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Using Symmetry to Enhance the Performance of Agent-Based Epidemic Models.

Gilberto M Nakamura, Alinne C C Souza, Francisco C M Souza

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    This study introduces spin symmetry methods to simplify disease spread analysis in agent-based models. These novel algorithms significantly enhance computational efficiency for epidemic simulations.

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    Area of Science:

    • Computational Physics
    • Epidemiology
    • Mathematical Modeling

    Background:

    • Symmetries represent system invariance under transformations, simplifying computations.
    • Efficient analysis of epidemic spread is crucial for public health.
    • Agent-based models are powerful tools for simulating disease dynamics.

    Purpose of the Study:

    • To propose and implement novel spin symmetry methods for agent-based epidemic models.
    • To demonstrate the computational efficiency gains of these symmetry-based algorithms.
    • To compare the performance against traditional simulation techniques.

    Main Methods:

    • Development of two distinct algorithms leveraging spin symmetries.
    • Integration of these algorithms into an agent-based epidemic model.
    • Performance evaluation using Monte Carlo simulation and exact Markov process.

    Main Results:

    • Symmetry-based algorithms significantly reduce computational effort.
    • Demonstrated performance improvements in Monte Carlo simulations.
    • Validated efficiency gains for the exact Markov process.

    Conclusions:

    • Spin symmetry implementation offers a powerful approach to accelerate epidemic modeling.
    • The proposed methods provide substantial computational advantages over traditional approaches.
    • This work paves the way for more efficient analysis of disease dynamics.