Evaluation of risk-based surveillance strategies for Salmonella Dublin in Danish dairy herds by modelling temporal test performance and herd status classification errors

The potential risk-based improvement of the Salmonella Dublin surveillance programme in Danish dairy herds was investigated, considering herd status misclassifications due to testing errors. started October 2002. Currently (early 2021) all are classified based on quarterly bulk tank milk (BTM) with an indirect antibody ELISA (iELISA). Over last two decades, prevalence as “likely infected” (levels 2,3) reduced remarkably. However, since 2015, apparent has increased again, calling for improved and control protect animal human health. A deterministic simulation model data (2018–2019) from 2283 level 1 (“most likely free infection”), developed estimate false negative (FN) positive (FP) herds, under strategies. These were: (A) current system BTM only, (B) alternative strategy additional blood up eight calves, within at high risk infection (HR). Both strategies were evaluated using three classification methods (I III) four sensitivity analysis scenarios (SA1-4), where different temporal performances simulated iELISA BTM. To apply B, best high-risk method (II), which combined managerial applicability minimized errors, would require approximately 1000 calves across 127 HR herds. In that case, cause 3 FNs 67 FPs, by assuming annual (BTMSe) 95% conditional a 1-year disease history specificity (BTMSp) 97%. Whereas B could similar number FNs, but 7 FPs more, (Se) 77% (Sp) 99% individual blood-samples (SA1). Assuming also BTMSe 53% BTMSp 99.9% (SA4), derived 28 2 while resulted 6 less 8 more. Therefore, improve early detection infected avoid more unnecessary restrictions false-positive This improves knowledge use illustrates how modelling can be used control.