Temporal Characteristics of the Chinese Aviation Network and their Effects on the Spread of Infectious Diseases

Mou, Jianhong; Liu, Chuchu; Chen, Saran; Huang, Guocheng; Lu, Xin

doi:10.1038/s41598-017-01380-5

Cited by 19 publications

(9 citation statements)

References 42 publications

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“…-from the SARS period -severe acute respiratory syndrome [2]; -the conclusions are confirmed, for example, by a recent study on the impact of a selected airport network on the spread of the virus [3]; -modern documents and regulations of the International Civil Aviation Organization (COVID-19 aviation recovery task force established by ICAO, International Airport Review, 30 April 2020 [4]); -knowledge of major international airports, such as Heathrow COVID-19 detection trials.…”

Section: Content Analysis and Study Of Experiencementioning

confidence: 60%

Specifications for Test Modules in the APVV Project PP-20-COVID-0002 Aerosol

Келемен

Bova²

2021

Acta Avionica

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The global pandemic of COVID-19 has forced the international community to look for relevant solutions to eliminate the negative effects and reduce the risks of contamination of people with this virus. The research team of the Technical University in Košice, with the support of experts and an application guarantor, BovaCHEM, approached the search for a partial solution within the applied research of a new chemical for decontamination and disinfection of surfaces that may be a source of COVID-19 threat to humans when handling packaging. ) in different conditions. The article presents a partial result of the analytical work within the project, identification and description of specifications for the creation of test modules in the project, for the subsequent verification of the pilot version of the new chemical and its further research to achieve the main goal of the project. The output of the content analysis of the issue, the examination of the patent solution at present, is the synthesis of knowledge, which is reflected in the proposed specifications for test modules and thus for further scientific work according to the approved project APVV until the end of 2021.

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Section: Content Analysis and Study Of Experiencementioning

confidence: 60%

Specifications for Test Modules in the APVV Project PP-20-COVID-0002 Aerosol

Келемен

Bova²

2021

Acta Avionica

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“…Baspinar and Koyuncu [35] and Mou et al [36] assumed that recovered vertices are not immune to flight delay and can be delayed with the same possibility with susceptible ones. ey employed the simplest SIR model.…”

Section: Flight Delay Propagationmentioning

confidence: 99%

“…In addition, the executions of both individual-based SIR [35,36] and improved cascade failure [37] require basic computer simulation. is requirement not only increased calculation complexity but also decreased flexibility to the adjustment of network architecture.…”

Section: Flight Delay Propagationmentioning

confidence: 99%

“…Baspinar and Koyuncu [35] e accuracy of established models in capturing delay spreading was demonstrated via the comparison between estimated rates and real rates. Mou et al [36] introduced two airport-based SI models to evaluate effects of flight time and ground-waiting time on the spreads of flight delay. In one of them, infection of flight delay completed at the moment flight lands.…”

Section: Flight Delay Propagationmentioning

confidence: 99%

“…On the foundation of character analyses about flight delay in complex network, scholars attempted to discover causal relations of flight delay [29][30][31][32][33][34]. Modellings or predictions about flight delay propagation in network level have also achieved much progress [35][36][37][38][39][40] with support of complex network.…”

Section: Introductionmentioning

confidence: 99%

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Recent Progress about Flight Delay under Complex Network

2021

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Flight delay is one of the most challenging threats to operation of air transportation network system. Complex network was introduced into research studies on flight delays due to its low complexity, high flexibility in model building, and accurate explanation about real world. We surveyed recent progress about flight delay which makes extensive use of complex network theory in this paper. We scanned analyses on static network and temporal evolution, together with identification about topologically important nodes/edges. And, we made a clarification about relations among robustness, vulnerability, and resilience in air transportation networks. Then, we investigated studies on causal relations, propagation modellings, and best spreaders identifications in flight delay. Ultimately, future improvements are summarized in fourfold. (1) Under Complex Network, flight operation relevant subsystems or sublayers are discarded by the majority of available network models. Hierarchical modelling approaches may be able to improve this and provide more capable network models for flight delay. (2) Traffic information is the key to narrow the gap between topology and functionality in current situations. Flight schedule and flight plan could be employed to detect flight delay causalities and model flight delay propagations more accurately. Real flight data may be utilized to validate and revise the detection and prediction models. (3) It is of great importance to explore how to predict flight delay propagations and identify best spreaders at a low cost of calculation complexity. This may be achieved by analyzing flight delay in frequency domain instead of time domain. (4) Summation of most critical nodes/edges may not be the most crucial group to network resilience or flight delay propagations. Effective algorithm for most influential sequence is to be developed.

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