Calibration of a Monte Carlo simulation model of disease spread in slaughter pig units

The use of new resampling methods to improve the handling of stochastic simulation models is demonstrated. As an example, we use a Monte-Carlo simulation model of disease spread within a slaughter pig herd. The model parameters reflect the disease spread and comprise, for example infection risk given diseases, and the positioning of the animals. The setting of the prior distribution of the parameters using expert knowledge is complicated, because the expert knowledge is generally based on the resulting dynamics rather than the underlying parameters. The paper shows how the prior distribution of model parameters can be made consistent with the knowledge concerning model output, using methods such as importance sampling and Markov Chain Monte Carlo techniques. Based on these methods, different management strategies are compared.

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