Predicting Positive ELISA Results in Dairy Herds with a Preferred Status in a Paratuberculosis Control Program.

Dairy herds participating in the Dutch milk quality assurance program for paratuberculosis are assigned a herd status on the basis of herd examinations by ELISA of individual serum or milk samples, followed by an optional confirmatory fecal PCR. Test-negative herds are assigned Status A; the surveillance of these herds consists of biennial herd examinations. Farmers falsely believing that their Status A herds are Map-free may inadvertently refrain from preventive measures. Therefore, we aimed to develop a predictive model to alert Status A farmers at increased risk of future positive ELISA results. Using data of 8566 dairy herds with Status A in January 2016, two logistic regression models were built, with the probabilities of ≥1 or ≥2 positive samples from January 2017-June 2019 as dependent variables, and province, soil type, herd size, proportion of cattle born elsewhere, time since previous positive ELISA results, and the 95th percentile of the S/P ratios in 2015-2016, as explanatory variables. As internal validation, both models were applied to predict positive ELISA results from January 2019-June 2021, in 8026 herds with Status A in January 2019. The model predicting ≥1 positive sample had an area under the receiver operating characteristics curve of 0.76 (95% CI: 0.75, 0.77). At a cut-off predicted probability π