Measles and the 2019 Hajj: risk of international transmission
Bibliographic record
Abstract
Over 2 million Muslim pilgrims will travel to Mecca, Saudi Arabia, this year to perform the Hajj.1 As Memish et al. described,2 the temporary congregation of international and domestic pilgrims in the midst of the current global surge in measles raises the specter of measles transmission at the Hajj. We quantified the risk of case importation associated with the 2019 Hajj and also identified countries vulnerable to secondary exportation as pilgrims return to their home countries. To identify countries at highest risk of importing measles cases to the Hajj, we combined pilgrimage data, population estimates and measles data. We used the total number of foreign pilgrims in 20181 and the proportionate country-level distribution of the Muslim population3 to determine country-level expected number of pilgrims. We converted national 12-month cumulative measles incidence4 (as of 10 July 2019) to incidence rates, which were multiplied by average duration of infection to obtain point prevalence. Finally, the expected number of arriving infected pilgrims, by country, was calculated by multiplying the number of pilgrims by measles prevalence.
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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 | 0.005 |
| 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 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".