scholarly journals Statistical Monitoring of Condemnations from Slaughterhouses

2015 ◽  
Vol 7 (1) ◽  
Author(s):  
Flavie Vial ◽  
Sarah Thommen ◽  
Leonhard Held
Keyword(s):  
Time Series ◽  
False Positive ◽  
Disease Outbreaks ◽  
Probability Of Detection ◽  
Meat Inspection ◽  
Herd Health ◽  
Statistical Monitoring ◽  
Indirect Indicator ◽  
Inspection Data

Whole carcass condemnations (WCC) following meat inspection could be a valuable indirect indicator of national herd health to monitor. We evaluate the performance of the improved Farrington algorithm for the detection of simulated outbreaks in meat inspection data. Disease outbreaks of random sizes (leading to increased WCC at slaughter) were simulated in the time series of the number of cattle slaughtered and condemned in Switzerland between 2007 and 2012. Overall, the improved Farrington algorithm led to low false positive rates but the probability of detection was low for small outbreaks.

scholarly journals Trigonometric Regression for Analysis of Public Health Surveillance Data

2014 ◽  
Vol 2014 ◽  
pp. 1-13 ◽  
Author(s):  
Steven E. Rigdon ◽  
George Turabelidze ◽  
Ehsan Jahanpour
Keyword(s):  
Public Health ◽  
False Positive ◽  
Disease Surveillance ◽  
Communicable Disease ◽  
False Positive Rate ◽  
Disease Outbreaks ◽  
Probability Of Detection ◽  
Surveillance Data ◽  
False Alarms ◽  
Statistical Process

Statistical challenges in monitoring modern biosurveillance data are well described in the literature. Even though assumptions of normality, independence, and stationarity are typically violated in the biosurveillance data, statistical process control (SPC) charts adopted from industry have been widely used in public health for communicable disease monitoring. But, blind usage of SPC charts in public health that ignores the characteristics of disease surveillance data may result in poor detection of disease outbreaks and/or excessive false-positive alarms. Thus, improved biosurveillance systems are clearly needed, and participation of statisticians knowledgeable in SPC alongside epidemiologists in the design and evaluation of such systems can be more productive. We describe and study a method for monitoring reportable disease counts using a Poisson distribution whose mean is allowed to vary depending on the week of the year. The seasonality is modeled by a trigonometric function whose parameters can be estimated by some baseline set of data. We study the ability of such a model to detect an outbreak. Specifically, we estimate the probability of detection (POD), the average number of weeks to signal given that a signal has occurred (conditional expected delay, or CED), and the false-positive rate (FPR, the average number of false-alarms per year).

scholarly journals A simulation study on the statistical monitoring of condemnation rates from slaughterhouses for syndromic surveillance: an evaluation based on Swiss data

2015 ◽  
Vol 143 (16) ◽  
pp. 3423-3433 ◽  
Author(s):  
F. VIAL ◽  
S. THOMMEN ◽  
L. HELD
Keyword(s):  
Syndromic Surveillance ◽  
Animal Health ◽  
False Positive Rate ◽  
Probability Of Detection ◽  
Meat Inspection ◽  
Small Range ◽  
Post Mortem ◽  
Multivariate System ◽  
Inspection Data ◽  
Reporting Frequency

SUMMARYSyndromic surveillance (SyS) systems currently exploit various sources of health-related data, most of which are collected for purposes other than surveillance (e.g. economic). Several European SyS systems use data collected during meat inspection for syndromic surveillance of animal health, as some diseases may be more easily detected post-mortem than at their point of origin or during the ante-mortem inspection upon arrival at the slaughterhouse. In this paper we use simulation to evaluate the performance of a quasi-Poisson regression (also known as an improved Farrington) algorithm for the detection of disease outbreaks during post-mortem inspection of slaughtered animals. When parameterizing the algorithm based on the retrospective analyses of 6 years of historic data, the probability of detection was satisfactory for large (range 83–445 cases) outbreaks but poor for small (range 20–177 cases) outbreaks. Varying the amount of historical data used to fit the algorithm can help increasing the probability of detection for small outbreaks. However, while the use of a 0·975 quantile generated a low false-positive rate, in most cases, more than 50% of outbreak cases had already occurred at the time of detection. High variance observed in the whole carcass condemnations time-series, and lack of flexibility in terms of the temporal distribution of simulated outbreaks resulting from low reporting frequency (monthly), constitute major challenges for early detection of outbreaks in the livestock population based on meat inspection data. Reporting frequency should be increased in the future to improve timeliness of the SyS system while increased sensitivity may be achieved by integrating meat inspection data into a multivariate system simultaneously evaluating multiple sources of data on livestock health.

