Quarantine Centrality: Principal Component Analysis of SIS Model Simulation Results to Quantify the Vulnerability of Nodes to Stay Infected in Complex Networks

We propose a novel simulations-based centrality metric to proactively identify and quarantine topologically vulnerable nodes that are more likely to stay infected (due to repeated infections from the neighbors) for a longer time during an epidemic spread per the SIS (Susceptible-Infected-Susceptible) model wherein a node does not get immunity after recovering from an infection and again becomes susceptible for infection. Referred to as the Quarantine Centrality, it is the first such simulations-based centrality metric proposed in the literature and is computed as follows: First, conduct multiple in-situ simulation runs of the SIS model on the complex real-world network to build a dataset that records the number of rounds the nodes stay infected in each simulation run and then run PCA (principal component analysis) on the dataset to quantify (the Quarantine Centrality metric) and rank the nodes with respect to the extent they could stay infected during an SIS-style epidemic spread.