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Area of Science:

  • Epidemiology
  • Computational Biology
  • Data Science

Background:

  • Epidemiological models are crucial for understanding disease spread, but fitting their parameters can be complex.
  • Current methods often intertwine fitting algorithms with specific models, hindering direct comparison of model performance.
  • This limitation complicates the selection of the most appropriate model for a given epidemic scenario.

Purpose of the Study:

  • To develop a novel, model-agnostic framework for the parameter fitting of epidemic models.
  • To enable fair and robust comparison between different epidemiological models.
  • To provide a flexible and user-friendly tool for researchers in computational epidemiology.

Main Methods:

  • Developed a Python framework that accepts any epidemic model as a Python function.
  • Implemented automatic configuration for parameter fitting, ensuring high-quality fits.
  • Designed the framework to be adaptable to models written in various programming languages.

Main Results:

  • The framework successfully fits parameters for diverse epidemic models.
  • It allows for equitable comparison of different models without compromising parameter estimation quality.
  • Provided example implementations of four concrete epidemic models with accompanying data.

Conclusions:

  • The model-agnostic framework facilitates standardized and reliable comparison of epidemiological models.
  • This tool enhances the ability to select optimal models for public health interventions.
  • The open-source code and documentation are available for broader research community adoption.