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Disease spreading through human travel networks has been a topic of greatinterest in recent years, as witnessed during outbreaks of influenza A (H1N1)or SARS pandemics. One way to stop spreading over the airline network aretravel restrictions for major airports or network hubs based on the totalnumber of passengers of an airport. Here, we test alternative strategies usingedge removal, cancelling targeted flight connections rather than restrictingtraffic for network hubs, for controlling spreading over the airline network.We employ a SEIR metapopulation model that takes into account the population ofcities, simulates infection within cities and across the network of the top 500airports, and tests different flight cancellation methods for limiting thecourse of infection. The time required to spread an infection globally, assimulated by a stochastic global spreading model was used to rank the candidatecontrol strategies. The model includes both local spreading dynamics at thelevel of populations and long-range connectivity obtained from real globalairline travel data. Simulated spreading in this network showed that spreadinginfected 37% less individuals after cancelling a quarter of flight connectionsbetween cities, as selected by betweenness centrality. The alternative strategyof closing down whole airports causing the same number of cancelled connectionsonly reduced infections by 18%. In conclusion, selecting highly ranked singleconnections between cities for cancellation was more effective, resulting infewer individuals infected with influenza, compared to shutting down wholeairports. It is also a more efficient strategy, affecting fewer passengerswhile producing the same reduction in infections.  The network of connections between the top 500 airports is available underthe resources link on our website http://www.biological-networks.org.

Critical paths in a metapopulation model of H1N1: Efficiently delaying influenza spreading through flight cancellation