Novel epidemic models on PSO-based networks

Area of Science:

• Epidemiology
• Network Science
• Mathematical Modeling

Background:

• Understanding epidemic dynamics in complex networks is crucial.
• Existing models often overlook the influence of network structure on spread.
• Spatio-temporal factors significantly impact disease transmission.

Purpose of the Study:

• To propose novel spatio-temporal epidemic network models.
• To incorporate popularity and similarity optimization (PSO) in network formation.
• To analyze epidemic spread considering both spatial and temporal dimensions.

Main Methods:

• Developed two models: r-SI and r-SIS, integrating popularity and similarity.
• Modeled spatial spread using diffusion equations.
• Modeled temporal spread using the logistic map.
• Represented models using partial differential equations for analytical and simulation purposes.

Main Results:

• Simulations on artificial and real networks demonstrated model effectiveness.
• The proposed models successfully capture spatio-temporal epidemic dynamics.
• Integration of PSO in network formation influences epidemic spread patterns.

Conclusions:

• The r-SI and r-SIS models provide a robust framework for studying epidemics in evolving networks.
• Popularity and similarity are key factors in spatio-temporal epidemic network modeling.
• The models are computationally tractable and validated through simulations.