The Evolution of Vigilance and Its Atrophy Preceding the COVID-19 Global Pandemic
Bibliographic record
Abstract
Introduction: Recent infectious outbreaks preceding the COVID-19 crisis resulted in the evolution of vigilance for preparedness against the next pandemic. This vigilance was maintained to varying degrees in different jurisdictions. Objective: To evaluate the evolution of vigilance following previous epidemics and pandemics and the subsequent atrophy of vigilance prior to the COVID-19 global pandemic. Methods: We evaluated documentation discussing US, Canada, and South Korea from March 2002 to October 2021. Our policy search strategy was rooted in academic literature, government documents and media reports. Results: In the US, there were examples of atrophy of vigilance; however, there was clear understanding of pandemic readiness actions that were simply not executed amongst political chaos. In Canada, political mishaps were less evident at the time the pandemic unfolded. Nevertheless, atrophy was evident with erosion in preparedness programs following SARS. South Korea appeared least subjected to atrophy of vigilance. The more recent MERS outbreak prompted evolution of sustained vigilance and compliance with basic public health measures such as mask wearing. Recommendations: Policy options need to be explored and instituted that increase protection of preparedness programs through institutional safeguards and accountability measure.
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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 | 0.005 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| 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".