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 χ 2 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 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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".