Minimizing the spread of infections in local and metapopulation networks by removing a set of links

This thesis evaluates the performance of different link removal methods to minimize the spread of infections in local and metapopulation networks. The aim is to replicate the results of Nandi and Medal (2016) and to extend the analysis to a metapopulation network. For the local network, we study generated scale-free networks and for the metapopulation network we study a global airline network. We examine the link removal methods MinConnect, MinAtRisk, MinPaths and MinWPaths proposed by Nandi and Medal (2016) and compare these with the Random method. As these methods use indirect metrics, the spread of infection is measured using simulation. We find that MinConnect is in general most effective to maximize the time to infect half of the susceptible nodes in a scale-free network, whereas MinAtRisk is the best method to minimize the number of new infections. Most results are in line with the findings of Nandi and Medal (2016). One exception is that MinWPaths performs worse than expected in slowing down the speed of spread. For the global airline network, MinWPaths performs best. We conclude that the performance of the link removal methods strongly depends on the network structure, the type of infection and some other factors.