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Approximate Bayesian Computation for infectious disease modelling.

Amanda Minter1, Renata Retkute2

  • 1Centre for the Mathematical Modelling of Infectious Diseases, London School of Hygiene and Tropical Medicine, London, UK.

Epidemics
|September 30, 2019
PubMed
Summary
This summary is machine-generated.

Approximate Bayesian Computation (ABC) methods offer a way to fit models without likelihood functions. This guide helps users optimize ABC for infectious disease modeling, improving accuracy and efficiency.

Keywords:
Approximate Bayesian ComputationEpidemic modelRSpatial modelStochastic model

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Area of Science:

  • Computational Biology
  • Epidemiology
  • Statistical Modeling

Background:

  • Approximate Bayesian Computation (ABC) is a class of statistical inference methods.
  • ABC methods are valuable when likelihood functions are intractable or computationally expensive.
  • Effective implementation of ABC requires careful consideration of algorithmic choices and user-defined parameters.

Purpose of the Study:

  • To provide an introduction to Approximate Bayesian Computation (ABC) techniques.
  • To focus the application of ABC methods on infectious disease models.
  • To guide users in making informed decisions for efficient and accurate ABC analyses.

Main Methods:

  • The study introduces Approximate Bayesian Computation (ABC) techniques.
  • A tutorial on coding practices for ABC in the R programming language is presented.
  • Three case studies demonstrate the application of ABC to infectious disease models.

Main Results:

  • Understanding design choices in ABC algorithms is crucial for reducing computation time.
  • User-defined parameters significantly impact the accuracy of estimation in ABC.
  • The presented R coding practices and case studies facilitate practical application of ABC.

Conclusions:

  • Approximate Bayesian Computation (ABC) provides a viable alternative for model fitting without likelihoods.
  • Informed decisions regarding ABC algorithm design and parameter choices enhance analytical efficiency and accuracy.
  • The tutorial and case studies serve as a practical resource for applying ABC in infectious disease modeling.