Detection of Imported Measles Outbreak (Clusters) in Al-Buraimi Governorate during COVID-19 Pandemic: A Case Series Study
Bibliographic record
Abstract
During the first wave of pandemic in 2020, the initial prodromal symptoms of COVID-19 were similar to vaccine preventable diseases like Measles apart from typical rash and probability of missing such cases during COVID-19 will lead to local spread of cases. The most significant risk lies in children below five years, especially unvaccinated expatriate children who contribute to imported cases of measles from highly endemic countries. After initial confirmation of 3 cases in April 2020, this outbreak was epidemiologically investigated in Al Buraimi Governorate, Oman, which included data on clinical symptoms, exposure information, travel history, immunization, and history of contact with others. Among the positive cases, 75% were girls; 6 were Afghani nationals and 2 were Pakistani nationals. However, most cases were reported between Afghani nationals 6 (75%) due to their low vaccination status. Genotyping B3 was isolated, and the virus traced back to Pakistan as the country of origin. In 2019, the Regional Verification Commission for Measles and Rubella (RVC), has declared Oman as a measles and rubella-free nation. The rationale of this study is to have a clear understanding of the events that led to the importation of genotype B3 measles outbreak in Al Buraimi Governorate, Oman, during initial phase of first wave of COVID-19 pandemic in April 2020 which highlighted the existence of vigilant surveillance system of the country. The field investigation was done to confirm an outbreak and to prevent transmission by isolating the cases and vaccinating the unvaccinated children and lastly to make critical recommendations that should be applied to prevent similar outbreaks in the future.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".