  Background: Statisticians evaluating the impact of policy interventions such
as screening or vaccination will need to make use of mathematical and
computational models of disease progression and spread. Calibration is the
process of identifying the parameters of these models, with a Bayesian
framework providing a natural way in which to do this in a probabilistic
fashion. Markov Chain Monte Carlo (MCMC) is one of a number of computational
tools that is useful in carrying out this calibration. Objective: In the
context of complex models in particular, a key problem that arises is one of
non-identifiability. In this setting, one approach which can be used is to
consider and ensure that appropriately informative priors are specified on the
joint parameter space. We give examples of how this arises and may be addressed
in practice. Methods: Using a basic SIS model the calibration process and the
associated challenge of non-identifiability is discussed. How this problem
arises in the context of a larger model for HPV and cervical cancer is also
illustrated. Results: The conditions which allow the problem of
non-identifiability to be resolved are demonstrated for the SIS model. For the
larger HPV model, how this impacts on the calibration process is also
discussed.
