Epidemiological Differences in Hajj-Acquired Airborne Infections in Pilgrims Arriving from Low and Middle-Income versus High-Income Countries: A Systematised Review
Bibliographic record
Abstract
This systematised review aims to compare the epidemiological patterns of Hajj-acquired airborne infections among pilgrims from low and middle-income countries (LMIC) versus those from high-income countries (HIC). A PubMed search was carried out for all published articles before February 2023, using a combination of MeSH terms and text words. The Newcastle–Ottawa Scale (NOS) was used to assess data quality. From a total of 453 titles identified, 58 studies were included in the review (LMIC = 32, and HIC = 26). In the pooled sample, there were 27,799 pilgrims aged 2 days to 105 years (male: female = 1.3:1) from LMIC and 70,865 pilgrims aged 2 months to 95 years (male: female = 1:1) from HIC. Pilgrims from both HIC and LMIC had viral and bacterial infections, but pilgrims from HIC tended to have higher attack rates of viral infections than their LMIC counterparts. However, the attack rates of bacterial infections were variable: for instance, pilgrims from LMIC seemed to have higher rates of meningococcal infections (0.015–82% in LMIC vs. 0.002–40% in HIC) based on the study population, but not Mycobacterium tuberculosis (0.7–20.3% in LMIC vs. 38% in HIC). Targeted measures are needed to prevent the spread of airborne infections at Hajj.
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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 | 0.003 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.011 | 0.010 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".