Abstract:
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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. The fact that the underlying transmission models exhibit complex non-linear dynamics, coupled with practical constraints on observing key epidemiological events such as infections, makes robust inference challenging. Any solution to the inference problem requires the use of methodologies that can integrate over multiple hidden states and infer missing information. Simulation-based techniques such as Approximate Bayesian Computation (ABC) show great promise in this area, since they rely only on the development of suitable simulation models. In this talk 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 inference and model comparison in a Bayesian framework. We illustrate these approaches on both simulated data and real data from an experimental transmission study of highly pathogenic avian influenza in genetically modified chickens.
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