Effective vaccination strategies in network-based SIR model

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. •We developed an epidemic spreading model in networks using SIR model and simulated epidemics on Social Networks and synthetic graphs.•We proved vaccination is a NP-Hard problem and hence, we implemented several vaccination strategies and measured their efficiency.•We investigate the use of network structure centrality measures and disease-spreading parameters, as well as a combination of both, to maximize the survival ratio and prevent the spread of infectious diseases.•We also proposed a hybrid algorithm, which combines both network centrality and illness factors, outperforms previous strategies in terms of lowering the final death ratio in the community on various real-world networks and synthetic graph models.