Search

Air transport has been identified as one of the primary means whereby COVID-19 spread throughout Europe during the early stages of the pandemic. In this paper we analyse two categories of methods – dynamic network markers (DNMs) and network analysis-based methods – as potential early warning signals for detecting and anticipating COVID-19 outbreaks in Europe on the basis of accuracy regarding the daily confirmed cases. The analysis was carried out from 15 February 2020, around two weeks before the first COVID-19 cases appeared in Europe, and 1 May 2020, approximately two weeks after all the air traffic in Europe had been shut down. Daily European COVID-19 information sourced from the World Health Organization was used, whereas air traffic data from Flightradar24 has been incorporated into the analyses by means of four alternative adjacency matrices. Some DNMs have been discarded since they output multiple time series, which makes it very difficult to interpret their results. The only DNM outputting a single time series does not emulate the COVID-19 trend: it does not detect all the main peaks, which means that peak heights do not match up with the increase in the number of infected people. However, many combinations of network analysis based methods and adjacency matrices output good results (with high accuracy and 20-day advance forecasts), with only minor differences from one to another. The number of edges and the network density methods are slightly better when dynamic flight frequency information is used. [ABSTRACT FROM AUTHOR]

We exploit daily information on flight traffic between 37 European countries from 2019 to 2022 to identify the drivers of air travel demand during and after the pandemic. By applying two-way fixed-effects regressions on daily data at the route-level, we find that the strict lockdown orders reduced bilateral flights by about 17% on average. Our results suggest that the fall in flight traffic from the first and second lockdowns was about 10% and 12%, respectively, while the third lockdown led to a massive reduction corresponding to 22%. The results also reveal that a one-unit change in the aggregate stringency index led to 0.2% decrease in flight frequency, while reducing the number of passengers by 0.7%. The empirical evidence suggests that a substantial change in travel patterns was explained by the spread of the virus itself, as well as voluntary actions based on individual preferences and fear of infection. The main findings of this paper have important policy implications for a more coherent response to any future virus outbreak as it affects airlines and the general economy. • We assess the drivers of bilateral flights and passenger demand during COVID-19. • European flight data observed from 2019 to June 2022 are used. • Lockdowns and stringent measures explained less than 50% of flight reductions. • Individual preferences and voluntary actions also matter. • Incentives for vaccination uptake and for reducing the fear of infection are needed. [ABSTRACT FROM AUTHOR]