A heuristic optimization method for mitigating the impact of a virus attack

TL;DR

This paper introduces a genetic algorithm-based heuristic to select individuals for immunization during a virus outbreak, outperforming traditional single-metric strategies on complex networks.

Contribution

A novel GA-based method that combines multiple network insights without relying solely on single centrality measures for virus mitigation.

Findings

Outperforms single-metric strategies on the US air transportation network.

Achieves comparable results to best strategies on the high school network.

Demonstrates effectiveness of combining network information in immunization strategies.

Abstract

Taking precautions before or during the start of a virus outbreak can heavily reduce the number of infected. The question which individuals should be immunized in order to mitigate the impact of the virus on the rest of population has received quite some attention in the literature. The dynamics of the of a virus spread through a population is often represented as information spread over a complex network. The strategies commonly proposed to determine which nodes are to be selected for immunization often involve only one centrality measure at a time, while often the topology of the network seems to suggest that a single metric is insufficient to capture the influence of a node entirely. 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 but only exploits this type of information to narrow the search space. The fitness of an individual is defined as the estimated expected number of infections of a virus following SIR dynamics. The proposed method is evaluated on two contact networks: the Goodreau's Faux Mesa high school and the US air transportation network. The GA method manages to outperform the most common strategies based on a single metric for the air transportation network and its performance is comparable with the best performing strategy for the high school network.