Taking precautions before or during the start of a virus outbreak can heavilyreduce the number of infected. The question which individuals should beimmunized in order to mitigate the impact of the virus on the rest ofpopulation has received quite some attention in the literature. The dynamics ofthe of a virus spread through a population is often represented as informationspread over a complex network. The strategies commonly proposed to determinewhich nodes are to be selected for immunization often involve only onecentrality measure at a time, while often the topology of the network seems tosuggest that a single metric is insufficient to capture the influence of a nodeentirely. In this work we present a generic method based on a genetic algorithm (GA)which does not rely explicitly on any centrality measures during its search butonly exploits this type of information to narrow the search space. The fitnessof an individual is defined as the estimated expected number of infections of avirus following SIR dynamics. The proposed method is evaluated on two contactnetworks: the Goodreau's Faux Mesa high school and the US air transportationnetwork. The GA method manages to outperform the most common strategies basedon a single metric for the air transportation network and its performance iscomparable with the best performing strategy for the high school network.
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