Critical paths in a metapopulation model of H1N1: Efficiently delaying influenza spreading through flight cancellation
Jose Marcelino, Marcus Kaiser

TL;DR
This study demonstrates that targeted flight connection cancellations based on betweenness centrality are more effective and efficient in delaying influenza spread than shutting down entire airports, using a detailed SEIR metapopulation model.
Contribution
The paper introduces a novel strategy of cancelling specific flight connections rather than entire airports to control influenza spread, showing improved effectiveness and efficiency.
Findings
Cancelling 25% of flight connections reduces infections by 37%.
Targeted connection cancellations outperform airport shutdowns in reducing spread.
Efficient control achieved by removing highly ranked connections based on betweenness centrality.
Abstract
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…
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