Prevalence and Epidemiological Trends in Mortality Due to COVID-19 in Saudi Arabia
Bibliographic record
Abstract
Abstract Background: Coronavirus disease of 2019 (COVID-19) created a major public health emergency and an international concern. It is an infectious respiratory illness caused by acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The international mortality rates due to COVID-19 reached 2,748,763 on March 24, 2021. We describe the prevalence, case fatality rate, and epidemiological trends of COVID-19 mortality in Saudi Arabia in this paper.Method: A systematic approach of evaluating COVID-19 related mortalities was established in Saudi Arabia. A scientific committee that evaluated all reported cases with suspicious or confirmed COVID-19 disease using a standardized electronic form. A data registry of all deaths with all clinical parameters was built based on active reporting from all healthcare facilities in Saudi Arabia. Analysis of data using national and regional crude case fatality rate (cCFR) and death per 100,000 population was carried. Descriptive analysis of age, gender, nationality, and comorbidities. Mortality trend was plotted per week and compared to international figures.Results: The total reported number of deaths between March 23rd until April 9, 2021 was 6,737. cCFR was reported as 1.70%, and death per 100,000 population was reported as 19.24 which compared favourably to figures reported by several developed countries. Highest percentages of deaths were among individuals aged between 60-69 years, males (74%), individuals with diabetes (60%), and Hypertension (50%). Conclusion: Case fatality rate and death per 100,000 population in Saudi Arabia is among the lowest in the world due to multiple factors. Several comorbidities have been identified namely diabetes, hypertension, obesity, and cardiac arrhythmias.
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 | 0.002 | 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) | 0.001 | 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".