Serological and virological BVDV prevalence and risk factor analysis for herds to be BVDV seropositive in Belgian cattle herds

Sarrazin, Steven; Veldhuis, Anouk; Meroc, Estelle; Vangeel, Ilse; Laureyns, Jozef; Dewulf, Jeroen; Caij, Ann Brigitte; Piepers, Sofie; Hooyberghs, J.; Ribbens, Stefaan; Stede, Yves Van der

doi:10.1016/j.prevetmed.2012.07.005

Cited by 45 publications

(38 citation statements)

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“…A number of studies based on traditional risk factors identification approaches (logistic regression mainly) have been performed on BVDV [4-8], and the knowledge about major risk factors are related to the following: biosecurity [6], reproduction management [2,6,9,10], herd size [5,8], animal introduction [2,4,5,11], direct contact with other animals (from the same species or not) [4,11-13], communal grazing [4,5], age of animals [5,14], artificial insemination (AI) [15], and natural mating [13]. Nonetheless, usual epidemiologic analytic frameworks like logistic regression are often limited for the analysis of high-dimensional, imbalanced and nonlinear data, and may be poorly adapted to epidemiological datasets with a large number of predictor variables (parameters) in relation to the number of observations given the high susceptibility to overfitting [16,17].…”

Section: Introductionmentioning

confidence: 99%

What variables are important in predicting bovine viral diarrhea virus? A random forest approach

Machado

Recamonde‐Mendoza

Corbellini

2015

Vet Res

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Bovine viral diarrhea virus (BVDV) causes one of the most economically important diseases in cattle, and the virus is found worldwide. A better understanding of the disease associated factors is a crucial step towards the definition of strategies for control and eradication. In this study we trained a random forest (RF) prediction model and performed variable importance analysis to identify factors associated with BVDV occurrence. In addition, we assessed the influence of features selection on RF performance and evaluated its predictive power relative to other popular classifiers and to logistic regression. We found that RF classification model resulted in an average error rate of 32.03% for the negative class (negative for BVDV) and 36.78% for the positive class (positive for BVDV).The RF model presented area under the ROC curve equal to 0.702. Variable importance analysis revealed that important predictors of BVDV occurrence were: a) who inseminates the animals, b) number of neighboring farms that have cattle and c) rectal palpation performed routinely. Our results suggest that the use of machine learning algorithms, especially RF, is a promising methodology for the analysis of cross-sectional studies, presenting a satisfactory predictive power and the ability to identify predictors that represent potential risk factors for BVDV investigation. We examined classical predictors and found some new and hard to control practices that may lead to the spread of this disease within and among farms, mainly regarding poor or neglected reproduction management, which should be considered for disease control and eradication.Electronic supplementary materialThe online version of this article (doi:10.1186/s13567-015-0219-7) contains supplementary material, which is available to authorized users.

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Section: Introductionmentioning

confidence: 99%

What variables are important in predicting bovine viral diarrhea virus? A random forest approach

Machado

Recamonde‐Mendoza

Corbellini

2015

Vet Res

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“…Bovine viral diarrhoea, caused by the bovine viral diarrhoea virus (BVDV) in the Pestivirus genus of the Flaviviridae, is widespread and most commonly infects cattle and can cause considerable economic losses (Handel et al 2011;Sarrazin et al 2013;Lanyon et al 2014). Lots of pathology in several body systems has been considered to be associated with BVDV infection, including the reproductive, digestive, respiratory and systemic disorders (Brock 2004;Talafha et al 2009;Sarrazin et al 2013).…”

Section: Introductionmentioning

confidence: 99%

“…Lots of pathology in several body systems has been considered to be associated with BVDV infection, including the reproductive, digestive, respiratory and systemic disorders (Brock 2004;Talafha et al 2009;Sarrazin et al 2013). Because the ability of the virus to infect multiple organ systems is different, clinical symptoms caused by BVDV are highly variable, ranging from subclinical to acute infections with high mortality (Handel et al 2011).…”

