The Prevalence of COVID-19 in Jizan Region-Saudi Arabia: A Demographic Analysis
Bibliographic record
Abstract
BACKGROUND The unrelenting pandemic of the SARS CoV2 (COVID-19) pleads for re-examining predictors of infection and containment measures, once again. AIMS The researchers aim to investigate the prevalence of COVID-19 in Jizan region to analyse the demographic details of the population, to examine the quarantine predictors and the prescription of zinc and azithromycin. METHODS The researcher reviewed the Jizan region data obtained from the Ministry of Health of Saudi Arabia and performed a cross-sectional study from September 1st, 2020 - September 29th, 2020. The researchers surveyed people from the same region to collect and analyse demographic and quarantine data. RESULTS The total number of positive cases was 11,752 patients in the Jizan region since the start of the pandemic. The prevalence of infection is 0.84% with a mortality rate of 1.73% (n=257). Out of 328 participants, 46.4% (n=148) acquired the infection with an admission rate of 1.6% (n=5). We noted two predictors for infection in the region: female gender and being married. Furthermore, males were more likely to be admitted than females and irrespective of age and chronic diseases. The quarantine after contact with a probable case or after travel showed an inverse relationship with the age; and in particular young females stratum, p <0.05. One third received zinc supplementation, whereas the majority 82.4% was not pre- scribed azithromycin. CONCLUSION Overall, the researchers provide a region-specific analysis that uncovers important infection determinants for COVID-19 infection, which should be taken into consideration when designing and implementing health promotions programs.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.028 | 0.104 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.005 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".