The role of the airline transportation network in the prediction and predictability of global epidemics

By Vittoria Colizza, Alain Barrat, Marc Barthelemy, Alessandro Vespignani. Proceedings of the National Academy of Sciences of the United States of America, 2006

1,030 citations recorded by the source corpus.

Subfields: Modeling and Simulation, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/6013996efadd0061e9696c736d2cc55a78dd6991

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Citations in the corpus

Human mobility: Models and applications (2017, background): “Whilst it has been known for some time that air transportation plays a major role in the global spreading of diseases [342, 343], the implementation of air traffic data in epidemic models has only been possible in recent times with the advances in information technologies and computing resources ove”
Networks and the Epidemiology of Infectious Disease (2010, methodology): “The movement of passenger aircraft as collated by the International Air Transport Association (IATA) provides very useful information about the long-distance movement of individuals and hence how rapidly infection is likely to travel around the globe [37, 45, 46].”
Collective Response of Human Populations to Large-Scale Emergencies (2011, neutral): “[1], such as life-threatening epidemic outbreaks [4,12], emergencies and traffic anomalies, as models based on stationary events are expected to break down under these circumstances.”
Analysis of the Chinese Airline Network as multi-layer networks (2016, neutral): “global pattern of emerging diseases [16].”
Graph theory and networks in Biology. (2006, background): “In [252] a computational SIR-type model of global epidemic spread is presented that is based on real air-transportation and census data.”
Comparing large-scale computational approaches to epidemic modeling: Agent-based versus structured metapopulation models (2010, background): “In particular, in each city j the number of passengers traveling on each connection j T l at time t defines a set of stochastic variables that follow a multinomial distribution [22].”
Statistical physics of vaccination (2016, background): “On the other hand, such a framework can be well-integrated with empirical data, such as the control of an infectious disease pandemic driven by air travel [776].”
Uncovering space-independent communities in spatial networks (2010, background): “This observation underlines the need for appropriate metrics for the analysis and modeling of networks where spatial constraints play an important role [26, 27, 28].”
Graph Signal Processing: Overview, Challenges, and Applications (2017, background): “To study these processes [18], [19] one usually considers one of two asymptotic regimes: (1) long term behavior (time-asymptotics), attempting to find the equilibrium distribution of the process [20], [21]; or (2) large network asymptotics (mean-field approximation) [22] leading”