Measles Outbreak-Khywan, Huth District, Amran Governorate, Yemen, March, 2017
Bibliographic record
Abstract
Background: On March 3, 2017, cases with fever and skin rash were reported in Khywan, Huth district, Amran by electronic disease early warning system (eDEWS) coordinator. A team from Y-FETP was sent to investigate the problem. Objective: To confirm the existence of outbreak and recommend control measures. Methods: We conducted active house to house- case search. Suspected case was any person with fever and skin rash at any age in Khywan area, Huth district, Amran governorate, from 5 February to 5 March 2017. Data analysis was performed by Epi Info version 7.2. Results: Results 32 cases met the standard case definition, the index case was reported in 5 Feb. 2017, the outbreak continued till 5 March with the peak of cases in 3 March 2017 (6 cases). The percentage of females was 53%. The highest percent was among the age group 1-<5 years with the attack rate 21/1000 of population. The percent of non-vaccinated cases was 97%. Reasons for non-vaccination were: lack of outreach vaccination services (39%), absence of vaccine in the nearby health center (39%), and vaccination refusal (22%). All cases had fever and skin rash, 97% of cases had cough and conjunctivitis. About 13% of cases were admitted in Huth hospital due to pneumoniae as a complication of measles. Conclusions: Existence of Measles outbreak in Khywan area, Huth district, Amran governorate was confirmed. Urgent measles immunization campaign with ongoing routine and outreach immunization services are recommended to increase vaccination coverage.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".