Observatório Covid-19 Fiocruz - uma análise da evolução da pandemia de fevereiro de 2020 a abril de 2022
Bibliographic record
Abstract
The COVID-19 pandemic had a significant impact on the living and working conditions of the entire population of Brazil, having a different and more intense effect on groups considered to be vulnerable. The objective of this article is to present an overview of the evolution of the pandemic in the country according to the bulletins of the Covid-19 Fiocruz Observatory in the period between the declarations of the beginning and end of the Public Health Emergency of National Concern (ESPIN, in Portuguese), February 2020 to April 2022. Several of the indicators adopted in the 69 bulletins published for the analysis of the pandemic were used, such as cases and deaths due to SARIs and COVID-19, age groups, % of occupancy of ICU beds, and vaccination, among others. The evolution analysis was organized between years and phases of the pandemic, seeking to highlight what characterized each moment. The closing statement of ESPIN in Brazil coincides with the discussions on the transition from a pandemic to an endemic scenario, without this representing the elimination of the virus, infections, and disease, posing the challenges of advances in vaccination processes in Brazil and around the world, as well as living with scenarios that may require the adoption of temporary protection measures in epidemic periods and periods of greater risk for vulnerable groups.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".