scholarly journals Agricultural Holdings and Slaughterhouses’ Impact on Patterns of Pathological Findings Observed during Post-Mortem Meat Inspection

Animals ◽  
2021 ◽  
Vol 11 (5) ◽  
pp. 1442
Author(s):  
Johannes Klinger ◽  
Beate Conrady ◽  
Marina Mikula ◽  
Annemarie Käsbohrer
Keyword(s):  
Multivariate Analysis ◽  
Feedback System ◽  
Meat Inspection ◽  
Post Mortem ◽  
Pathological Findings ◽  
Herd Health ◽  
Post Mortem Findings ◽  
Explained Variance ◽  
Time Periods ◽  
Inspection Data

Meat inspection data can provide valuable information about herd health to producers, veterinarians and veterinary authorities and can be used as a feedback system for farmers to improve their herd management. The aim of this study was to analyse the influence of agricultural holdings, slaughterhouses and time periods (quarters) on the occurrence and composition of the prevalence of post-mortem findings of 4 million pigs slaughtered in Austria in 2016, by applying a permutation multivariate analysis of variance. Pneumonia (21.9%) and milk spots (19.9%) were the most frequently recorded conditions. Our analysis indicated a statistically significant influence of all three considered factors (agricultural holdings, slaughterhouses and periods) on the prevalence of post-mortem findings. The observed prevalence could not only be explained by the differences between the farms of origin and slaughterhouses but also by the variability within the slaughterhouses. Much of the explained variance of the prevalence was due to differences between producers (mean R2 = 0.61), followed by slaughterhouses (mean R2 = 0.19) and period (mean R2 = 0.05). To meet the demand for a valid feedback system to farmers and attending veterinarians, a robust and ideally more detailed recording of frequent pathologies, especially those affecting the respiratory tract and the liver, should be developed.

Assessing the Use of Biometric Ear Tags as Body Temperature Monitoring Devices in Swine

2021 ◽  
Vol 99 (Supplement_2) ◽  
pp. 45-46
Author(s):  
Ellie A Amerson ◽  
Harrison Moss ◽  
Suresh Kumar ◽  
Terry D Brandebourg
Keyword(s):  
Body Temperature ◽  
Pearson Correlation ◽  
Disease Outbreaks ◽  
Correlation Coefficients ◽  
Raspberry Pi ◽  
Herd Health ◽  
Swine Industry ◽  
Sickness Behaviors ◽  
Large Production ◽  
Monitoring Devices

Abstract It is difficult to detect the subtle changes associated with sickness behaviors in individual pigs early enough to prevent disease outbreaks in group housing settings within large production facilities. This failure results in significant losses to the swine industry. Strategies that allow early detection of parameters such as febrile responses could therefore significantly improve herd health and producer profitability. Our objective was to determine if the use of a biometric ear tag capable of measuring temperature could be used to accurately monitor body temperature in swine. To accomplish this, 42-d-old pigs (n = 21) were fitted with biometric ear tags for 35 d. These devices continuously measured auricular skin temperature and allowed data collection via a paired raspberry pi aggregator. During this period, repeated epidermal temperatures were also taken daily on the rump, shoulder, and ear using a clinical grade infrared thermometer. Correlation analysis using the PROC CORR procedure of SAS was then conducted to determine the ability of the biometric device to estimate body temperature relative to estimates from the clinical device. Infrared temperature readings for the ear significantly correlated with those taken at the shoulder (P < 0.0001) and rump (P < 0.0001). Importantly, temperature readings measured by the biometric ear tags also significantly correlated with infrared readings at the ear (P < 0.0001), shoulder (P < 0.0001) and rump (P < 0.0001) with Pearson Correlation coefficients of 0.51, 0.21, and 0.23, respectively. Collectively, these data support the hypothesis that the biometric ear tag device tested during this trial can be used to continuously monitor body temperature in young swine. These results indicate that further efforts to develop these devices as novel herd health monitoring devices is indeed warranted with the next step involving the assessment of their ability to detect physiological changes in body temperature.

