Approximate Bayesian Computation for infectious disease modelling

Amanda Minter; Renata Retkute

doi:10.1016/j.epidem.2019.100368

Approximate Bayesian Computation for infectious disease modelling

Epidemics. 2019 Dec:29:100368. doi: 10.1016/j.epidem.2019.100368. Epub 2019 Sep 25.

Authors

Amanda Minter¹, Renata Retkute²

Affiliations

¹ Centre for the Mathematical Modelling of Infectious Diseases, London School of Hygiene and Tropical Medicine, London, UK. Electronic address: amanda.minter@lshtm.ac.uk.
² The Zeeman Institute for Systems Biology and Infectious Disease Epidemiology Research, University of Warwick, UK.

PMID: 31563466
DOI: 10.1016/j.epidem.2019.100368

Abstract

Approximate Bayesian Computation (ABC) techniques are a suite of model fitting methods which can be implemented without a using likelihood function. In order to use ABC in a time-efficient manner users must make several design decisions including how to code the ABC algorithm and the type of ABC algorithm to use. Furthermore, ABC relies on a number of user defined choices which can greatly effect the accuracy of estimation. Having a clear understanding of these factors in reducing computation time and improving accuracy allows users to make more informed decisions when planning analyses. In this paper, we present an introduction to ABC with a focus of application to infectious disease models. We present a tutorial on coding practice for ABC in R and three case studies to illustrate the application of ABC to infectious disease models.

Keywords: Approximate Bayesian Computation; Epidemic model; R; Spatial model; Stochastic model.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Algorithms
Bayes Theorem*
Communicable Diseases / epidemiology*
Communicable Diseases / transmission*
Computer Simulation
Data Collection
Humans
Likelihood Functions
Models, Statistical*