Efficient Bayesian model choice for partially observed processes: with application to an experimental transmission study of an infectious disease
McKinley, TJ; Neal, P; Spencer, SEF; et al.Conlan, AJK; Tiley, L
Date: 2 October 2019
Journal
Bayesian Analysis
Publisher
International Society for Bayesian Analysis (ISBA)
Publisher DOI
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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. ...
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.
Mathematics and Statistics
Faculty of Environment, Science and Economy
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