Lessons learned from the epidemiology and control of the COVD-19 pandemic in the American Continent
Bibliographic record
Abstract
On December 12, 2022, a panel seminar organized by the American Journal of Field Epidemiology (AJFE) was held with the enthusiastic support of the National Institute of Health of Colombia. More than 200 professionals from Colombia, Peru, Brazil, El Salvador, Canada, the USA, Ecuador, Costa Rica, Chile, and Mexico participated. The panelists, moderated by Dr. Marjorie Pollack, member of the AJFE Editorial Board and associate editor of ProMED, presented the epidemiological characteristics of the pandemic in Colombia, Brazil, Costa Rica, Puerto Rico, Haiti, and the Dominican Republic, as well as the public health response with which it was responded to mitigate and control it. The panelists emphasized the value of the preparation provided by the existence of field epidemiology training programs, the existence of a response plan, of establishing good communication with decision makers to make them collectively. The Costa Rican experience of using epidemiology to facilitate the provision of health services to patients with COVID-19 was shared. Likewise, the panelists emphasized that the misinformation, spread on the internet and some other means, had a negative impact, and the experience of educating those working for the news media was shared. It was commented that greater agility is needed to respond early to events that could lead to a pandemic. The panelists added the pandemic highlighted, more than ever before, the importance of having appropriate communication strategies to inform the public.
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 | 0.016 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.007 | 0.013 |
| Insufficient payload (model declined to judge) | 0.004 | 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".