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