Table 2.
Extant literature on COVID-19 pandemic spreading under air travel.
| Study | Major finding |
|---|---|
| Christidis and Christodoulou (2020) | Aviation data about outbound flights from China are used to predict the countries with a high risk of infections; and a methodology for monitoring the evolution of the pandemic across countries is suggested. |
| Chu et al. (2020a) | It is shown that infection case-correlation analysis between countries and their induced network structures can be complementary to using aviation data for the detection of pandemic outbreaks. |
| Coelho et al. (2020) | Using a standard multiple regression model, it was found that the exponential growth rate of COVID-19 is explained mainly by population size and country's importance (airport connections). |
| Daon et al. (2020) | Scenario analysis shows that airports in East Asia have the highest risk of acting as sources for future outbreaks; complemented by airports in India, Brazil, and Africa. |
| Gomez-Rios et al. (2020) | Based on data for Colombia, it is argued that the initially scarce control of inbound air travelers and their non-compliance with procedures has significantly contributed to the spread to/inside the country. |
| Hossain et al. (2020) | A simplified SIR meta-population model is proposed, which allows to for the calculation of the arrival time, number of imported cases, and the potential for an outbreak, based on aviation data. |
| Lau et al. (2020) | The authors identified a strong linear relationship between domestic COVID-19 cases and the passenger volume inside China. |
| Musselwhite et al. (2020) | Reducing the hypermobility of transport networks and focusing more on local connectivity is perhaps a solution for creating novel post-pandemic mobility patterns for networks. |
| Li et al. (2020b) | Restrictions placed on air traffic in eleven megacities in China reduced these cities' COVID-19 cases, but the restrictions were only effective for a short time. |
| Nakamura and Managi (2020) | The overall relative risk of importation and exportation of COVID-19 from/to every airport was calculated and the necessity of air travel reduction is suggested. |
| Nikolaou and Dimitriou (2020) | A novel epidemiological model for Europe's airline network is developed, which is able to identify the critical airports for infectious disease outbreaks. |
| Peirlinck et al. (2020) | A SEIR meta-population model is developed, which is used to analyze the outbreak dynamics in China and US. |
| Ribeiro et al. (2020) | The expansion of COVID-19 is directly proportionally to the airport closeness centrality within the Brazililan air transportation network |
| Tuite et al. (2020) | The outbreak size in Iran is predicted based on air travel data between Iran and other countries, together with an estimation where the disease may spread next. |
| Zhang et al. (2020b) | Proposes a risk index which measures a country's imported case risk based on the number of international flights; and evaluates the evolution of index's values over time. |
| Zhang et al. (2020c) | The role of air travel in the spread of COVID-19 in China is compared to those of high-speed train and coach services, finding that the spread is a complex interaction and most likely to emerge in larger cities. |