Proceedings of the 10th International Colloquium on Paratuberculosis

The aim <strong>of</strong> <strong>the</strong> present study was to evaluate <strong>the</strong> surveillance system regarding MAP in
Swedish cattle, to quantify <strong>the</strong> contribution from different surveillance components and to
estimate <strong>the</strong> probability that Swedish cattle are free from MAP.
MATERIALS AND METHODS
A stochastic scenario-tree model was used to estimate <strong>the</strong> probability <strong>of</strong> freedom from MAP
infection. This method has previously been described by Martin and co-workers (Martin et al.,
2007a; Martin et al., 2007b) and allows information from several different sources, e.g.
random or non-random surveillance data as well as documentation <strong>of</strong> differences in risk, to
contribute to <strong>the</strong> quantitative estimation <strong>of</strong> surveillance sensitivities and probability <strong>of</strong>
disease. In this type <strong>of</strong> model, factors relating to <strong>the</strong> structure <strong>of</strong> <strong>the</strong> study population and to
<strong>the</strong> probability <strong>of</strong> detection, or infection, are included as nodes for which input proportions or
probabilities are given. One example <strong>of</strong> <strong>the</strong> tree-like structure <strong>of</strong> <strong>the</strong> model is given in Fig. 1
which shows <strong>the</strong> risk categories and relevant detection pathways relating to paratuberculosis
investigations in fallen stock.
Figure 1. Structure <strong>of</strong> <strong>the</strong> scenario-tree model for evaluation <strong>of</strong> MAP surveillance in Sweden.
This example illustrates <strong>the</strong> surveillance component covering investigations <strong>of</strong> fallen stock.
Empty arrows indicate corresponding branches.
In brief, <strong>the</strong> scenario-tree model is used to calculate <strong>the</strong> probability <strong>of</strong> freedom based on <strong>the</strong>
different probabilities <strong>of</strong> detecting each type <strong>of</strong> infected unit, in case infection is present in <strong>the</strong>
population. By this approach more weight is given to investigations in herds and animal <strong>of</strong>
known risk categories.
The probability <strong>of</strong> detection at <strong>the</strong> animal level, i.e. <strong>the</strong> animal-level sensitivity, is used
to calculate <strong>the</strong> herd-level sensitivity. In our model, <strong>the</strong> herd sensitivity was calculated
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