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    This study models global infectious disease spread using network theory. It identifies key countries and proposes a control framework to prevent pandemics like COVID-19.

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    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.