Prevalence of brucellosis in dairy cattle from the main dairy farming regions of Eritrea.
23 Apr 2013-Onderstepoort Journal of Veterinary Research (AOSIS Publishing)-Vol. 80, Iss: 1, pp 448-448
TL;DR: In this article, a cross-sectional study was carried out in order to get a reliable estimate of brucellosis prevalence in Eritrean dairy cattle, where samples were screened with the Rose Bengal test (RBT) and positive cases were confirmed with the complement fixation test (CFT).
Abstract: In order to get a reliable estimate of brucellosis prevalence in Eritrean dairy cattle, a cross-sectional study was carried out in 2009. The survey considered the sub-population of dairy cattle reared in modern small- and medium-sized farms. Samples were screened with the Rose Bengal test (RBT) and positive cases were confirmed with the complement fixation test (CFT). A total of 2.77%(417/15 049; Credibility Interval CI: 2.52% – 3.05%) of the animals tested in this study were positive for antibodies to Brucellaspecies, with a variable and generally low distribution of positive animals at regional level. The highest seroprevalence was found in the Maekel region (5.15%; CI: 4.58% – 5.80%), followed by the Debub (1.99%; CI: 1.59% – 2.50%) and Gash-Barka (1.71%; CI: 1.34% – 2.20%) regions. Seroprevalence at sub-regional levels was also generally low, except for two sub-regions of Debub and the sub-region Haicota from the Gash-Barka region. Seroprevalence was high and more uniformly distributed in the Maekel region, namely in the Asmara, Berik and Serejeka sub-regions. Considering the overall low brucellosis prevalence in the country, as identified by the present study, a brucellosis eradication programme for dairy farms using a test-and-slaughter policy would be possible. However, to encourage the voluntary participation of farmers to the programme and to raise their awareness of the risks related to the disease for animals and humans, an extensive public awareness campaign should be carefully considered, as well as strict and mandatory dairy movement control.
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01 Jan 2001
TL;DR: The probability of any event is the ratio between the value at which an expectation depending on the happening of the event ought to be computed, and the value of the thing expected upon it’s 2 happening.
Abstract: Problem Given the number of times in which an unknown event has happened and failed: Required the chance that the probability of its happening in a single trial lies somewhere between any two degrees of probability that can be named. SECTION 1 Definition 1. Several events are inconsistent, when if one of them happens, none of the rest can. 2. Two events are contrary when one, or other of them must; and both together cannot happen. 3. An event is said to fail, when it cannot happen; or, which comes to the same thing, when its contrary has happened. 4. An event is said to be determined when it has either happened or failed. 5. The probability of any event is the ratio between the value at which an expectation depending on the happening of the event ought to be computed, and the value of the thing expected upon it’s 2 happening.
217 citations
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TL;DR: The results of the systematic review and meta-analysis reveal that the overall seroprevalence of brucellosis in dairy cattle herds in China was 1.9% during the selected period, rising from 1.6% in 2008-2012 to 2.
Abstract: Brucellosis remains one of the most common zoonotic diseases globally with more than a half million human cases reported annually. The Brucella reservoir associated with livestock brucellosis poses a significant threat to public health, and awareness of the seroprevalence and spatial distribution of livestock brucellosis is valuable for the prevention and control of diseases caused by Brucella, especially human brucellosis. Therefore, we conducted a systematic review and meta-analysis to evaluate the seroprevalence of brucellosis in dairy cattle in China. We retrieved 88 studies related to the seroprevalence of brucellosis in dairy cattle in China in which samples were harvested between 2008 and 2018. The results of our systematic review and meta-analysis reveal that the overall seroprevalence of brucellosis in dairy cattle herds in China was 1.9% during the selected period, rising from 1.6% in 2008-2012 to 2.6% in 2013-2018. In Northern China, where the traditional agropastoral areas with more developed animal breeding industry are located, the brucellosis seroprevalence was >10%. In contrast, the seroprevalence of brucellosis in Southern China reached only 5.5%. At the provincial level, the highest brucellosis seroprevalence in dairy cattle was estimated at >30% in Jilin province, followed by Shanxi, Ningxia, Inner Mongolia, and Guizhou, each with a prevalence of 10-20%. Additionally, the seroprevalence of brucellosis in some local areas was >30% or even >50%, indicating that Brucella infection was highly endemic in dairy herds in China. Our data may facilitate the prevention and control of brucellosis in domestic animals in China. Further epidemiological surveillance and the administration of a comprehensive monitoring program to determine the risk factors associated with brucellosis incidence in humans and domestic animals are recommended to refine brucellosis control strategies.
