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