Crimean-Congo Hemorrhagic Fever Outbreak in the North Region of Oman in August 2019: Case Series Study
Bibliographic record
Abstract
Background Crimean-Congo hemorrhagic fever (CCHF) is a viral zoonotic tickborne disease that has been linked to a high mortality rate in a number of nations. In Oman, the first case of CCHF was discovered in 1995. The Directorate of Disease Surveillance and Control received reports of four individuals with CCHF from various places in Northern Oman between August 17 and August 23, 2019 (during the Eid Adha festival). Objective The aim of this study was to identify CCHF patients, determine the source and mechanism of transmission, and recommend preventive measures to avoid further outbreaks. Methods We arranged for a field visit with teams from the Ministry of Agriculture, Fisheries and Municipality on the same day of notice (August 23-17, 2019) in the region, and a case series study was undertaken using a semistructured questionnaire. Results The findings revealed that all of the patients were men (three were Omanis), ranging in age from 40 to 55 years. Three of the patients worked in slaughterhouses, and all patients had close contact with raw sheep tissues. Fever and gastrointestinal problems were the most common symptoms, with a case fatality rate of 25%. Late bleeding signs and coagulopathy were detected in the patient who died. Conclusions The causative agent was most likely CCHF virus, and the source of the outbreak was infected imported sheep through direct contact with contaminated biological tissues, based on symptoms, signs, lab tests, and the incubation period. All imported sheep must be tested and flagged at the main gates of the three ports in Oman’s north region.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".