Análisis multidimensional de la evolución de la pandemia de la COVID-19 en países de las Américas
Bibliographic record
Abstract
Objective. To evaluate the evolution of the COVID-19 pandemic in countries of the Americas, comparing health system data from before the appearance of the virus in the Region, accumulated cases and deaths before the deployment of public immunization strategies, and the current state of vaccination. Methods. An HJ-Biplot multivariate analysis and cluster analysis were performed for 28 countries in the Region of the Americas at three points in time: December 2019, December 2020, and December 2021. Results. In the Americas, heterogeneity was observed in the actions implemented to contain the pandemic, and this was reflected in different groups of countries. Conclusions. Not all countries in the Region of the Americas had the health conditions necessary to contain COVID-19. At the end of 2019, the United States, Canada, Brazil, and Cuba had advantages over other countries in the Region; however, actions implemented during 2020 to contain the pandemic created different groups of countries in terms of the prevalence of infections and deaths. At the end of 2020, Bolivia, Ecuador, and Mexico had critical levels of mortality. At the end of 2021, after the implementation of vaccination plans, more than 60% of the population of Argentina, Brazil, Canada, Chile, Colombia, Costa Rica, Cuba, Panama, the United States, and Uruguay had completed the vaccination schedule. © 2022 Pan American Health Organization. All rights reserved.
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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 | 0.007 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".