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Implementing strategies targeting specific high-degree or high-betweenness nodes in epidemic networks significantly reduces outbreak sizes.
* The study uses a disease-spreading meta-population model with testkits to analyze how migration affects infection dynamics across different communities.
* The research finds that nodes with and without testkits separate into two clusters at low migration rates but merge into one cluster at higher rates, validating this behavior through extensive simulations in various network types.
Article Abstract
In epidemic networks, it has been demonstrated that implementing any intervention strategy on nodes with specific characteristics (such as a high degree or node betweenness) substantially diminishes the outbreak size. We extend this finding with a disease-spreading meta-population model using testkits to explore the influence of migration on infection dynamics within the distinct communities of the network. Notably, we observe that nodes equipped with testkits and no testkits tend to segregate into two separate clusters when migration is low, but above a critical migration rate, they coalesce into one single cluster. Based on this clustering phenomenon, we develop a reduced model and validate the emergent clustering behavior through comprehensive simulations. We observe this property in both homogeneous and heterogeneous networks.
