Modeling the spread of infectious diseases through influence maximization

Area of Science:

• Computational epidemiology
• Network science
• Mathematical modeling

Background:

• Traditional models (compartmental, agent-based) often overlook social network structures in disease transmission.
• Understanding the impact of social connections is crucial for effective public health interventions.

Purpose of the Study:

• To adapt the influence maximization problem for modeling infectious disease spread within social networks.
• To incorporate network structure, infection probabilities, and dynamic individual behaviors into disease transmission models.

Main Methods:

• Utilized influence maximization framework to model disease spread.
• Analyzed social network structures and individual infection probabilities.
• Formulated models using integer optimization.
• Validated models with simulations on random and real-world (COVID-19) networks.

Main Results:

• Demonstrated the effectiveness of the proposed influence maximization-based models.
• Showcased the ability to integrate network structure and behavioral dynamics.
• Provided insights into the relationship between proposed models and traditional compartmental models.

Conclusions:

• The adapted influence maximization approach offers a powerful tool for computational epidemiology.
• Integrating social network structure and dynamic behaviors enhances disease spread modeling realism.
• This method provides a valuable alternative/complement to existing epidemiological models.