Estimating worldwide costs of premature mortalities caused by COVID-19
Bibliographic record
Abstract
Purpose The purpose of this paper is to provide an estimate of the costs of premature mortality caused by the COVID-19 pandemic. Design/methodology/approach Using COVID-19 pandemic-derived mortality data for November 9, 2020 (globally 1,303,215 deaths) and applying a country-based value of statistical life (VSL), the worldwide cost of premature mortality was assessed. The cost was assessed based on income groups until November 9, 2020 and projected into the future until March 1, 2021 using three scenarios from the Institute for Health Metrics and Evaluation (IHME). Findings The global cost of premature mortality is currently estimated at Int$5.9 trillion. For the high-income group, the current estimated cost is Int$ $4.4 trillion or $3,700 per person. Using IHME projections until March 1, 2021, global premature mortality costs will increase to Int$13.7 trillion and reach Int$22.1 trillion if policies are relaxed, while the cost with 95% universal masks is Int$10.9 trillion. The richest nations will bear the largest burden of these costs, reaching $15,500 per person by March 1, 2021 if policies are relaxed. Originality/value The cost of human lives lost due to the pandemic is unprecedented. Preparedness in the future is the best policy to avoid many premature deaths and severe recessions in order to combat pandemics.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".