25 citations
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TL;DR: A high seroprevalence of brucellosis among the cattle in Bauchi state indicates that the disease is endemic and cattle are one of the animals that perpetuate and sustain the disease.
Abstract: Aim: To determine the seroepidemiological patterns of bovine brucellosis in three senatorial zones of Bauchi State, Nigeria. Materials and Methods: Blood samples were aseptically collected from the anterior jugular vein of 336 slaughtered cattle, between September 2013 and March 2014 in three senatorial zones of Bauchi State, Nigeria. The sera obtained were screened for brucellosis using rose Bengal plate test (RBPT) and serum agglutination test (SAT) in parallel. The data generated was subjected to Chi-square and Fishers exact test analysis to establish whether there is a relationship between the breeds, sex, and location of the animals sampled. Results: Of the 336 cattle screened, 18 (5.4%) and 13 (3.9%) were positive by RBPT and SAT, respectively. There was no statistically significant association (p>0.05) between the sex, age, and location of cattle with seropositivity of brucellosis in the state. It was concluded that brucellosis is prevalent in Bauchi State. Further study is recommended in other abattoirs and herds of cattle in Bauchi State for confirmation of the status of the disease among cattle slaughtered in the state. Conclusion: A high seroprevalence of brucellosis among the cattle in Bauchi state indicates that the disease is endemic and cattle are one of the animals that perpetuate and sustain the disease.
11 citations
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TL;DR: Although there was no acute disease, antibodies detected against L. hardjo and B. abortus in the cattle population indicated the presence of chronic and subclinical infection, which could challenge the fertility of the animals.
Abstract: AIM The aim was to assess the seroprevalence of B. abortus and Leptospira hardjo in the cattle population of Bihar, this work was carried out. MATERIALS AND METHODS Randomly selected 450 cattle from nine districts of Bihar were serologically screened for antibodies against L. hardjo and B. abortus. DAS-ELISA for leptospira and AB-ELISA for brucella were carried out. Based on the results prevalence in each district and the state are reported herewith. RESULTS AND DISCUSSION In this study, it was found that the seroprevalence of L. hardjo was 9.11% and that of B. abortus was 12.2% in Bihar. Indigenous cattle were found to be less susceptible to leptospirosis and brucellosis even though they accounted for 83.11% of the study population. CONCLUSION Although there was no acute disease, antibodies detected against L. hardjo and B. abortus in the cattle population indicated the presence of chronic and subclinical infection, which could challenge the fertility of the animals.
9 citations
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TL;DR: The main objective of this study was to determine the serological prevalence of brucellosis on a dairy farm with no past history of abortions, but where Brucella control measures including test and slaughter and vaccination of heifers at 4–8 months of age was practiced.
