Time dependent correlations between the probability of a node being infected and its centrality measures

Area of Science:

• Epidemiology
• Network Science
• Computational Biology

Background:

• Pandemics pose a significant global threat, impacting human life, economies, and well-being.
• The COVID-19 pandemic highlighted the urgent need for effective global disease spread prevention strategies.

Purpose of the Study:

• To develop an intervention methodology for preventing the global spread of infectious diseases.
• To integrate real-time infection data with social network structures for predictive modeling.

Main Methods:

• Utilized Susceptible-Infectious-Recovered (SIR) model simulations on various network datasets.
• Employed seven centrality measures to quantify node importance within social networks.
• Analyzed time-dependent correlations between node infection probability and centrality values.

Main Results:

• Correlation between node infection probability and centrality peaks in the early epidemic stages.
• This peak correlation allows for the prediction of node infection probability.
• The findings indicate that early-stage analysis is crucial for effective intervention.

Conclusions:

• The proposed method enables early prediction of infection spread dynamics.
• Identifying high-risk nodes through correlation analysis facilitates targeted interventions.
• This approach offers a proactive strategy for mitigating global pandemic threats.