Impact of the COVID-19 Pandemic on the Airline Industry : Comparison of Actual and Expected Losses
Bibliographic record
Abstract
Since the day COVID-19 was declared a pandemic many companies in various industries all over the world have been affected by this fact. While some businesses have extracted profit from this situation, the airline industry, and, in particular, its passenger transportation sector, as a whole have suffered major losses due to the pandemic. As of 2022, while there have been some works published on the topic of COVID-19 and airlines, there is still not enough literature available to fully understand and evaluate the effect the pandemic had or still has on commercial airlines. Thus, this work will expand the available knowledge of this subject. Using the data from OpenSky, FlightRadar24 and Yahoo Finance we check the changes in air traffic in different parts of the world over the years, as well as how specific commercial airlines have been affected by the pandemic from the initial periods to the current moment when the majority of the counties have removed COVID-19 restrictions. As a result of our analysis, several facts have been established, such as that even in the 3rd quarter of 2022, the majority of the airlines still experience the lingering effects of the pandemic and cannot operate on the same scale, they have done before, while some small number of the commercial airlines have financially benefitted from the COVID-19 in regards of their stock prices and managed to hold this advantage till the current year.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".