Risk Factors for Fatal Middle East Respiratory Syndrome Coronavirus Infections in Saudi Arabia: Analysis of the WHO Line List, 2013–2018
Bibliographic record
Abstract
Objectives. To explore complex associations among demographic factors, risk factors, health care, and fatality rates of Middle East respiratory syndrome coronavirus (MERS-CoV) in the Kingdom of Saudi Arabia. Methods. We based this study on analysis of a publicly accessible line listing of 1256 MERS-CoV cases (2013 to October 2018) available on the World Health Organization’s Web site. For analyses of demographic factors (e.g., age, gender), access to health care, promptness of laboratory services, risk factors (comorbidity, exposure to camels and persons with MERS-CoV), occupation (health care), and outcome (fatality), we used descriptive statistics, risk ratio (RR), and the Pearson χ2 test. Results. Presence of comorbidity (RR = 3; 95% confidence interval [CI] = 2.2, 3.9), being male (RR = 1.6; 95% CI = 1.2, 2.1), exposure to dromedary camels (RR = 1.6; 95% CI = 1.3, 2.3), and consumption of camel milk (RR = 1.5; 95% CI = 0.9, 1.7) can significantly increase risk for fatality. Health care workers have significantly lower fatality (P < .001) than the rest of the persons with MERS-CoV. Conclusions. Policies that promote health awareness for the high-risk population and their prompt seeking of health care should be considered. Publicly accessible line lists of infectious diseases such as MERS-CoV can be valuable sources for epidemiological analysis.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".