeenia mass gathering. Six communicable disease syndromes were selected: acute watery diarrhea, bloody diarrhea, fever and cough, vomiting with or without diarrhea, fever and bleeding tendency, and fever and rash. A simple questionnaire was used to directly gather basic demographics and the syndromic diagnosis from the attendees. RESULTS: A total of 87,865 patients attended the clinics during the 10-day period, with an average of 219 patients/clinic/day. Approximately 5% (3999) of the attendees had communicable diseases syndromes: of these, 1693 (42%) had fever and cough, 1144 (29%) had acute diarrhea, 1062 (27%) presented with vomiting with/without diarrhea, and 100 (2%) had bloody diarrhea. The distribution of the syndromes did not vary by age or gender. Stool specimen cultures for Vibrio cholerae performed for 120 patients with acute diarrhea were all negative. CONCLUSIONS: Syndromic surveillance was useful in determining the main communicable diseases encountered during the mass gathering. Expansion of this surveillance to other governorates and the use of mobile technology can help in timely detection and response to communicable disease outbreaks.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".