Section: Introductionmentioning

confidence: 99%

Herd-level prevalence and associated risk factors for Toxoplasma gondii, Neospora caninum, Chlamydia abortus and bovine viral diarrhoea virus in commercial dairy and beef cattle in eastern, northern and northeastern China

et al. 2015

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Although the seroprevalence of Toxoplasma gondii, Neospora caninum, Chlamydia abortus and bovine viral diarrhea virus infection in cattle have been reported in some areas in China, most of them were conducted with small number of cattle samples and very limited districts and neglected the assessment of herd management factors associated with herd-level prevalence of these pathogen infections. Thus, from September 2013 to December 2014, a large-scale seroprevalence study was conducted to determine the animal-level and herd-level seroprevalence and identify herd-level risk factors associated with these pathogen infections in 4487 cattle from 134 herds in five provinces (Heilongjiang, Jilin, Liaoning, Shandong, Hebei) and Inner Mongolia Autonomous Region of China. At animal level, the true prevalence of antibodies against T. gondii, N. caninum, C. abortus and bovine viral diarrhoea virus (BVDV) was 10.48, 17.14, 11.92 and 50.10%, respectively. At herd level, the true prevalence of antibodies against T. gondii, N. caninum, C. abortus and BVDV was 27.16, 29.10, 37.31 and 40.30%, respectively. Multivariate analysis of these characteristics showed that source of water and presence of felids were significantly associated with T. gondii infection in the studied cattle herds. Source of water was significantly associated with N. caninum infection in the studied cattle herds. While herd size and management system were significantly associated with BVDV infection in the studied cattle herds, this is the first report of herd-level prevalence and associated risk factors of T. gondii, N. caninum, C. abortus and BVDV infection in cattle in China.

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“…In Belgium, however, BVD control is currently implemented on a voluntary basis at farm level (Letellier et al., ). A cross‐sectional study in 2010 estimated that the BVDV within‐herd seroprevalence in Belgian cattle was 32.9% [95% CI, (31.6–34.2)] in young cattle (6–12 months) and that the viroprevalence, based on antigen detection by ELISA, was 0.3% [95% CI, (0.1–0.6)] (Sarrazin et al., ).…”

Section: Introductionmentioning

confidence: 99%

Distinction Between Persistent and Transient Infection in a Bovine Viral Diarrhoea (BVD) Control Programme: Appropriate Interpretation of Real-Time RT-PCR and Antigen-ELISA Test Results

Hanon

Stede

Antonissen³

et al. 2012

Transbound Emerg Dis

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Control of bovine viral diarrhoea (BVD) in Belgium is currently implemented on a voluntary basis at herd level and mainly relies on detection and culling of persistently infected (PI) animals. The present field study was conducted during the winter of 2010/2011 to assess the performances of diagnostic assays used in the testing scheme for BVD as proposed by the two Belgian regional laboratories. Individual blood samples were collected from 4972 animals, and individual samples from the same herd were pooled (maximum of 30 individual samples per pool) and screened for the presence of Bovine Viral Diarrhoea Virus (BVDV)-specific RNA using a commercial real-time RT-PCR test (ADIAGENE). Individual samples from positive pools were then tested in parallel with the same RT-PCR test and with an antigen-capture ELISA test (IDEXX) to detect viremic animals. This study demonstrated that individual results differed according to the type of assay used (P < 0.001): 140 animals (2.8%) were positive by RT-PCR and 72 (1.4%) by antigen-ELISA. A second blood sample was taken 40 days later from 74 PCR positive animals to detect persistent viremia: 17 (23%) of these were still PCR positive and considered to be PI and the 57 that no longer tested positive were assumed to be transiently infected (TI) animals. All PI animals were positive also by antigen-ELISA at both time points. Among TI animals, 10 (16%) were positive by antigen-ELISA at the first but none at the second sampling. A highly significant difference in cycle threshold (Ct ) values obtained by RT-PCR was observed between PI and TI animals. ROC analysis was performed to establish thresholds to confirm with high probability that an animal is PI, based on the result of RT-PCR test performed on a single individual blood sample.

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Serological and virological BVDV prevalence and risk factor analysis for herds to be BVDV seropositive in Belgian cattle herds

Cited by 45 publications

References 23 publications

What variables are important in predicting bovine viral diarrhea virus? A random forest approach

What variables are important in predicting bovine viral diarrhea virus? A random forest approach

Herd-level prevalence and associated risk factors for Toxoplasma gondii, Neospora caninum, Chlamydia abortus and bovine viral diarrhoea virus in commercial dairy and beef cattle in eastern, northern and northeastern China

Distinction Between Persistent and Transient Infection in a Bovine Viral Diarrhoea (BVD) Control Programme: Appropriate Interpretation of Real-Time RT-PCR and Antigen-ELISA Test Results

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