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Bayesian workflow for disease transmission modeling in Stan.

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Summary
This summary is machine-generated.

This tutorial guides researchers in building and validating disease transmission models using Stan, a Bayesian framework ideal for infectious disease research, including the SARS-CoV-2 pandemic.

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Area of Science:

  • Epidemiology
  • Computational Biology
  • Biostatistics

Background:

  • Bayesian modeling offers a robust framework for quantifying uncertainty in disease transmission.
  • Stan, a probabilistic programming language, simplifies complex model development and enhances transparency.
  • Accurate infectious disease modeling is crucial for understanding and controlling pandemics like SARS-CoV-2.

Purpose of the Study:

  • To provide a comprehensive tutorial on constructing, fitting, and critically evaluating disease transmission models using Stan.
  • To demonstrate the application of Bayesian inference and Stan for infectious disease modeling, with a focus on SARS-CoV-2.
  • To equip researchers with advanced techniques for sophisticated model development and validation.

Main Methods:

  • Utilizing Stan for Bayesian inference in compartmental disease transmission models.
  • Implementing and diagnosing a susceptible-infected-recovered (SIR) model.
  • Developing and fitting a more complex transmission model relevant to the SARS-CoV-2 pandemic.

Main Results:

  • Demonstrated the practical application of Stan for building and fitting epidemiological models.
  • Showcased the utility of Hamiltonian Monte Carlo sampling for reliable inference and diagnostics.
  • Illustrated advanced techniques including model simulation and scaling ODE-based models.

Conclusions:

  • Stan provides a powerful, transparent, and extensible platform for Bayesian infectious disease modeling.
  • The tutorial effectively guides researchers in applying Stan for both basic and advanced epidemiological modeling tasks.
  • This approach enhances the ability to model and understand disease dynamics, aiding pandemic preparedness and response.