Mobility-based SIR model for complex networks: with case study Of COVID-19

Area of Science:

• Epidemiology
• Mathematical Modeling
• Network Science

Background:

• Pandemics like COVID-19 highlight the need for effective spread prediction models.
• Global connectivity and non-uniform population distribution significantly influence epidemic trajectories.

Purpose of the Study:

• To develop and validate advanced mobility-based SIR models for pandemic spread analysis.
• To account for real-life interactions, population distribution, and global connectivity in epidemic modeling.

Main Methods:

• Proposed two versions of a mobility-based SIR model: fully mixed and complex networks.
• Incorporated population distribution, geographic connectivity, and individual network data.
• Performed simulations with synthetic data and applied models to real-world COVID-19 case forecasting.

Main Results:

• The proposed models effectively integrate population distribution and connectivity for enhanced pandemic spread prediction.
• Demonstrated generalization capabilities through extensive simulations.
• Successfully forecasted COVID-19 cases at county and regional levels in Estonia and France.

Conclusions:

• The novel mobility-based SIR models offer a significant advancement in understanding and forecasting pandemic dynamics.
• The models provide valuable tools for public health agencies in pandemic preparedness and response.
• This approach is adaptable for predicting the spread of various infectious diseases.