CovidVisualized: Visualized compilation of international updated models’ estimates of COVID-19 pandemic at global and country levels
Bibliographic record
Abstract
OBJECTIVES: To identify international and periodically updated models of the COVID-19 epidemic, compile and visualize their estimation results at the global, regional, and country levels, and periodically update the compilations. This compilation can serve as an early warning mechanism for countries about future surges in cases and deaths. When one or more models predict an increase in daily cases or infections and deaths in the next one to three months, technical advisors to the national and subnational decision-makers can consider this early alarm for assessment and suggestion of augmentation of preventive measures and interventions. DATA DESCRIPTION: Five international and periodically updated models of the COVID-19 pandemic were identified, created by: (1) Massachusetts Institute of Technology, Cambridge, (2) Institute for Health Metrics and Evaluation, Seattle, (3) Imperial College, London, (4) Los Alamos National Laboratories, Los Alamos, and (5) University of Southern California, Los Angeles. Estimates of these five identified models were gathered, combined, and graphed at global and two country levels. Canada and Iran were chosen as countries with and without subnational estimates, respectively. Compilations of results are periodically updated. Three Github repositories were created that contain the codes and results, i.e., "CovidVisualizedGlobal" for the global and regional levels, "CovidVisualizedCountry" for a country with subnational estimates-Canada, and "covir2" for a country without subnational estimates-Iran.
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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 | 0.003 | 0.018 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.078 | 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; 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".