Mexico: Lessons learned from the 2009 pandemic that help us fight COVID-19
Bibliographic record
Abstract
In April 2009, Mexican, American, and Canadian authorities announced a novel influenza that became the first pandemic of the century. We report on lessons learned in Mexico. The Mexican Pandemic Influenza Preparedness and Response Plan, developed and implemented since 2005, was a decisive element for the early response. Major lessons-learned were the need for flexible plans that consider different scenarios; the need to continuously strengthen routine surveillance programs and laboratory capacity and strengthen coordination between epidemiological departments, clinicians, and laboratories; maintain strategic stockpiles; establish a fund for public health emergencies; and collaboration among neighboring countries. Mexico responded with immediate reporting and transparency, implemented aggressive control measures and generous sharing of data and samples. Lessons learned induced changes leading to a better response to public health critical events.
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 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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.009 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.186 | 0.025 |
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; both teacher heads agree on what is shown here.
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".