Instituto Brasileiro de Geografia e Estatística - Concentração jurídica

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.

https://link.springer.com/content/pdf/10.1186%2Fs13567-015-0219-7.pdf

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

Background
Leptospirosis is an epidemic-prone neglected disease that affects humans and animals,
mostly in vulnerable populations. The One Health approach is a recommended strategy to
identify drivers of the disease and plan for its prevention and control. In that context, the aim
of this study was to analyze the distribution of human cases of leptospirosis in the State of
Rio Grande do Sul, Brazil, and to explore possible drivers. Additionally, it sought to provide
further evidence to support interventions and to identify hypotheses for new research at the
human-animal-ecosystem interface.
Methodology and findings
The risk for human infection was described in relation to environmental, socioeconomic,
and livestock variables. This ecological study used aggregated data by municipality (all
496). Data were extracted from secondary, publicly available sources. Thematic maps were
constructed and univariate analysis performed for all variables. Negative binomial regression
was used for multivariable statistical analysis of leptospirosis cases. An annual average
of 428 human cases of leptospirosis was reported in the state from 2008 to 2012. The
cumulative incidence in rural populations was eight times higher than in urban populations.
Variables significantly associated with leptospirosis cases in the final model were: Parana/
Paraiba ecoregion (RR: 2.25; CI95%: 2.03–2.49); Neossolo Litolitico soil (RR: 1.93; CI95%: 1.26–2.96); and, to a lesser extent, the production of tobacco (RR: 1.10; CI95%: 1.09–1.11)
and rice (RR: 1.003; CI95%: 1.002–1.04).
Conclusion
Urban cases were concentrated in the capital and rural cases in a specific ecoregion. The
major drivers identified in this study were related to environmental and production processes
that are permanent features of the state. This study contributes to the basic knowledge
on leptospirosis distribution and drivers in the state and encourages a comprehensive
approach to address the disease in the animal-human-ecosystem interface.

/pdf/leptospirosis-in-rio-grande-do-sul-brazil-an-ecosystem-4okjd8ns74.pdf

Leptospirosis in Rio Grande do Sul, Brazil: An Ecosystem Approach in the Animal-Human Interface.

Development and validation of a scoring system to assess the relative vulnerability of swine breeding herds to the introduction of PRRS virus.

Foot and mouth disease (FMD) is a highly infectious disease that affects cloven-hoofed livestock and wildlife. FMD has been a problem for decades, which has led to various measures to control, eradicate and prevent FMD by National Veterinary Services worldwide. Currently, the identification of areas that are at risk of FMD virus incursion and spread is a priority for FMD target surveillance after FMD is eradicated from a given country or region. In our study, a knowledge-driven spatial model was built to identify risk areas for FMD occurrence and to evaluate FMD surveillance performance in Rio Grande do Sul state, Brazil. For this purpose, multi-criteria decision analysis was used as a tool to seek multiple and conflicting criteria to determine a preferred course of action. Thirteen South American experts analyzed 18 variables associated with FMD introduction and dissemination pathways in Rio Grande do Sul. As a result, FMD higher risk areas were identified at international borders and in the central region of the state. The final model was expressed as a raster surface. The predictive ability of the model assessed by comparing, for each cell of the raster surface, the computed model risk scores with a binary variable representing the presence or absence of an FMD outbreak in that cell during the period 1985 to 2015. Current FMD surveillance performance was assessed, and recommendations were made to improve surveillance activities in critical areas.

/pdf/identification-of-foot-and-mouth-disease-risk-areas-using-a-34phhgnh9w.pdf

Identification of foot and mouth disease risk areas using a multi-criteria analysis approach.

Background: The economy of Rio Grande do Sul (RS) relies majorly on agriculture; among the livestock production chains, cattle production has the largest economic, historical, and cultural importance in RS. The cattle industry is the main zootechnical activity in RS. Due to this, there is an actual need for updated characterization of the animal population, considering the population dynamics and the requirements imposed by the Official Veterinary Service (SVO) to meet certain characteristics. This would facilitate appropriate policies and measures to safeguard the health of the cattle in RS, as well as safeguard public health, and consequently avoid the economic impacts of possible health events. Materials, Methods & Results: Based on data from the livestock survey of 2013 from the Department of Animal Health (DDA), the Secretariat of Agriculture, Livestock, and Agribusiness of the State of Rio Grande do Sul (SEAPA-RS), descriptive and spatial analyses of the cattle population were performed using software R and ArcMap TM 10, respectively. It was observed that the state has more than 13 million cattle distributed over approximately 346,000 farms. The majority of the bovine population consists of females over the age of 36 months. The predominant function of these farms is a complete cycle (breeding to fattening). Beef production is the predominant activity, followed by a mix of beef production and dairy production, and then sole dairy production. These characteristics differ depending on the state’s region. Regarding the number of animals per property, 88% of properties are small having up to 50 cattle, and about 1% of properties have more than 500 animals. The general average in the state for the proportion of T:V (calf: cow) is 57 calves per 100 cows, and this is close to the national average. Discussion: About 60.62% of cattle herds consisted of animals aged over 24 months, and most of this group were females over 36 months of age (38.95%). About 50% of properties have up to 10 animals, demonstrating a large proportion of small farms. RS has an imbalance in its production system, with a large number of breeding females for the activity of beef production. The cattle in RS are mostly bred for the production of beef in a full cycle system (with all stages of production on the property), and only 10% of cattle raised in RS are bred solely for milk production. With regard to the proportion of T:V, we concluded that the state’s beef production shows modest productivity and needs to improve production rates to increase financial returns for producers and enable competitiveness in the domestic and international markets. Furthermore, this information correlates with previous studies that have reported that farms in the business of beef production use low technology and low performance animals. Dairy farming, in contrast with beef farming, has been modernizing and developing in recent years by increasing co-operatives and agribusinesses, which has led to greater knowledge through technical assistance to the farms. Extensive farming is dependent on field areas and is historically associated with the natural fields in the campaign region, since dairy farming is dependent on areas where there is a supply of specialized food. Thus, despite the state having a greater concentration of animals in the south-southwest, production indices are similar to other regions, and the type of farming undertaken exerts a great influence on the regional animal population structure.

Panorama da bovinocultura no Rio Grande do Sul Cattle Rearing in Rio Grande do Sul, Brazil

Panorama da bovinocultura no Rio Grande do Sul

/pdf/cattle-rearing-in-rio-grande-do-sul-brazil-36wn0cmgkn.pdf

Diego Viali dos Santos

Papers

Identification of foot and mouth disease risk areas using a multi-criteria analysis approach.

Panorama da bovinocultura no Rio Grande do Sul Cattle Rearing in Rio Grande do Sul, Brazil

Panorama da bovinocultura no Rio Grande do Sul

Cattle rearing in Rio Grande do Sul, Brazil.