An immunization method using a context-based centrality in multiplex networks

This is very important to prioritize nodes for immunization in controlling infectious disease outbreaks. In this paper, we propose a new immunization strategy for multiplex networks; we specifically model two separate layers: the physical layer where infection propagates and the virtual layer where information is transmitted. We assume that each layer has a different “context” and use that to identify the most suitable centrality measure for each. For the infection layer, we choose PageRank, as it has shown certain effectiveness in determining those nodes crucial for reducing transmission. For the awareness layer, we show how closeness centrality is a better measure of quality for the passing of information along short paths. We, therefore, propose Multiplex Combined PageRank, or MCPR, combining the centralities from both layers to immunize the most important nodes. The simulations employ the extended SIR-UA model, which exploits the interaction between infection and awareness dynamics, to scenarios on measles and smallpox. Validation on both synthetic networks and the real-world Copenhagen Networks Study dataset demonstrates consistent superiority of MCPR over classical methods. In terms of epidemic size in simulations with very limited immunization budgets, MCPR indeed resulted in better outcomes than the single-layer PageRank immunization strategy and the existing Multiplex PageRank method. Real-world validation shows epidemic size reductions of 2.2% for measles and 7% for smallpox at 10% immunization coverage, with parameter optimization yielding improvements up to 9.5%. The sensitivity analysis demonstrates that increasing transmission of awareness and the quality of information can help control the infection immensely.