A One Health Approach to Studying the Differences in the Evolutionary Dynamics of MERS and SARS Coronaviruses
Bibliographic record
Abstract
Abstract BackgroundSARS-CoV and MERS-CoV are two coronaviruses that received great attention due to their high pathogenicity and mortality rates in human populations. While SARS was controlled, MERS continues to be a global public health concern. To examine differences in the epidemic patterns of these two viruses, we collected all available sequences to compare the different evolutionary characteristics of SARS-CoV and MERS-CoV. Notably, almost all of the human infection cases occurred in the Middle East, and cases that occurred outside of the Middle East involved travelers from this region, while African infections have so far not been reported. It is not clear that genetic differences between Africans and Arabs lead to differences in susceptibility.ResultsIn this study, we compared their evolutionary dynamics to provide a One Health perspective of their different 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 easier to spill over from animal-to-human interface. Although MERS-CoVs are endemic to dromedary camels in both the Middle East and Africa, all human infections are linked to the Middle East. The nucleotide sequences of the MERS-CoV receptor gene--dipeptidyl peptidase 4 (DPP4) from 30 Egyptians, 36 Sudanese, and 34 Saudi Arabians showed little difference.ConclusionsOur study reveals the reason why MERS-CoV is not easily controlled. Analysis of genetic differences between Africans and Arabs suggest that human population differences in DPP4 might not be the reason for their different MERS prevalence, raising the possibility that other reasons, such as poorer disease surveillance in Africa, might explain these observations.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| 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) | 0.004 | 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".