A computational framework for modeling and studying pertussis epidemiology and vaccination

Area of Science:

• Epidemiology
• Computational Biology
• Bioinformatics

Background:

• Emerging infectious diseases pose significant public health challenges, necessitating advanced tools for understanding disease dynamics.
• Computational models and simulations are crucial for epidemiologists to predict disease spread and inform control strategies.
• Developing accessible mathematical models for disease analysis remains a challenge for researchers lacking advanced computational skills.

Purpose of the Study:

• To introduce a novel, user-friendly modeling framework for epidemiological systems.
• To enhance the accessibility of complex computational modeling for a broader range of researchers.
• To provide a robust and reproducible platform for epidemiological analysis.

Main Methods:

• Utilized a graphical formalism (Petri Nets) for simplified model creation.
• Implemented an R package with a user-friendly interface for accessing analysis techniques.
• Employed Docker containerization for enhanced portability and reproducibility of analyses.
• Established a well-defined schema for integrating custom analysis workflows.

Main Results:

• Demonstrated the framework's effectiveness through a case study on pertussis epidemiology in Italy.
• The framework simplifies model creation and analysis through its graphical interface and R package.
• Containerization ensures high portability and reproducibility of all implemented analysis techniques.

Conclusions:

• A new general modeling framework for epidemiological systems has been developed, integrating Petri Nets, R, and Docker.
• The framework is designed for accessibility, enabling researchers without advanced computational skills to perform sophisticated analyses.
• Adherence to Reproducible Bioinformatics Project guidelines ensures reproducible analyses and facilitates the development of new user-defined workflows.