Bayesian diagnostic test evaluation and true prevalence estimation of mycoplasma bovis in dairy herds.

The true prevalence of dairy cattle herds with M. bovis infections in the Netherlands is unknown. Previous attempts to estimate prevalences were hampered by the absence of a diagnostic serological test that was validated under field conditions. This study estimated sensitivity and specificity of two commercial serum ELISAs and the true M. bovis herd prevalence using different Bayesian latent class models. A total of 7305 serum samples from 415 randomly chosen dairy herds were collected in fall/winter 2019 and investigated for presence of antibodies against M. bovis using the BIO-K-260 ELISA from Bio-X. Serum samples from 100 of these herds were also tested with a second ELISA, from IDvet. A Bayesian latent class model using the paired test results estimated a sensitivity of 14.1% (95% Bayesian probability interval (BPI): 11.6-16.7%) for the Bio-X ELISA and a specificity of 97.2% (95% BPI: 95.9-98.4%). Sensitivity and specificity for the IDvet ELISA were estimated at 92.5% (95% BPI: 88.3-96.5%) and 99.3% (95% BPI: 98.7-99.8%), respectively. Also, Bio-X ELISA sensitivity was considerably higher with data from calves only and with data from a selection of herds with a clinical outbreak, whereas the IDvet ELISA sensitivity was fairly constant under these conditions. These differences in test sensitivity is expected to be related to an effect of time since infection. A second Bayesian model, applied on test results of all 415 herds, estimated a true herd prevalence of 69.9% (95% BPI: 62.7-77.6%), suggesting M. bovis in endemic amongst dairy cattle herds in the Netherlands. To what extent seropositive herds have experienced a clinical outbreak needs further investigation.

Application of syndromic surveillance on routinely collected cattle reproduction and milk production data for the early detection of outbreaks of Bluetongue and Schmallenberg viruses.

This study aimed to evaluate the use of routinely collected reproductive and milk production data for the early detection of emerging vector-borne diseases in cattle in the Netherlands and the Flanders region of Belgium (i.e., the northern part of Belgium). Prospective space-time cluster analyses on residuals from a model on milk production were carried out to detect clusters of reduced milk yield. A CUSUM algorithm was used to detect temporal aberrations in model residuals of reproductive performance models on two indicators of gestation length. The Bluetongue serotype-8 (BTV-8) epidemics of 2006 and 2007 and the Schmallenberg virus (SBV) epidemic of 2011 were used as case studies to evaluate the sensitivity and timeliness of these methods. The methods investigated in this study did not result in a more timely detection of BTV-8 and SBV in the Netherlands and BTV-8 in Belgium given the surveillance systems in place when these viruses emerged. This could be due to (i) the large geographical units used in the analyses (country, region and province level), and (ii) the high level of sensitivity of the surveillance systems in place when these viruses emerged. Nevertheless, it might be worthwhile to use a syndromic surveillance system based on non-specific animal health data in real-time alongside regular surveillance, to increase the sense of urgency and to provide valuable quantitative information for decision makers in the initial phase of an emerging disease outbreak.