The Differences in the Evolutionary Dynamics of MERS and SARS Coronaviruses
Bibliographic record
Abstract
SARS-CoV and MERS-CoV are two coronaviruses that have received significant attention due to their high pathogenicity and mortality rates in human populations. In this study, we compared their evolutionary dynamics to provide a One Health perspective on their differences in terms of the results of disease control. The phylogenetic network of SARS-CoVs showed that human isolates gathered into a “super-spreader” cluster and were distinct from civet isolates. In contrast, dromedary camel- and human-isolated MERS-CoVs were clustered together. Thus, most clades of MERS-CoV can infect humans, and MERS-CoVs seem to more easily spill over the animal-to-human interface. Additionally, the civet can be easily controlled, while the intermediate host (dromedary camels) of MERS-CoV is an important livestock species, so it is impossible to eliminate all animals. This further leads to difficulties in disease control in MERS. Although MERS-CoVs are endemic to dromedary camels in both the Middle East and Africa, human infections are mainly linked to the Middle East. The nucleotide sequences of the MERS-CoV receptor gen (dipeptidyl peptidase 4 (DPP4)) from 30 Egyptians, 36 Sudanese, and 34 Saudi Arabians showed little difference. These findings suggest that the observed disparities in MERS prevalence between populations in the Middle East and Africa may be more strongly attributed to inadequate disease surveillance and the limited camel-to-human transmission of clade C MERS-CoV in Africa, rather than variations in DPP4 gene.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".