Distribución geográfica de las variantes de SARS COV -2 y vacunación en América del Sur: una revisión bibliográfica
Bibliographic record
Abstract
INTRODUCTION. SARS CoV 2 is a single-stranded betacoronavirus RNA virus with more than \n238 million cases, around 5 million deaths and an increase in infections daily to date \nOBJECTIVES. To analyze the main variants of Sars CoV 2, its geographical distribution and \nvaccination rates in South America. MATERIALS AND METHODS. Bibliographic review, \nscientific publications in the best medical journals and portals like Pubmed, Springer, Nature, Read \nby Qx Med, British Medical Journal of medicine, Jama, New England Journal of medicine and \nScielo. RESULTS. In South America, until the second quarter of 2020 we had the circulation of \nthe wild variant, since then the appearance of variants has been modifying both the clinical and \nepidemiological evolution of the pandemic, with the current predominance of omicron and a \nvaccination average in Latin America of 78% with the highest rate in Chile and our country in \nfourth place. DISCUSSION: The evolution of the pandemic from high contagiousness and \nvirulence, to the present day, raises the possibility of theories, such as that of Theobald Smith's or \nLevine's, in which it was stated that with each mutation there is a tendency to virulence and \nseasonality. CONCLUSION: Variants up to the revision in this article were B.1.1.7 (alpha) in the \nUK, B.1.3.51(beta) Brazil, B.1.617.2 (Delta) India, and B.1.1.529. (Omicron) in Bottswana, all \nwith cases registered in Latin America, and with a temporal evolution that went from the \npredominance of the wild virus through delta to omicron, which is dominant globally, not only in \nLatin America. Vaccination in South America presented 78% of its population with a complete \nschedule.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".