Health economics and emergence from COVID-19 lockdown: the great big marginal analysis
Bibliographic record
Abstract
Despite denials of politicians and other advisors, trade-offs have already been apparent in many policy decisions addressing the coronavirus disease 2019 pandemic and its social and economic consequences. Here, we illustrate why it is important, from a wellbeing perspective, to recognise such trade-offs, and provide a framework, based on the economic concept of 'marginal analysis', for doing so. We illustrate its potential through consideration of optimising the balance between reducing the reproductive rate (R) of the virus and further opening of the economy. The framework accommodates both perspectives in the health-vs-economy debate whereby, depending on where we are within the marginal analysis framework, either health issues are allowed to dominate or, below some threshold of R and/or background level of infection, health and economic considerations can be traded off against each other. Given the inevitability of such trade-offs, the framework exposes crucial questions to be addressed, such as: the critical value of R and/or background infection, above which health considerations predominate, and which may vary from jurisdiction to jurisdiction; and the value of lives forgone resulting from the small increases in R and/or background infection levels that may have to be tolerated as the economy is gradually opened.
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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 | 0.008 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.026 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.008 | 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".