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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".