Non-pharmaceutical interventions to combat COVID-19 in the Americas described through daily sub-national data
Bibliographic record
Abstract
This dataset covers national and subnational non-pharmaceutical interventions (NPI) to combat the COVID-19 pandemic in the Americas. Prior to the development of a vaccine, NPI were governments' primary tools to mitigate the spread of COVID-19. Variation in subnational responses to COVID-19 is high and is salient for health outcomes. This dataset captures governments' dynamic, varied NPI to combat COVID-19 for 80% of Latin America's population from each country's first case through December 2021. These daily data encompass all national and subnational units in Argentina, Bolivia, Brazil, Chile, Colombia, Ecuador, Mexico, and Peru. The dataset includes individual and aggregate indices of nine NPI: school closures, work suspensions, public event cancellations, public transport suspensions, information campaigns, local travel restrictions, international travel controls, stay-at-home orders, and restrictions on the size of gatherings. We also collected data on mask mandates as a separate indicator. Local country-teams drew from multiple data sources, resulting in high-quality, reliable data. The dataset thus allows for consistent, meaningful comparisons of NPI within and across countries during the pandemic.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.005 |
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".