Contagion Modeling and Simulation in Transport and Air Travel Networks During the COVID-19 Pandemic: A Survey

Riquelme, Fabián; Aguilera, Ana M.; Inostrosa-Psijas, Alonso

doi:10.1109/access.2021.3123892

Cited by 10 publications

(5 citation statements)

References 109 publications

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“…Some studies investigate the relationships between COVID-19 cases and measurements of network structure such as network density (Chu et al, 2020), shortest path length (Kuo and Chiu, 2021), centrality of nodes (Coelho et al, 2020;Ribeiro et al, 2020), and weight of edges (i.e., number of flights or passengers on each edge) (Zhang et al, 2020a;Lau et al, 2020). Several studies also proposed some models to investigate the spread of the virus in airline networks, such as a SIR meta-population model (Hossain et al, 2020), a SEIR meta-population model (Peirlinck et al, 2020), or an epidemiological model (Nikolaou and Dimitriou, 2020;Riquelme et al, 2021).…”

Section: Literature Reviewmentioning

confidence: 99%

Dynamics of the US domestic airline network during the COVID-19 pandemic

Sugishita¹,

Mizutani²,

Hanaoka³

2022

Preprint

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The ongoing COVID-19 pandemic has had serious impacts on the airline industry. Understanding the network dynamics of individual airlines is essential for discussing aviation policies in emergent situations to achieve both ensuring network connectivity and maintaining competition among airlines. In this study, we quantitatively reveal the day-to-day dynamics of the US domestic airline networks consisting of 17 airlines from January 2019 to December 2021. Specifically, we applied a framework for analyzing temporal networks, in which network structure changes over time. First, we found that, even though the number of nodes and edges returned to the pre-pandemic levels around July 2021, the structure of the entire US domestic airline network had been still largely different from the pre-pandemic structure. We also found that the network dynamics varied significantly from airline to airline. Full-service carriers are less flexible in changing their network structure and suffer higher revenue losses. On the contrary, most regional carriers completely shifted to new structure, which may contribute to reduce their revenue losses. Low-cost carriers are characterized by more pronounced differences between airlines and drastically changed their network structure immediately after the declaration of national emergency.

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Section: Literature Reviewmentioning

confidence: 99%

Dynamics of the US domestic airline network during the COVID-19 pandemic

Sugishita¹,

Mizutani²,

Hanaoka³

2022

Preprint

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“…Additionally, Riquelme et al [32] conducted a comprehensive survey on contagion modeling and simulation within transport and air travel networks during the COVID-19 pandemic. Their work underscores that, during the specified period, the scientific literature presented 15 models for the spread of COVID-19 contagion.…”

Section: Introductionmentioning

confidence: 99%

Airplane Seating Assignment Greedy Algorithms That Separate Passengers Likely to Be Susceptible to Infectious Disease From Those Likely to Be Infectious

Milne,

Cotfas,

Delcea

et al. 2024

IEEE Access

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Although the COVID-19 pandemic has mostly ended, there may be future situations (e.g. future pandemics) in which infectious disease spread on airplanes should be minimized. The COVID-19 pandemic led to social distancing as a means of enhancing passenger safety. Methods were developed to separate homogenous passengers from each other on airplanes and in other settings. This paper presents three greedy methods that assign passengers to airplane seats so that those passengers most likely to be susceptible to infectious diseases are separated from those passengers who are most likely to be infectious. Stochastic simulation results show that the performance of the proposed greedy methods provide much higher values for the average distance of separation between susceptible and infectious passengers when compared to a random seat assignment. The improvements in the two best of the three greedy methods range from 152% to 343% across the selected scenarios. In addition to considering passengers who are likely to be infectious and those who are likely to be susceptible to the disease, the methods consider those passengers who are likely to be both infectious and susceptible. By accounting for variations in individual passenger infectiousness and susceptibility to infection, we illustrate how disease spread may be reduced during future pandemics or similar health crises, thereby improving the safety and resiliency of air travel.

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“…Due to the COVID-19 pandemic, there have been a plethora of papers related to all sorts of different disciplines, such as health sciences, social sciences, computer simulations, economics, Social Network Analysis (SNA), among many others. The sole purpose of several papers is to organize and categorize all this information in reviews 2 , 3 , with the spread of the virus through different means of transport, especially air traffic, being one of the most studied topics.…”

Section: Introductionmentioning

confidence: 99%

Complex network analysis techniques for the early detection of the outbreak of pandemics transmitted through air traffic

Fragua,

Jiménez-Martín,

Mateos

2023

Sci Rep

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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.

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Contagion Modeling and Simulation in Transport and Air Travel Networks During the COVID-19 Pandemic: A Survey

Cited by 10 publications

References 109 publications

Dynamics of the US domestic airline network during the COVID-19 pandemic

Dynamics of the US domestic airline network during the COVID-19 pandemic

Airplane Seating Assignment Greedy Algorithms That Separate Passengers Likely to Be Susceptible to Infectious Disease From Those Likely to Be Infectious

Complex network analysis techniques for the early detection of the outbreak of pandemics transmitted through air traffic

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