Data Resource Profile: COVerAGE-DB: a global demographic database of COVID-19 cases and deaths
Bibliographic record
Abstract
Abstract COVerAGE-DB is an open access database including cumulative counts of confirmed COVID-19 cases, deaths, and tests by age and sex. Original data and sources are provided alongside data and measures in age-harmonized formats. The database is still in development, and at this writing, it includes 87 countries, and 195 subnational areas. Cumulative counts of COVID-19 cases, deaths, and tests are recorded daily (when possible) since January 2020. Many time series thus fully capture the first pandemic wave and the beginning of later waves. An international team, composed of more than 60 researchers, contributed to the collection of data and metadata in COVerAGE-DB from governmental institutions, as well as to the design and implementation of the data processing and validation pipeline. We encourage researchers interested in supporting this project to send a message to the email: coverage-db@demogr.mpg.de
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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 | 0.004 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.008 | 0.014 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.180 | 0.067 |
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".