Outcomes of COVID-19 in the Eastern Mediterranean Region in the first 4 months of the pandemic
Bibliographic record
Abstract
OBJECTIVES: To summarize the outcomes of the coronavirus disease 2019 infections in the Eastern Mediterranean Region (EMR) in the first 4 months of the pandemic. METHODS: A meta-analysis approach was used in this context. We used the aggregate data from the World Health Organization Regional Office for the EMRO (until 26 May 2020) to generate this report. RESULTS: An analysis of official data from all 22 countries and territories in the Middle East, North Africa, the Horn of Africa, and Central Asia K=22 (a total of 438,717 cases) was performed. The total number of cases, recovered cases were 438,717,228,986, and deaths was 11,290 in the EMR. Meta-analytic pooling of the point estimates of recovery rate per country in the EMR was 52.5% (95% CI 52.3% - 52.6%). The lowest recovery rates were in Somalia (4.3%), and the highest rates were in Tunisia (87.4%). Meta-analytic pooling of the point estimates of death rate per country in the EMR yielded 3.85% [95% CI 3.80% - 3.9%]. Meta-analytic pooling of the point estimates of recovery rate per country in the Gulf Cooperation Council countries was 46.1% (95% CI 45.8% - 46.3%). Meta-analytic pooling of the point estimates of death rate per country in the Gulf Cooperation Council countries was 0.6% (95% CI 0.50% - 0.65%). CONCLUSION: Wide variability was found between EMR countries in recovery and mortality, implying the possible impact of resource availability, and genetic and environmental factors on the morality and recovery of the COVID-19.
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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 | 0.016 | 0.023 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.034 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".