Empirical Study of Monthly Economic Losses Assessments for “Standard Unit Lockdown” Due to COVID-19
Bibliographic record
Abstract
Background: The pandemic of COVID-19 has been shaping economic developments of the world. From the standpoint of government measures to prevent and control the epidemic, the lockdown was widely used. It is essential to access the economic losses in a lockdown environment which will provide government administration with a necessary reference for decision making in controlling the epidemic. Methods: We introduce the concept of "standard unit incident" and an economic losses assessment methodology for both the standard and the assessed area. We build a "standard unit lockdown" economic losses assessment system and indicators to estimate the economic losses for the monthly lockdown. Using the comprehensive assessment system, the loss infected coefficient of monthly economic losses during lockdown in the 40 countries has been calculated to assess the economic losses by the entropy weighting method (EWM) with data from the CSMAR database and CDC website. Results: We observe that countries in North America suffered the most significant economic losses due to the epidemic, followed by South America and Europe, Asia and Africa, and Oceania and Antarctica suffered relatively minor economic losses. The top 10 countries for monthly economic losses during lockdown were the United States, India, Brazil, France, Turkey, Russia, the United Kingdom, Italy, Spain, and Germany. The United States suffered the greatest monthly economic losses under lockdown ($65.3 billion), roughly 1.5 times that of China, while Germany suffered the least ($56.4 billion), roughly 1.3 times that of China. Conclusion: Lockdown as a control and mitigation strategy has great impact on the economic development and causes huge economic losses. The economic impact due to the pandemic has varied widely among the 40 countries. It will be important to conduct further studies to compare and understand the differences and the reasons behind.
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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 | 0.004 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| 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".