How efficient are the lockdown measures taken for mitigating the Covid-19 epidemic?
Bibliographic record
Abstract
Abstract Various lockdown measures have been taken in different countries to mitigate the Covid-19 pandemic. But, for citizens, it is not always simple to understand how these measures have been taken. Should they have been more (or less) restrictive? Should the lockdown period have been longer (or shorter)? What would have been the benefits of starting to confine the population earlier? To provide some elements of response to these questions, we propose a simple behavior model for the government decision-making operation. Although simple and obviously improvable, the proposed model has the merit to implement in a pragmatic and insightful way the tradeoff between health and macroeconomic aspects. For a given tradeoff between the assumed cost functions for the economic and health impacts, it is then possible to determine the best lockdown starting date, the best lockdown duration, and the optimal severity levels during and after lockdown. The numerical analysis is based on a standard SEIR model and performed for the case of France but the adopted approach can be applied to any country. Our analysis, based on the proposed model, shows that for France it would have been possible to have just a quarter of the actual number of people infected (over [March 1, August 31]), while simultaneously having a Gross Domestic Product loss about 30% smaller than the one expected with the current policy
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".