The Global Emergency of Novel Coronavirus (SARS-CoV-2): An Update of the Current Status and Forecasting
Bibliographic record
Abstract
Over the past two decades, there have been two major outbreaks where the crossover of animal Betacoronaviruses to humans has resulted in severe acute respiratory syndrome coronavirus (SARS-CoV) and Middle East respiratory syndrome coronavirus (MERS-CoV). In December 2019, a global public health concern started with the emergence of a new strain of coronavirus (SARS-CoV-2 or 2019 novel coronavirus, 2019-nCoV) which has rapidly spread all over the world from its origin in Wuhan, China. SARS-CoV-2 belongs to the Betacoronavirus genus, which includes human SARS-CoV, MERS and two other human coronaviruses (HCoVs), HCoV-OC43 and HCoV-HKU1. The fatality rate of SARS-CoV-2 is lower than the two previous coronavirus epidemics, but it is faster spreading and the large number of infected people with severe viral pneumonia and respiratory illness, showed SARS-CoV-2 to be highly contagious. Based on the current published evidence, herein we summarize the origin, genetics, epidemiology, clinical manifestations, preventions, diagnosis and up to date treatments of SARS-CoV-2 infections in comparison with those caused by SARS-CoV and MERS-CoV. Moreover, the possible impact of weather conditions on the transmission of SARS-CoV-2 is also discussed. Therefore, the aim of the present review is to reconsider the two previous pandemics and provide a reference for future studies as well as therapeutic approaches.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".