Identifying influential spreaders in complex networks for disease spread and control.

Area of Science:

• Network Science
• Epidemiology
• Computational Social Science

Background:

• Identifying influential spreaders is vital for controlling information and disease dissemination in complex networks.
• Node importance ranking is challenging due to network complexity and dynamic variations.

Purpose of the Study:

• To evaluate the effectiveness of different centrality metrics (betweenness, degree, H-index, coreness) for identifying influential spreaders.
• To determine optimal target immunization strategies based on these metrics for disease control.

Main Methods:

• Utilized betweenness, degree, H-index, and coreness to measure node centrality.
• Constructed disease spreading models and target immunization strategies.
• Conducted numerical simulations on six networks (four real, two BA scale-free).

Main Results:

• Betweenness centrality demonstrated the widest propagation and smallest epidemic threshold across all simulated networks.
• Betweenness-based immunization was most effective for BA scale-free networks but less so for real networks.
• Degree-based immunization proved most effective for the four real networks studied.

Conclusions:

• Target immunization strategies should be tailored to network structure; betweenness centrality is optimal for standard scale-free networks.
• Degree centrality is more effective for real-world and non-standard scale-free networks.
• Findings offer insights into selecting appropriate node importance metrics for disease transmission and control.