COVID-19 in the Arab countries: Three-year study
Bibliographic record
Abstract
Background: Twenty-two Arab countries share a common language, history, and culture. Nevertheless, governmental policies, healthcare systems, and resources differ from one Arab country to another. We have been following Coronavirus (COVID-19) from the beginning in each Arab country. In the present study, we aimed to assess the prevalence of COVID-19 in the Arab world and to compare these findings with other significantly affected countries. Methods: Websites of the World Health Organization, World COVID-vaccinations tracker, Worldometer, and Ministries of Health were used to extract COVID-19 data in all Arab countries between the period January 2020 to December 2022. Results: All Arab countries had 14,218,042 total confirmed COVID-19 cases, 13,384,924 total recovered cases and 173,544 total related deaths. The trend demonstrated that the third quarter of 2021 recorded the highest death toll and the first quarter of 2022 recorded the highest number of confirmed and recovered cases. Compared to the top 15 affected countries, the Arab world ranked last as it had the lowest overall incidence per million population (PMP) of 31,609. The data on total deaths PMP showed that India had the lowest number of deaths with only 377 cases followed by the Arab world with 386 cases. Conclusions: Although the number of confirmed, death, and recovered cases of COVID-19 have greatly reduced in the last quarter of 2022 in most Arab countries, many Arab countries still need to re-campaign about COVID-19 vaccines and raise awareness programs about boosters. COVID-19 has had a relatively smaller impact on Arab countries than on other countries that have been significantly affected.
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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 | 0.016 | 0.063 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.000 | 0.004 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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; both teacher heads agree on what is shown here.
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".