Approximate Bayesian Computation for infectious disease modelling

• Published in: Epidemics Vol. 29; p. 100368
• Main Authors: Minter, Amanda, Retkute, Renata
• Format: Journal Article
• Language: English
• Published: Netherlands Elsevier B.V 01.12.2019
• Subjects: Approximate Bayesian Computation; Epidemic model; Infectious Disease; Internal Medicine; Spatial model; Stochastic model; NN; Approximate Bayesian Computation; Stochastic model; ABC; R; SMC; Spatial model; Epidemic model; Sequential Monte Carlo; nearest neighbour
• ISSN: 1755-4365; 1878-0067; 1878-0067
• DOI: 10.1016/j.epidem.2019.100368

•We provide an introductory tutorial in using Approximate Bayesian Computation in R.•We investigate specific ABC choices required to tune the algorithm.•Three case studies of ABC applied to infectious disease models are presented.•The summary statistic and model must be chosen to align with the data available.•Smaller perturbation variance improved computational efficiency of ABC SMC. 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.