Abstract: The main objective of this study was to determine the serological prevalence of brucellosis on a dairy farm with no past history of abortions, but where Brucella control measures including test and slaughter and vaccination of heifers at 4–8 months of age was practiced. Secondary data from 2011 to 2014 obtained from the Epidemiology Section of the Directorate of Veterinary Services was used. Mandatory annual brucellosis testing results for mature dairy cows on a dairy farm for the period 2011–2014 were collated and analyzed. Results of a total of 6912 cows were analysed. The data comprised of the year of testing, number of cows tested for Brucella antibodies and the number of cows that tested positive. Serological testing was carried out using the Rose Bengal Test (RBT) as a screening test and the Complement Fixation Test as a confirmatory test for results that tested positive on the RBPT. Over the 4-year period, one dairy cow tested positive for Brucella antibodies in 2013 giving an apparent prevalence of 0.05% and an overall prevalence of 0.01%. When apparent prevalence was adjusted for RBPT test specificity and sensitivity of 71 and 78% respectively, true prevalence was determined to be zero.
7 citations
References
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01 Jan 1996
TL;DR: This tutorial jumps right in to the power ofparameter estimation without dragging you through the basic concepts of parameter estimation.
Abstract: 1. The Basics 2. Parameter Estimation I 3. Parameter Estimation II 4. Model Selection 5. Assigning Probabilities 6. Non-parametric Estimation 7. Experimental Design 8. Least-Squares Extensions 9. Nested Sampling 10. Quantification Appendices Bibliography
1,946 citations
Book•
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05 Dec 2000
TL;DR: In this article, the authors present a risk analysis approach based on Monte-Carlo simulation, which is used to fit a first-order parametric distribution to observed data and then combine it with a second-order probability distribution.
Abstract: Preface. Part 1: Introduction. 1. Why do a risk analysis? 1.1. Moving on from "What If" Scenarios. 1.2. The Risk Analysis Process. 1.3. Risk Management Options. 1.4. Evaluating Risk Management Options. 1.5. Inefficiencies in Transferring Risks to Others. 1.6. Risk Registers. 2. Planning a risk analysis. 2.1. Questions and Motives. 2.2. Determine the Assumptions that are Acceptable or Required. 2.3. Time and Timing. 2.4. You'll Need a Good Risk Analyst or Team. 3. The quality of a risk analysis. 3.1. The Reasons Why a Risk Analysis can be Terrible. 3.2. Communicating the Quality of Data Used in a Risk Analysis. 3.3. Level of Criticality. 3.4. The Biggest Uncertainty in a Risk Analysis. 3.5. Iterate. 4. Choice of model structure. 4.1. Software Tools and the Models they Build. 4.2. Calculation Methods. 4.3. Uncertainty and Variability. 4.4. How Monte Carlo Simulation Works. 4.5. Simulation Modelling. 5. Understanding and using the results of a risk analysis. 5.1. Writing a Risk Analysis Report. 5.2. Explaining a Model's Assumptions. 5.3. Graphical Presentation of a Model's Results. 5.4. Statistical Methods of Analysing Results. Part 2: Introduction. 6. Probability mathematics and simulation. 6.1. Probability Distribution Equations. 6.2. The Definition of "Probability". 6.3. Probability Rules. 6.4. Statistical Measures. 7. Building and running a model. 7.1. Model Design and Scope. 7.2. Building Models that are Easy to Check and Modify. 7.3. Building Models that are Efficient. 7.4. Most Common Modelling Errors. 8. Some basic random processes. 8.1. Introduction. 8.2. The Binomial Process. 8.3. The Poisson Process. 8.4. The Hypergeometric Process. 8.5. Central Limit Theorem. 8.6. Renewal Processes. 8.7. Mixture Distributions. 8.8. Martingales. 8.9. Miscellaneous Example. 9. Data and statistics. 9.1. Classical Statistics. 9.2. Bayesian Inference. 9.3. The Bootstrap. 