Real-Time Surveillance of Infectious Diseases and Other Health Conditions During Iraq’s Arbaeenia Mass Gathering: Cross-Sectional Study
Bibliographic record
Abstract
BACKGROUND: The most common religious mass gatherings in the Middle East are the Hajj at Mecca in Saudi Arabia, which occurs annually, and the Arbaeenia in Karbala. The importance of developing public health surveillance systems for mass gatherings has been previously emphasized in other reports. OBJECTIVE: This study aimed to describe the common illnesses and health conditions affecting people during the Arbaeenia mass gathering in Iraq in 2016. METHODS: A total of 60 data collectors took part in the field data collection over a period of 11 days, from November 12, 2016 to November 22, 2016. Data were collected from 20 health outlets along the major route from Najaf to Karbala (10 health facilities in each governorate). Two digital forms, the Health Facility Survey and the Case Survey, were used for data collection. RESULTS: A total of 41,689 patients (33.3% female and 66.7% male) visited the 20 health care facilities over a period of 11 days from November 12, 2016 to November 22, 2016. More than three quarters of patients (77.5%; n=32,309) were between 20-59 years of age, more than half of patients were mainly from Iraq (56.5%; n=23,554), and about 38.9% (n=16,217) were from Iran. Patients in this study visited these health care facilities and presented with one or more conditions. Of a total 41,689 patients, 58.5% (n=24,398) had acute or infectious conditions and symptoms, 33.1% (n=13,799) had chronic conditions, 23.9% (n=9974) had traumas or injuries, 28.2% (n=11,762) had joint pain related to walking long distances, and 0.3% (n=133) had chronic dermatologic conditions. CONCLUSIONS: The Arbaeenia mass gathering in 2016 exerted a high burden on the Iraqi health care system. Therefore, efforts must be made both before and during the event to ensure preparedness, proper management, and control of different conditions.
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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) | 0.001 | 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".