Table 3.
Literature on flight suspensions in presence of COVID-19.
| Study | Major finding |
|---|---|
| Adiga et al. (2020) | An index for measuring the effect of airline suspensions on delaying the spread is proposed; identifying mostly a few days difference only. |
| Albers and Rundshagen (2020) | Classification of airline reactions over time: Retrenchment, Persevering, Innovating, Exit, Resume, etc. based on aviation industry newsletter Aviation Week Network. |
| Bombelli (2020) | Analysis of the role of integrators/freight airlines during the pandemic; using complex network tools. Capacity reduction for cargo has taken place rather short-term only. |
| Budd et al. (2020) | The reactions of airlines towards the early stages of COVID-19, the possible drivers and explanations of decisions, and the possible mid-term impacts are discussed. |
| Iacus et al. (2020) | Scenario analysis based on air passenger data comparing normal projections with COVID-19 and discussing implications such as loss in revenue and jobs inside Europe. |
| Li (2020) | The air cargo sector in China has suffered a less severe depression compared to air passenger traffic. |
| Nhamo et al. (2020) | Effect of network changes on airport revenues are analyzed; During COVID-19, many airports converted into parking lots and ghost towns. |
| Nižetić (2020) | The impact of COVID-19 on the EU was analyzed, with hundreds of thousands of cancelled flights. Cargo flights were not severely affected. |
| da Silveira Pereira and Soares de Mello (2021) | Brazilian airlines are being analyzed; airlines with a better aircraft mix are more likely to survive many flight cancellations and its inherent challenges during a pandemic. |
| Sousa and Barata (2020) | Decisions on increasing and reducing mobility based on open data and machine learning are discussed on test cases for Hongkong and Wuhan. |
| Strauss et al. (2020) | Analyzing the impact of flight reductions on the kidney transplants transportation network on the US. |
| Sun et al. (2020a) | Complex network analysis tools are used to explore the impact of COVID-19 on air transportation, at different levels of fractality. |
| Sun et al. (2020b) | Countries' reactions in terms of flight reductions are compared to the number of COVID-19 cases; finding that largely heterogeneous responses led to a possibly too-late response. |
| Suzumura et al. (2020) | The number of flights during COVID-19 is analyzed as a time series; in addition, the number of workers in the tourism and airlines business are analyzed. |