9.4. Maximum Entropy Principle. 9.5. Which Technique Should You Use? 9.6. Adding uncertainty in Simple Linear Least-Squares Regression Analysis. 10. Fitting distributions to data. 10.1. Analysing the Properties of the Observed Data. 10.2. Fitting a Non-Parametric Distribution to the Observed Data. 10.3. Fitting a First-Order Parametric Distribution to Observed Data. 10.4. Fitting a Second-Order Parametric Distribution to Observed Data. 11. Sums of random variables. 11.1. The Basic Problem. 11.2. Aggregate Distributions. 12. Forecasting with uncertainty. 12.1. The Properties of a Time Series Forecast. 12.2. Common Financial Time Series Models. 12.3. Autoregressive Models. 12.4. Markov Chain Models. 12.5. Birth and Death Models. 12.6. Time Series Projection of Events Occurring Randomly in Time. 12.7. Time Series Models with Leading Indicators. 12.8. Comparing Forecasting Fits for Different Models. 12.9. Long-Term Forecasting. 13. Modelling correlation and dependencies. 13.1. Introduction. 13.2. Rank Order Correlation. 13.3. Copulas. 13.4. The Envelope Method. 13.5. Multiple Correlation Using a Look-Up Table. 14. Eliciting from expert opinion. 14.1. Introduction. 14.2. Sources of Error in Subjective Estimation. 14.3. Modelling Techniques. 14.4. Calibrating Subject Matter Experts. 14.5. Conducting a Brainstorming Session. 14.6. Conducting the Interview. 15. Testing and modelling causal relationships. 15.1. Campylobacter Example. 15.2. Types of Model to Analyse Data. 15.3. From Risk Factors to Causes. 15.4. Evaluating Evidence. 15.5. The Limits of Causal Arguments. 15.6. An Example of a Qualitative Causal Analysis. 15.7. Is Causal Analysis Essential? 16. Optimisation in risk analysis. 16.1. Introduction. 16.2. Optimisation Methods. 16.3. Risk Analysis Modelling and Optimisation. 16.4. Working Example: Optimal Allocation of Mineral Pots. 17. Checking and validating a model. 17.1. Spreadsheet Model Errors. 17.2. Checking Model Behaviour. 17.3. Comparing Predictions Against Reality. 18. Discounted cashflow modelling. 18.1. Useful Time Series Models of Sales and Market Size. 18.2. Summing Random Variables. 18.3. Summing Variable Margins on Variable Revenues. 18.4. Financial Measures in Risk Analysis. 19. Project risk analysis. 19.1. Cost Risk Analysis. 19.2. Schedule Risk Analysis. 19.3. Portfolios of risks. 19.4. Cascading Risks. 20. Insurance and finance risk analysis modelling. 20.1. Operational Risk Modelling. 20.2. Credit Risk. 20.3. Credit Ratings and Markov Chain Models. 20.4. Other Areas of Financial Risk. 20.5. Measures of Risk. 20.6. Term Life Insurance. 20.7. Accident Insurance. 20.8. Modelling a Correlated Insurance Portfolio. 20.9. Modelling Extremes. 20.10. Premium Calculations. 21. Microbial food safety risk assessment. 21.1. Growth and Attenuation Models. 21.2. Dose-Response Models. 21.3. Is Monte Carlo Simulation the Right Approach? 21.4. Some Model Simplifications. 22. Animal import risk assessment. 22.1. Testing for an Infected Animal. 22.2. Estimating True Prevalence in a Population. 22.3. Importing Problems. 22.4. Confidence of Detecting an Infected Group. 22.5. Miscellaneous Animal Health and Food Safety Problems. I. Guide for lecturers. II. About ModelRisk. III. A compendium of distributions. III.1. Discrete and Continuous Distributions. III.2. Bounded and Unbounded Distributions. III.3. Parametric and Non-Parametric Distributions. III.4. Univariate and Multivariate Distributions. III.5. Lists of Applications and the Most Useful Distributions. III.6. How to Read Probability Distribution Equations. III.7. The Distributions. III.8. Introduction to Creating Your Own Distributions. III.9. Approximation of One Distribution with Another. III.10. Recursive Formulae for Discrete Distributions. III.11. A Visual Observation On The Behaviour Of Distributions. IV. Further reading. V. Vose Consulting. References. Index.
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