Prevention of a Growing Pandemic, Middle Eastern Respiratory Syndrome: A Literature Review
Bibliographic record
Abstract
Background : Middle Eastern Respiratory Syndrome is a viral respiratory syndrome caused by MERS-CoV. From its first identification in 2012 until today, 1,374 laboratory-confirmed cases of infection with MERS-CoV have been identified in 26 countries, with the majority of these cases (>80%) occurring in the Kingdom of Saudi Arabia (KSA).(1) The rapid international spread of this virus suggests that it is crucial to review the current prevention & control strategies. It is also important to outline further recommendations to prevent the growth of a potential pandemic disease. Methodology : This study was conducted as a targeted literature review and critical analysis of articles from multiple databases. An emphasis was put on the updates and archives posted by the WHO – coronavirus infections. Prevention Strategies : Researchers have studied the rapid spread of MERS-CoV in KSA, focusing on viral spread in hospital settings, via dromedary camels, and religious mass gatherings as risk factors.(3,4,7) Current advancements in the knowledge of camels as a potential MERS reservoir, guidelines for travelers to high-risk countries, and improvements in the surveillance have contributed to the reduction of the MERS incidence cases globally.(5,7) Despite these prevention strategies, more outbreaks continue in KSA and major recent outbreaks have been reported in the Republic of Korea.(8) Conclusion : The remaining and ongoing MERS cases reported in KSA & Republic of Korea represent a global public health concern. As a result there is a need to further improve the management of MERS. Given the rapid spread of the virus and the gap in literature about the transmission of MERS, it is recommended that both high and low risk nations abide by the international MERS recommendations outlined by the WHO.(6) Furthermore, collaboration between the animal and human public health organizations would serve as a progressive step to control the spread of this zoonotic disease.(6) Full Article: http://hdl.handle.net/11375/18028
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".