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Funding Information: This Open Access publication was funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) – 491094227 “Open Access Publication Funding” and the University of Veterinary Medicine Hannover, Foundation.

Knowledge about the distribution of Anaplasma spp. in small ruminants from Germany is limited. Therefore, serum samples were examined from 71 small ruminant flocks (2731 sheep, 447 goats) located in the five German federal states: Schleswig-Holstein (SH), Lower Saxony (LS), North Rhine-Westphalia (NRW), Baden-Wuerttemberg (BW) and Bavaria (BAV). Antibodies to Anaplasma spp. were determined by a cELISA based on the MSP5 antigen. A risk factor analysis at animal and flock level was also performed. Antibodies to Anaplasma spp. were detected in 70/71 flocks without significant difference in the intra-flock prevalence (IFP) between the federal states. The mean antibody levels from sheep were significantly lower in northern Germany (LS, SH) compared to west (NRW) and south Germany (BW, BAV). Sheep had a 2.5-fold higher risk of being seropositive than goats. Females and older animals (&gt, 2 years) were more likely to have antibodies to Anaplasma spp. in one third and one quarter of cases, respectively. Flocks used for landscape conservation had a five times higher risk of acquiring an IFP greater than 20%. Cats and dogs on the farms increased the probability for small ruminant flocks to have an IFP of above 20% 10-fold and 166-fold, respectively. Further studies are necessary to assess the impact of Anaplasma species on the health of small ruminants in Germany.

Latent class analysis is a well-established method in human and veterinary medicine for evaluating the accuracy of diagnostic tests without a gold standard. An important assumption of this procedure is the conditional independence of the tests. If tests with the same biological principle are used, this assumption is no longer met. Therefore, the model has to be adapted so that the dependencies between the tests can be considered. Our approach extends the traditional latent class model with a term for the conditional dependency of the tests. This extension increases the number of parameters to be estimated and leads to negative degrees of freedom of the model, meaning that not enough information is contained in the existing data to obtain a unique estimate. As a result, there is no clear solution. Hence, an iterative algorithm was developed to keep the number of parameters to be estimated small. Given adequate starting values, our approach first estimates the conditional dependencies and then regards the resulting values as fixed to recalculate the test accuracies and the prevalence with the same method used for independent tests. Subsequently, the new values of the test accuracy and prevalence are used to recalculate the terms for the conditional dependencies. These two steps are repeated until the model converges. We simulated five application scenarios based on diagnostic tests used in veterinary medicine. The results suggest that our method and the Bayesian approach produce similar precise results. However, while the presented approach is able to calculate more accurate results than the Bayesian approach if the test accuracies are initially misjudged, the estimates of the Bayesian method are more precise when incorrect dependencies are assumed. This finding shows that our approach is a useful addition to the existing Bayesian methods, while it has the advantage of allowing simpler and more objective estimations.