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Efficient Bayesian model choice for partially observed processes : with application to an experimental transmission study of an infectious disease

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McKinley, Trevelyan J., Neal, Peter, Spencer, Simon E. F., Conlan, Andrew J. K. and Tiley, Laurence (2019) Efficient Bayesian model choice for partially observed processes : with application to an experimental transmission study of an infectious disease. Bayesian Analysis . doi:10.1214/19-BA1174

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Official URL: https://projecteuclid.org/euclid.ba/1569981633

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Abstract

Infectious diseases such as avian influenza pose a global threat to human health. Mathematical and statistical models can provide key insights into the mechanisms that underlie the spread and persistence of infectious diseases, though their utility is linked to the ability to adequately calibrate these models to observed data. Performing robust inference for these systems is challenging. The fact that the underlying models exhibit complex non-linear dynamics, coupled with practical constraints to observing key epidemiological events such as transmission, requires the use of inference techniques that are able to numerically integrate over multiple hidden states and/or infer missing information. Simulation-based inference techniques such as Approximate Bayesian Computation (ABC) have shown great promise in this area, since they rely on the development of suitable simulation models, which are often easier to code and generalise than routines that require evaluations of an intractable likelihood function. In this manuscript we make some contributions towards improving the efficiency of ABC-based particle Markov chain Monte Carlo methods, and show the utility of these approaches for performing both model inference and model comparison in a Bayesian framework. We illustrate these approaches on both simulated data, as well as real data from an experimental transmission study of highly pathogenic avian influenza in genetically modified chickens.

Item Type: Journal Article
Subjects: Q Science > QA Mathematics
R Medicine > RC Internal medicine
Divisions: Faculty of Science, Engineering and Medicine > Science > Statistics
Library of Congress Subject Headings (LCSH): Bayesian statistical decision theory, Communicable diseases -- Mathematical models, Partially ordered sets -- Research, Markov processes, Monte Carlo method
Journal or Publication Title: Bayesian Analysis
Publisher: International Society for Bayesian Analysis
ISSN: 1931-6690
Official Date: 2 October 2019
Dates:
DateEvent
2 October 2019Published
19 July 2019Accepted
DOI: 10.1214/19-BA1174
Status: Peer Reviewed
Publication Status: Published
Access rights to Published version: Restricted or Subscription Access
Copyright Holders: 2019 International Society for Bayesian Analysis
RIOXX Funder/Project Grant:
Project/Grant IDRIOXX Funder NameFunder ID
MR/P026400/1 [MRC] Medical Research Councilhttp://dx.doi.org/10.13039/501100000265
EP/R018561/1[EPSRC] Engineering and Physical Sciences Research Councilhttp://dx.doi.org/10.13039/501100000266
BB/G00479X/1[BBSRC] Biotechnology and Biological Sciences Research Councilhttp://dx.doi.org/10.13039/501100000268
BBS/B/00239[BBSRC] Biotechnology and Biological Sciences Research Councilhttp://dx.doi.org/10.13039/501100000268
BBS/B/00301[BBSRC] Biotechnology and Biological Sciences Research Councilhttp://dx.doi.org/10.13039/501100000268
VT0105University of Cambridgehttp://viaf.org/viaf/153568718
VT0105Department for Environment, Food and Rural Affairshttp://dx.doi.org/10.13039/501100000277
VT0105Higher Education Funding Council for Englandhttp://dx.doi.org/10.13039/100011722
BB/I024550/1[BBSRC] Biotechnology and Biological Sciences Research Councilhttp://dx.doi.org/10.13039/501100000268
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