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Approximate Bayesian computation for infectious disease modelling

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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 ISSN 1755-4365.

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Official URL: https://doi.org/10.1016/j.epidem.2019.100368

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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
Subjects: Q Science > QA Mathematics
R Medicine > RA Public aspects of medicine
Divisions: Faculty of Science, Engineering and Medicine > Science > Mathematics
Library of Congress Subject Headings (LCSH): Communicable diseases , Communicable diseases -- Epidemiology -- Mathematical models, Bayesian statistical decision theory, Mathematical analysis
Journal or Publication Title: Epidemics
Publisher: Elsevier BV
ISSN: 1755-4365
Official Date: 25 September 2019
Dates:
DateEvent
25 September 2019Available
30 August 2019Accepted
Article Number: 100368
DOI: 10.1016/j.epidem.2019.100368
Status: Peer Reviewed
Publication Status: Published
Access rights to Published version: Open Access (Creative Commons)
RIOXX Funder/Project Grant:
Project/Grant IDRIOXX Funder NameFunder ID
HPRU-2012–10080[NIHR] National Institute for Health Researchhttp://dx.doi.org/10.13039/501100000272
HPRU-2012–10080Public Health Englandhttp://dx.doi.org/10.13039/501100002141
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  • https://www.sciencedirect.com/science/ar...

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