Dengue Fever Outbreak in Al-Garrahi District, Al-Hudaydah Governorate, Yemen, 2019
Bibliographic record
Abstract
Background Dengue fever (DF) has re-emerged in Yemen with a higher frequency during the last years. On November 6, 2019, an increased number of suspected DF cases in Al-Garrahi district was reported. On November 7, 2019, a team was sent to investigate. Objective This study aims to confirm the existence of an outbreak, describe the outbreak characteristics, and recommend suitable intervention for control. Methods A descriptive study was conducted. The World Health Organization case definition was used to identify patients. An active search from house to house, along with entomological investigation and health education, was conducted. A line list was used to collect data. Blood specimens were collected and tested by enzyme-linked immunosorbent assay for dengue IgM. Frequency, percentage, and rates were calculated, and the population from the central statistical organization was used. Results A total of 2067 cases met the case definition. Of them, 51% were males and 32% were aged <10 years. All patients complained of fever, headache, and arthralgia (100%), followed by myalgia and retro-orbital pain (67% and 39%, respectively). The first case patient was in week 41, and the peak was reached with 1058 patients in week 46. The overall attack rate was 16 of 1000, significantly higher among patients aged 10 years to <50 years and ≥50 years compared with patients aged <10 years (17 and 19/1000 vs 14/1000; P<.001). Of 20 tested blood samples, 12 (60%) were IgM positive. The house index was 70%, the container index was 50%, and the Breteau index was 140. Vector control measures with community participation were intensified in week 46, and patient cases decreased to 140 in week 48. Conclusions A dengue outbreak was confirmed in Al-Garrahi district. The spread of infection was facilitated by storing water and the presence of indoor larvae. The findings emphasize the importance of health awareness and community participation for containing DF outbreaks.
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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 | 0.000 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".