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Record W3112588819 · doi:10.21203/rs.3.rs-122443/v1

A One Health Approach to Studying the Differences in the Evolutionary Dynamics of MERS and SARS Coronaviruses

2020· preprint· en· W3112588819 on OpenAlexaff
Xu Zhang, Jamal S. M. Sabir, Xuejuan Shen, Zhiqing Pu, Nahid H. Hajrah, Mohamed Morsi M. Ahmed, Tingting Luo, Meshaal J. Sabir, Onaizan Godian Al-Zogabi, Junbin Pan, David M. Irwin, Yongyi Shen

Bibliographic record

VenueResearch Square · 2020
Typepreprint
Languageen
FieldMedicine
TopicSARS-CoV-2 and COVID-19 Research
Canadian institutionsUniversity of Toronto
FundersHigher Education Discipline Innovation ProjectNatural Science Foundation of Guangdong ProvinceNational Natural Science Foundation of China
KeywordsMiddle East respiratory syndrome coronavirusPhylogenetic treeBiologyEvolutionary biologyCladeEvolutionary dynamicsDipeptidyl peptidase-4Middle East respiratory syndromeCoronavirus disease 2019 (COVID-19)Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)ZoologyPhylogeneticsVirologyDiseaseGeneGeneticsInfectious disease (medical specialty)MedicineEnvironmental healthPopulation

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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.

Opus teacher head0.311
GPT teacher head0.467
Teacher spread0.156 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2020
Admission routes1
Has abstractyes

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