Evaluating the surveillance for swine dysentery and progressive atrophic Rhinitis in closed multiplier herds using Scenario tree modelling

Nathues, Christina; Hillebrand, Anna Sarah; Rossteuscher, S; Zimmermann, Werner; Nathues, Heiko; Schüpbach, Gertraud (2015). Evaluating the surveillance for swine dysentery and progressive atrophic Rhinitis in closed multiplier herds using Scenario tree modelling. Porcine health management, 1(1) BioMed Central 10.1186/s40813-015-0001-2

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Background: The Swiss pig population enjoys a favourable health situation. To further promote this, the Pig Health
Service (PHS) conducts a surveillance program in affiliated herds: closed multiplier herds with the highest PHS-health
and hygiene status have to be free from swine dysentery and progressive atrophic rhinitis and are clinically examined
four times a year, including laboratory testing. Besides, four batches of pigs per year are fattened together with pigs
from other herds and checked for typical symptoms (monitored fattening groups (MF)).
While costly and laborious, little was known about the effectiveness of the surveillance to detect an infection in a
herd. Therefore, the sensitivity of the surveillance for progressive atrophic rhinitis and swine dysentery at herd level
was assessed using scenario tree modelling, a method well established at national level. Furthermore, its costs and
the time until an infection would be detected were estimated, with the final aim of yielding suggestions how to
optimize surveillance.
Results: For swine dysentery, the median annual surveillance sensitivity was 96.7 %, mean time to detection
4.4 months, and total annual costs 1022.20 Euro/herd. The median component sensitivity of active sampling was
between 62.5 and 77.0 %, that of a MF between 7.2 and 12.7 %.
For progressive atrophic rhinitis, the median surveillance sensitivity was 99.4 %, mean time to detection 3.1 months
and total annual costs 842.20 Euro. The median component sensitivity of active sampling was 81.7 %, that of a MF
between 19.4 and 38.6 %.
Conclusions: Results indicate that total sensitivity for both diseases is high, while time to detection could be a risk in
herds with frequent pig trade. From all components, active sampling had the highest contribution to the surveillance
sensitivity, whereas that of MF was very low. To increase efficiency, active sampling should be intensified (more animals
sampled) and MF abandoned. This would significantly improve sensitivity and time to detection at comparable or
lower costs.
The method of scenario tree modelling proved useful to assess the efficiency of surveillance at herd level. Its versatility
allows adjustment to all kinds of surveillance scenarios to optimize sensitivity, time to detection and/or costs.

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