A Comparative Analysis of Factors Influencing Two Outbreaks of Middle Eastern Respiratory Syndrome (MERS) in Saudi Arabia and South Korea
Bibliographic record
Abstract
In 2012, an emerging viral infection was identified in Saudi Arabia that subsequently spread to 27 additional countries globally, though cases may have occurred elsewhere. The virus was ultimately named Middle Eastern Respiratory Syndrome Coronavirus (MERS-CoV), and has been endemic in Saudi Arabia since 2012. As of September 2019, 2468 laboratory-confirmed cases with 851 associated deaths have occurred with a case fatality rate of 34.4%, according to the World Health Organization. An imported case of MERS occurred in South Korea in 2015, stimulating a multi-month outbreak. Several distinguishing factors emerge upon epidemiological and sociological analysis of the two outbreaks including public awareness of the MERS outbreak, and transmission and synchronization of governing healthcare bodies. South Korea implemented a stringent healthcare model that protected patients and healthcare workers alike through prevention and high levels of public information. In addition, many details about MERS-CoV virology, transmission, pathological progression, and even the reservoir, remain unknown. This paper aims to delineate the key differences between the two regional outbreaks from both a healthcare and personal perspective including differing hospital practices, information and public knowledge, cultural practices, and reservoirs, among others. Further details about differing emergency outbreak responses, public information, and guidelines put in place to protect hospitals and citizens could improve the outcome of future MERS outbreaks.
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 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.007 | 0.001 |
| Bibliometrics | 0.001 | 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) | 0.000 | 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".