Outbreak Control Policies for Middle East Respiratory Syndrome (MERS): The Present and the Future
Bibliographic record
Abstract
Looking at current policies in place regarding the MERS outbreak, MoH's Command and Control Center reviewed data related to MERS-CoV from 2012-14, which helped the center develop policies to minimize the spread of .The policies in place are primarily for improving the efficiency of surveillance i.e. case reporting as well as to ensure the reliability of the data collected.Focusing on increasing the efficiency, quality and capacity of the standardized laboratory settings was yet another step taken to improve data collection [18].Currently there is no vaccination or treatment available for this AbstractMiddle East Respiratory Syndrome coronavirus (MERS-CoV) is the latest coronavirus to have emerged in the human population.The Saudi Arabian Ministry of Health (MoH) quickly came up with guidelines for the public and healthcare workers.This article looks at the policies recommended by International Organizations and the MoH.Like Ebola, the MERS-CoV is speculated to have come from bats, which harbour similar Coronaviruses.The article uses the principles of One Health to look at the outbreak and compares the MERS outbreak to the Ebola virus disease outbreak in West Africa.Besides highlighting key policy recommendations for the MERS-CoV outbreak, some recommendations have been brought about.The article intends to explain the MERS-CoV outbreak in a more holistic approach, taking in to consideration the One Health implications of the outbreak.
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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 | 0.042 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.010 | 0.011 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".