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Minter, Amanda and Retkute, Renata (2019) Approximate Bayesian computation for infectious disease modelling. Epidemics . 100368. doi:10.1016/j.epidem.2019.100368
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Official URL: https://doi.org/10.1016/j.epidem.2019.100368
Abstract
Approximate Bayesian Computation (ABC) techniques are a suite of modelfitting methods which can be im-plemented without a using likelihood function. In order to use ABC in a time-efficient manner users must makeseveral 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 usersto make more informed decisions when planning analyses. In this paper, we present an introduction to ABC witha focus of application to infectious disease models. We present a tutorial on coding practice for ABC in R andthree case studies to illustrate the application of ABC to infectious disease models.
| Item Type: | Journal Article | ||||||
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| Divisions: | Faculty of Science > Mathematics | ||||||
| Journal or Publication Title: | Epidemics | ||||||
| Publisher: | Elsevier BV | ||||||
| ISSN: | 1755-4365 | ||||||
| Official Date: | 25 September 2019 | ||||||
| Dates: |
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| Article Number: | 100368 | ||||||
| DOI: | 10.1016/j.epidem.2019.100368 | ||||||
| Status: | Peer Reviewed | ||||||
| Publication Status: | Published | ||||||
| Access rights to Published version: | Open Access | ||||||
| Open Access Version: |
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