Stochastic modelling of Salmonella monitoring in finishing pigs
Results of serological monitoring for Salmonella in finishing pigs are used to classify herds and target control measures at herds with high prevalence. The outcome of monitoring depends on three factors: (a) the optical density percentage (OD%) used to declare a sample positive, (b) the herd classification scheme, and (c) the number of samples. The goal of this study was to analyse the impact of these three factors on the reliability and cost of Salmonella monitoring in finishing pigs. A stochastic simulation model was constructed to evaluate 12 monitoring scenarios based on: (a) four cut-off values for the OD% and (b) three herd classification schemes. Furthermore, eight different sampling schemes were evaluated. The main outputs of the model are (a) the percentage of herds changing classification as a reliability criterion and (b) the total number of samples taken as a cost criterion. Results indicated that monitoring scenarios based on cut-off OD% 10 are most reliable. Moreover, inclusion of a zero-prevalence class decreased the reliability of monitoring. The economically optimal sampling scheme depended on the monitoring scenario used.
| Main Authors: | , , |
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| Format: | Article in monograph or in proceedings biblioteca |
| Language: | English |
| Subjects: | Life Science, |
| Online Access: | https://research.wur.nl/en/publications/stochastic-modelling-of-salmonella-monitoring-in-finishing-pigs-2 |
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| Summary: | Results of serological monitoring for Salmonella in finishing pigs are used to classify herds and target control measures at herds with high prevalence. The outcome of monitoring depends on three factors: (a) the optical density percentage (OD%) used to declare a sample positive, (b) the herd classification scheme, and (c) the number of samples. The goal of this study was to analyse the impact of these three factors on the reliability and cost of Salmonella monitoring in finishing pigs. A stochastic simulation model was constructed to evaluate 12 monitoring scenarios based on: (a) four cut-off values for the OD% and (b) three herd classification schemes. Furthermore, eight different sampling schemes were evaluated. The main outputs of the model are (a) the percentage of herds changing classification as a reliability criterion and (b) the total number of samples taken as a cost criterion. Results indicated that monitoring scenarios based on cut-off OD% 10 are most reliable. Moreover, inclusion of a zero-prevalence class decreased the reliability of monitoring. The economically optimal sampling scheme depended on the monitoring scenario used. |
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