Assessment of the risk posed to Singapore by the 2015 Middle East respiratory syndrome outbreak in the Republic of Korea
Bibliographic record
Abstract
OBJECTIVE: To assess the public health risk to Singapore posed by the Middle East respiratory syndrome (MERS) outbreak in the Republic of Korea in 2015. METHODS: The likelihood of importation of MERS cases and the magnitude of the public health impact in Singapore were assessed to determine overall risk. Literature on the epidemiology and contextual factors associated with MERS coronavirus infection was collected and reviewed. Connectivity between the Republic of Korea and Singapore was analysed. Public health measures implemented by the two countries were reviewed. RESULTS: The epidemiology of the 2015 MERS outbreak in the Republic of Korea remained similar to the MERS outbreaks in Saudi Arabia. In addition, strong infection control and response measures were effective in controlling the outbreak. In view of the air traffic between Singapore and MERS-affected areas, importation of MERS cases into Singapore is possible. Nonetheless, the risk of a serious public health impact to Singapore in the event of an imported case of MERS would be mitigated by its strong health-care system and established infection control practices. DISCUSSION: The MERS outbreak was sparked by an exported case from the Middle East, which remains a concern as the reservoir of infection (thought to be camels) continues to exist in the Middle East, and sporadic cases in the community and outbreaks in health-care settings continue to occur there. This risk assessment highlights the need for Singapore to stay vigilant and to continue enhancing core public health capacities to detect and respond to MERS coronavirus.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 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".