Modeling economic impacts of mobility restriction policy during the COVID‐19 pandemic
Bibliographic record
Abstract
The economic impacts of pandemics can be enormous. However, lockdown and human mobility restrictions are effective policies for containing the spread of the disease. This study proposes a framework for assessing the economic impact of varying degrees of movement restrictions and examines the effectiveness of this framework in a case study examining COVID-19 control measures in Japan. First, mobile network operators data and total employment statistics on a 500-meter grid scale are used to determine the status of mobility restrictions and impacts on consumption in 30 industrial sectors. Next, the economic impacts are assessed using a spatial computable general equilibrium (CGE) model, proven to yield valuable insights into the total economic impacts of natural disasters. In sectors that implement telework and e-commerce-wholesale/retail, finance/insurance, and communication sectors-estimates of production and GDP are obtained that are close to the actual figures. The current case study is limited to Japan, but similar analysis can be conducted by using the CGE model for each country and open mobility data. Thus, the framework has potential to serve as an effective tool for assessing trade-offs between infection risks and economic impacts to inform policy-making by combining with findings from epidemiology.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".