Estimating and Projecting Air Passenger Traffic during the COVID-19\n Coronavirus Outbreak and its Socio-Economic Impact
Bibliographic record
Abstract
The main focus of this study is to collect and prepare data on air passengers\ntraffic worldwide with the scope of analyze the impact of travel ban on the\naviation sector. Based on historical data from January 2010 till October 2019,\na forecasting model is implemented in order to set a reference baseline. Making\nuse of airplane movements extracted from online flight tracking platforms and\non-line booking systems, this study presents also a first assessment of recent\nchanges in flight activity around the world as a result of the COVID-19\npandemic. To study the effects of air travel ban on aviation and in turn its\nsocio-economic, several scenarios are constructed based on past pandemic crisis\nand the observed flight volumes. It turns out that, according to this\nhypothetical scenarios, in the first Quarter of 2020 the impact of aviation\nlosses could have negatively reduced World GDP by 0.02% to 0.12% according to\nthe observed data and, in the worst case scenarios, at the end of 2020 the loss\ncould be as high as 1.41-1.67% and job losses may reach the value of 25-30\nmillions. Focusing on EU27, the GDP loss may amount to 1.66-1.98% by the end of\n2020 and the number of job losses from 4.2 to 5 millions in the worst case\nscenarios. Some countries will be more affected than others in the short run\nand most European airlines companies will suffer from the travel ban.\n
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".