The Demography of COVID-19 Deaths Database
Bibliographic record
Abstract
AN OPEN ACCESS PLATFORM TO INTERNATIONAL DATA AND METADATA ON COVID-19 DEATHS AIM. Since the start of the pandemic, statistical offices in many countries have been publishing daily estimates of COVID-19 deaths. This project collects material from national reports and databases presenting these numbers and publishes it in a freely accessible and duly documented international database on COVID-19 mortality. The aim is to provide researchers and non-specialists with tools to rigorously assess the accuracy of COVID-19 death counts for international comparisons of the pandemic. CONTENT/METHOD. The database comprises (1) COVID-19 death counts stratified by sex, age group and place of death, and (2) information describing the data coverage, quality and accuracy. This bilingual French-English platform also provides information on key issues about the limitations of COVID-19 data for analyses of mortality dynamics. So far data, metadata and the associated documentation (e.g., national reports, methodological documents) have been published for the following 21 countries: Austria, Belgium, Canada, Denmark, England and Wales, France, Germany, Italy, Japan, Netherlands, Norway, Portugal, Republic of Korea, Republic of Moldova, Romania, Scotland, Spain, Sweden, Switzerland, Ukraine and United States of America. Data will be periodically updated (weekly at the moment) until the end of 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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.024 | 0.010 |
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".