Frequência de anticorpos e fatores de risco para a infecção pelo vírus da diarreia viral bovina em fêmeas bovinas leiteiras não vacinadas na região Amazônica Maranhense, Brasil

Chaves, Nancyleni Pinto; Bezerra, Danilo Cutrim; Sousa, Vanessa Evangelista de; Santos, Hamilton Pereira; Pereira, H. de M.

doi:10.1590/s0103-84782010005000089

Cited by 11 publications

(8 citation statements)

References 7 publications

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“…The most impactful variable for BVDV prediction was related to farms that perform AI (Figure 5A), a factor that has been considered a predictor for BVDV globally, especially when semen is used from untested bulls or when farms use AI along with natural mating in order to “guarantee” the success of a pregnancy, a common and unsafe practice in Brazil [10]. AI is an important route of transmission of BVDV because semen remains infective, which is evident by the demonstration that susceptible cows can become infected following insemination [15,49-51].…”

Section: Discussionmentioning

confidence: 99%

“…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%

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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: Discussionmentioning

confidence: 99%

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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“…Freitas et al (2011) observed 37.37% (34/91) of positive animals and 18.68% (17/91) of suspect animals (totaling 56.04% [51/91] of seroreactive animals). In contrast, a higher prevalence of seroreactive animals using iELISA was described by Chaves et al (2010) (61.5%, 246/400) and Sousa et al (2013) (67.30%, 105/156) in dairy herds unvaccinated against BVDV in the Maranhão state. These results suggest a lower prevalence of infection in beef cattle, probably due to the shorter period of stay of the animals on the properties, and consequently, the lesser direct contact of susceptible animals to infected animals.…”

Section: Resultsmentioning

confidence: 87%

Sensitivity and specificity of indirect ELISA for the detection of antibody titers against BVDV from beef cattle raised in Pará State

Viana¹,

Kzam²,

Monteiro³

et al. 2017

SCA

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The aims of this study were to establish the prevalence of anti-bovine viral diarrhea virus (BVDV) antibodies (Ab) in beef cattle raised in Pará state, to compare the prevalence of seropositive animals to BVDV using a commercial indirect enzyme-linked immunosorbent assay kit (iELISA) and the virus neutralization (VN) test, and finally, to determine the sensitivity (Se) and specificity (Sp) of the iELISA for the detection of anti-BVDV Ab using VN as a gold standard. A total of 400 serum blood samples from Nelore cows aged at least 24 months from five farms in the Pará state from two mesoregions VN) and between the different farms (1, 2, 3, 4, and 5). The statistical analysis was performed with a significance level of 5%. The prevalence of seropositive animals was found to be different (P < 0.0001) using VN (39.25% [157/400]) and iELISA (54.50% [218/400] 25.93% (63/243) of the samples were considered false-positive and 1.27% false-negative (2/157). It was concluded that the BVDV infection is present in beef cattle herds of the state of Para. Based on the speed of execution, ease of handling, and high Se of the iELISA, it is suggested that this assay can be used as a screening test for the detection of anti-BVDV Ab with the aim of eliminating infected animals from large herds of beef cattle.

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“…Exposure to the virus is ás widespreád in Brázil ás it is in the endemic regions of other countries. There áre some studies in the scientific literáture thát reláte the frequency ánd the máin risk fáctors for the diseáse (CHAVES et ál., 2010;2012;ALMEIDA et ál., 2013;SOUSA et ál., 2013;MARQUES et ál. 2016;TSCHOPP et ál., 2017).…”

Section: Palavras-chaveunclassified

Frequency and risk factors associated with the bovine viral diarrhea virus in herds in the semiarid region of the states of Bahia and Pernambuco, Brazil

Souza

Santos

Souza

et al. 2019

AVB

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Article historyBovine virál diárrheá virus (BVDV) is án importánt virál páthogen in cáttle. There áre á few reports of this diseáse in northeástern Brázil, máinly in the semiárid region. The goál of this study wás to determine both the frequency of seropositive ánimáls, ánd the risk fáctors for BVDV in the municipálities of Báhiá ánd Pernámbuco. A non-probábilistic sámpling wás initiálly employed to determine the properties to be visited, whereás á probábilistic sámpling wás used to determine the number of ánimáls per property. Serum sámples were submitted to á seroneutrálizátion test to determine the ántibody titer for BVDV. An epidemiologicál questionnáire wás used in the herds to eváluáte the correlátion ánd ássociátion between possible risk fáctors ánd BVDV infection in these herds. Of the 257 ánimáls in the study, 144 were positive for infection BVDV, resulting in án overáll frequency of 56.0%. The highest frequency wás observed in Miguel Cálmon (76.1%) ánd followed by Senhor do Bonfim (53.1%). The váriábles identified ás risk fáctors for the BVDV infection were án extensive breeding system (OR = 2.58) ánd á mixed livestock purpose (beef ánd dáiry) (OR = 3.32). The presence of BVDV-seropositive ánimáls in the ássessed municipálities suggests the circulátion of the virál ágent in these locátions. Therefore, control ánd preventive meásures must be implemented in order to minimize losses due to the diseáse, such ás monitoring of the properties through láborátory tests, eliminátion of infected ánimáls, ánd the use of váccines in the indicáted regions by the ánimál heálth depártment.

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Frequência de anticorpos e fatores de risco para a infecção pelo vírus da diarreia viral bovina em fêmeas bovinas leiteiras não vacinadas na região Amazônica Maranhense, Brasil

Cited by 11 publications

References 7 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

Sensitivity and specificity of indirect ELISA for the detection of antibody titers against BVDV from beef cattle raised in Pará State

Frequency and risk factors associated with the bovine viral diarrhea virus in herds in the semiarid region of the states of Bahia and Pernambuco, Brazil

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