Graph Theory Approach for the Control of COVID-19 Diffusion

Area of Science:

• Epidemiology
• Network Science
• Computational Biology

Background:

• Pandemics spread globally through infectious diseases.
• Non-pharmaceutical interventions (NPIs) can curb outbreaks but are complex to implement.
• COVID-19 highlighted the need for computational strategies to control global disease spread.

Purpose of the Study:

• To develop a computational model for COVID-19 spread networks.
• To identify influential countries in global disease transmission.
• To propose a network theory-based control framework to mitigate pandemic spread.

Main Methods:

• Modeled COVID-19 spread using daily confirmed cases per country.
• Utilized mutual information to quantify interdependence between countries.
• Applied graph theory to identify key nodes (countries) in the spread network.

Main Results:

• Identified key countries crucial to the COVID-19 spread network.
• Developed a control framework based on network theory principles.
• Demonstrated the framework's potential for preventing disease transmission.

Conclusions:

• The proposed network model and control framework show promise for preventing global infectious disease spread.
• Prompt implementation of such computational strategies is vital for pandemic control.
• This approach can be applied to future outbreaks beyond COVID-19.