  Controlling and understanding epidemic outbreaks has recently drawn great
interest in a large spectrum of research communities. Vaccination is one of the
most well-established and effective strategies in order to contain an epidemic.
In the present study, we investigate a network-based virus-spreading model
building on the popular SIR model. Furthermore, we examine the efficacy of
various vaccination strategies in preventing the spread of infectious diseases
and maximizing the survival ratio. The experimented strategies exploit a wide
range of approaches such as relying on network structure centrality measures,
focusing on disease-spreading parameters, and a combination of both. Our
proposed hybrid algorithm, which combines network centrality and illness
factors, is found to perform better than previous strategies in terms of
lowering the final death ratio in the community on various real-world networks
and synthetic graph models. Our findings particularly emphasize the
significance of taking both network structure properties and disease
characteristics into account when devising effective vaccination strategies.
