The Dollar Value of Human Life Losses Associated With COVID-19 in Canada
Bibliographic record
Abstract
Background: The coronavirus disease 2019 (COVID-19) pandemic continues to cause morbidity and premature mortality and ravage the socio-economic sectors in Canada.Objectives: The study aimed to appraise the Total Dollar Value of Human Life Losses (TDVHL) associated with COVID-19 in Canada. Methods: The net output approach was applied in the dollar valuation of the 8810 human life losses associated with COVID-19 in Canada as of July 16, 2020. The economic model wasrerun assuming 3%, 5%, and 10% discount rates with Canada’s life expectancy of 83 years, the world’s average life expectancy of 73 years, the world’s highest average life expectancy of88 years, and a 3% discount rate. Results: The human lives lost to COVID-19 had an estimated value of the international dollar (Int$) 2037021173 and an average of Int$ 231217 per human life lost. Quebec and Ontarioprovinces alone accounted for 94.99% of the TDVHL. Reanalysis of the economic model with discount rates of 5% and 10% resulted in declines in TDVHL of Int$ 192721390 (9%)and Int$ 530132423 (26%), respectively. Substitution of the nation with the word’s average life expectancy shrank the TDVHL by Int$ 1754972473 (86%) while applying the world’shighest life expectancy triggered a growth in the TDVHL of Int$ 498674987 (24%).
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".