Abstract

Background

Globalization and subsequent growth in international trade in animals and animal products has increased the importance of international disease reporting. Efficient and reliable surveillance systems are needed in order to document the disease status of a population at a given time. In this context, passive surveillance plays an important role in early warning systems. However, it is not yet routinely integrated in the assessment of disease surveillance systems because different factors like the disease awareness (DA) of people reporting suspect cases influence the detection performance of passive surveillance. In this paper, we used scenario tree methodology in order to evaluate and compare the quality and benefit of abortion testing (ABT) for Brucella melitensis (Bm) between the disease free situation in Switzerland (CH) and a hypothetical disease free situation in Bosnia and Herzegovina (BH), taking into account DA levels assumed for the current endemic situation in BH.

Results

The structure and input parameters of the scenario tree were identical for CH and BH with the exception of population data in small ruminants and the DA in farmers and veterinarians. The sensitivity analysis of the stochastic scenario tree model showed that the small ruminant population structure and the DA of farmers were important influential parameters with regard to the unit sensitivity of ABT in both CH and BH. The DA of both farmers and veterinarians was assumed to be higher in BH than in CH due to the current endemic situation in BH. Although the same DA cannot necessarily be assumed for the modelled hypothetical disease free situation as for the actual endemic situation, it shows the importance of the higher vigilance of people reporting suspect cases on the probability that an average unit processed in the ABT-component would test positive.

Conclusion

The actual sensitivity of passive surveillance approaches heavily depends on the context in which they are applied. Scenario tree modelling allows for the evaluation of such passive surveillance system components under assumed disease free situation. Despite data gaps, this is a real opportunity to compare different situations and to explore consequences of changes that could be made.