Middle East respiratory syndrome coronavirus: epidemic potential or a storm in a teacup?
Bibliographic record
Abstract
MERS coronavirus: potential pandemic or storm in a teacup?http://ow.ly/ttpkiThe Middle East respiratory syndrome (MERS) is a new killer respiratory disease caused by the MERS coronavirus (CoV) first reported from the Kingdom of Saudi Arabia (KSA) in September 2012, after identification of a novel betacoronavirus from a Saudi Arabian patient who died from a severe respiratory illness [1,2].Retrospective study of stored samples later showed that, earlier in April 2012, a cluster of severe respiratory illness occurred in a public health hospital in Zarqa, Jordan, where eight healthcare workers (HCWs) were among the 11 people affected, with two deaths attributed to .The appearance of any new fatal infectious disease, and uncertainty about its origin and mode of transmission, invariably threatens global health security and its detection in western countries rapidly focuses political and scientific attention.Unfortunately, at the same time, it evokes unnecessary and unwarranted fierce scientific competition and discourse, as was illustrated by the HIV, severe acute respiratory syndrome (SARS) and avian influenza epidemics [4][5][6][7][8].Disappointingly, the events surrounding the MERS-CoV have been no different [6].MERS-CoV was first isolated, sequenced and patented by Erasmus Medical Centre (EMC) researchers in Rotterdam, the Netherlands, and initially it was named after their centre as HCoV-EMC [2].Subsequently, international consensus led to renaming it as MERS-CoV [9].Since the first KSA case report in September 2012, the KSA Ministry of Health (MoH) has recommended mandatory testing for MERS-CoV in all cases of respiratory illness requiring intensive care admission.6 months after MERS-CoV was discovered, at the end of March 2013, there were only 17 MERS-CoV cases reported globally, nine of which were from KSA [10], four of these from one family case cluster [11].This small number of MERS-CoV cases would not have attracted much global attention had it not been for the high mortality rate in persons who contracted the disease, all of whom had medical comorbidities [9].Frenzied media reports followed the detection of MERS cases in the UK [11][12][13], France [14], Germany [15][16][17] and Italy [18], and focussed international attention.The media scaremongering and hype led to exaggerated claims of the potential threat of MERS-CoV to global health security.A flurry of scientific, political and media activity ensued, with global attention focussed on the pandemic potential of MERS-CoV.This had become particularly urgent and important in light of an estimated 2 million pilgrims from over 182 countries expected to visit Makkah and Madinah, KSA, to perform the
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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 | 0.007 | 0.024 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.023 | 0.026 |
| Insufficient payload (model declined to judge) | 0.009 | 0.009 |
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".