Davidson, R.S., McKendrick, I.J., Wood, J.C., Marion, G., Greig, A., Stevenson, K., Sharp, J.M. and Hutchings, M.R.
BMC Veterinary Research 8, 159.
A common approach to the application of epidemiological models is to determine a single (point estimate)
parameterisation using the information available in the literature. However, in many cases there is considerable
uncertainty about parameter values, reflecting both the incomplete nature of current knowledge and natural
variation, for example between farms. Furthermore model outcomes may be highly sensitive to different
parameter values. Paratuberculosis is an infection for which many of the key parameter values are poorly
understood and highly variable, and for such infections there is a need to develop and apply statistical
techniques which make maximal use of available data.
A technique based on Latin hypercube sampling combined with a novel reweighting method was developed
which enables parameter uncertainty and variability to be incorporated into a model-based framework for
estimation of prevalence. The method was evaluated by applying it to a simulation of paratuberculosis in dairy
herds which combines a continuous time stochastic algorithm with model features such as within herd
variability in disease development and shedding, which have not been previously explored in paratuberculosis
models. Generated sample parameter combinations were assigned a weight, determined by quantifying the
model's resultant ability to reproduce prevalence data. Once these weights are generated the model can be
used to evaluate other scenarios such as control options. To illustrate the utility of this approach these
reweighted model outputs were used to compare standard test and cull control strategies both individually and
in combination with simple husbandry practices that aim to reduce infection rates.
The technique developed has been shown to be applicable to a complex model incorporating realistic control
options. For models where parameters are not well known or subject to significant variability, the reweighting
scheme allowed estimated distributions of parameter values to be combined with additional sources of
information, such as that available from prevalence distributions, resulting in outputs which implicitly handle
variation and uncertainty. This methodology allows for more robust predictions from modelling approaches by
allowing for parameter uncertainty and combining different sources of information, and is thus expected to be
useful in application to a large number of disease systems.