Lethal zoonotic coronavirus infections of humans – comparative phylogenetics, epidemiology, transmission, and clinical features of coronavirus disease 2019, The Middle East respiratory syndrome and severe acute respiratory syndrome
Bibliographic record
Abstract
PURPOSE OF REVIEW: Severe acute respiratory syndrome-coronaviruses-2 (SARS-CoV-2), the cause of coronavirus disease 2019 (COVID-19), emerged as a new zoonotic pathogen of humans at the end of 2019 and rapidly developed into a global pandemic. Over 106 million COVID-19 cases including 2.3 million deaths have been reported to the WHO as of February 9, 2021. This review examines the epidemiology, transmission, clinical features, and phylogenetics of three lethal zoonotic coronavirus infections of humans: SARS-CoV-1, SARS-CoV-2, and The Middle East respiratory syndrome coronavirus (MERS-COV). RECENT FINDINGS: Bats appear to be the common natural source of SARS-like CoV including SARS-CoV-1 but their role in SARS-CoV-2 and MERS-CoV remains unclear. Civet cats and dromedary camels are the intermediary animal sources for SARS-CoV-1 and MERS-CoV infection, respectively whereas that of SARS-CoV-2 remains unclear. SARS-CoV-2 viral loads peak early on days 2-4 of symptom onset and thus high transmission occurs in the community, and asymptomatic and presymptomatic transmission occurs commonly. Nosocomial outbreaks are hallmarks of SARS-CoV-1 and MERS-CoV infections whereas these are less common in COVID-19. Several COVID-19 vaccines are now available. SUMMARY: Of the three lethal zoonotic coronavirus infections of humans, SARS-CoV-2 has caused a devastating global pandemic with over a million deaths. The emergence of genetic variants, such as D614G, N501Y (variants 1 and 2), has led to an increase in transmissibility and raises concern about the possibility of re-infection and impaired vaccine response. Continued global surveillance is essential for both SARS-CoV-2 and MERS-CoV, to monitor changing epidemiology due to viral variants.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".