COVID‑19 pandemic: Understanding the emergence, pathogenesis and containment (Review)
Bibliographic record
Abstract
The ongoing crisis of the novel SARS‑CoV‑2 disease (COVID‑19) has forced several countries to resort to extreme public health measures at a level never encountered in the past century. Originating in Wuhan, China, this is the third highly pathogenic coronavirus (CoV) following SARS‑CoV‑1 and MERS‑CoV. As of June 20, 2020, >8.6 million of the world population were infected with SARS‑COV‑2, and almost 0.46 million individuals have succumbed to the disease. After 6 months of the outbreak, the understanding of the pathobiology and epidemiology of COVID‑19, as well as clinical management strategies have increased substantially. The phylogenetic analysis of SARS‑CoV‑2, exhibiting close similarity (~96%) with bat SARS‑like CoV has indicated its zoonosis in bats. The human‑to‑human transmission of COVID‑19 has been confirmed through multiple modes, such as nasal droplets, oral mucus, aerosols and fomites. SARS‑CoV‑2 has an incubation period of 2‑14 days with symptoms of fever, cough and breathlessness, which may manifest from mild pneumonia to severe illness and death. Currently, RT‑PCR and antibody‑based test kits are being used for the identification of infected individuals. Owing to the lack of specific available treatments, several repurposed drugs and new vaccine candidates are currently undergoing phase I/II clinical trials and are expected to be available to the public as soon as possible. Nonetheless, it is imperative for world bodies to unite in combating this pandemic by developing cost‑effective kits and therapeutics, and making them available to resource‑poor countries.
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 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".