A Simulation Model to Determine Sensitivity and Timeliness of Surveillance Strategies.

Abstract

Animal surveillance systems need regular evaluation. We developed an easily applicable simulation model of the German wild boar population to investigate two evaluation attributes: the sensitivity and timeliness (i.e. the ability to detect a disease outbreak rapidly) of a surveillance system. Classical swine fever (CSF) was used as an example for the model. CSF is an infectious disease that may lead to massive economic losses. It can affect wild boar as well as domestic pigs, and CSF outbreaks in domestic pigs have been linked to infections in wild boar. Awareness of the CSF status in wild boar is therefore vital. Our non-epidemic simulation model is based on real data and evaluates the currently implemented German surveillance system for CSF in wild boar. The results show that active surveillance for CSF fulfils the requirements of detecting an outbreak with 95% confidence within one year after the introduction of CSF into the wild boar population. Nevertheless, there is room for improved performance and efficiency by more homogeneous (active and passive) sampling of wild boar over the year. Passive surveillance alone is not sufficient to meet the requirements for detecting the infection. Although CSF was used as example to develop the model, it may also be applied to the evaluation of other surveillance systems for viral diseases in wild boar. It is also possible to compare sensitivity and timeliness across hypothetical alternative or risk-based surveillance strategies.