A Review of Crack Detection In-Line Inspection Case Studies

2010 ◽  
Author(s):  
Neil Bates ◽  
David Lee ◽  
Clifford Maier
Keyword(s):  
Crack Growth ◽  
Case Studies ◽  
Crack Detection ◽  
Growth Parameters ◽  
Probability Distributions ◽  
Low Frequency ◽  
Probability Of Failure ◽  
Probability Of Detection ◽  
Inspection Data

This paper describes case studies involving crack detection in-line inspections and fitness for service assessments that were performed based on the inspection data. The assessments were used to evaluate the immediate integrity of the pipeline based on the reported features and the long-term integrity of the pipeline based on excavation data and probabilistic SCC and fatigue crack growth simulations. Two different case studies are analyzed, which illustrate how the data from an ultrasonic crack tool inspection was used to assess threats such as low frequency electrical resistance weld seam defects and stress corrosion cracking. Specific issues, such as probability of detection/identification and the length/depth accuracy of the tool, were evaluated to determine the suitability of the tool to accurately classify and size different types of defects. The long term assessment is based on the Monte Carlo method [1], where the material properties, pipeline details, crack growth parameters, and feature dimensions are randomly selected from certain specified probability distributions to determine the probability of failure versus time for the pipeline segment. The distributions of unreported crack-related features from the excavation program are used to distribute unreported features along the pipeline. Simulated crack growth by fatigue, SCC, or a combination of the two is performed until failure by either leak or rupture is predicted. The probability of failure calculation is performed through a number of crack growth simulations for each of the reported and unreported features and tallying their respective remaining lives. The results of the probabilistic analysis were used to determine the most effective and economical means of remediation by identifying areas or crack mechanisms that contribute most to the probability of failure.

scholarly journals The Potential of Post-Mortem Carcass Assessments in Reflecting the Welfare of Beef and Dairy Cattle

Animals ◽  
2019 ◽  
Vol 9 (11) ◽  
pp. 959
Author(s):  
Melody Knock ◽  
Grace A. Carroll
Keyword(s):  
Regression Analysis ◽  
Dairy Cattle ◽  
Body Condition ◽  
Skin Lesions ◽  
Meat Inspection ◽  
Carcass Weight ◽  
Post Mortem ◽  
Lesion Score ◽  
Inspection Data ◽  
General Indicator

There is increasing interest in utilizing meat inspection data to help inform farmers of the health and welfare of their herds. The aim of this study was to determine whether ante-mortem measures of welfare in beef and dairy cattle (N = 305) were associated with post-mortem measures at a United Kingdom (UK) abattoir. Multiple regression analysis was used to determine the ability of ante-mortem measures of lameness, cleanliness, skin lesions, hair loss and body condition in predicting hot carcass weight and the frequency of carcass bruising. For beef cattle, lameness score (p = 0.04), cleanliness score (p = 0.02) and age (p < 0.001), were predictors of carcass bruise score while lameness score (p = 0.03), body condition (p = 0.01) and sex (p < 0.001) were predictors of hot carcass weight. For dairy cattle, sex (p < 0.001) and slaughter day (p < 0.001) were predictors of carcass bruise score while skin lesion score (p = 0.01), body condition (p < 0.001), age (p < 0.001), slaughter day (p < 0.001) and number of moves (p = 0.01) were predictors of hot carcass weight. These results suggest that recording carcass weight and carcass bruising at meat inspection may have potential as a general indicator of health and welfare status in cattle. However, animal characteristics and variables, such as slaughter day and abattoir staffing, should be taken into account when interpreting the results.

scholarly journals Deviations from normal distributions in artificial and real time series: a false positive prescription

2019 ◽  
Vol 489 (2) ◽  
pp. 2117-2129 ◽  
Author(s):  
Paul J Morris ◽  
Nachiketa Chakraborty ◽  
Garret Cotter
Keyword(s):  
Time Series ◽  
False Positive ◽  
Functional Form ◽  
False Positive Rate ◽  
Light Curves ◽  
Artificial Light ◽  
Positive Rate ◽  
Functional Forms ◽  
Distribution Of Power ◽  
Normally Distributed

ABSTRACT Time-series analysis allows for the determination of the Power Spectral Density (PSD) and Probability Density Function (PDF) for astrophysical sources. The former of these illustrates the distribution of power at various time-scales, typically taking a power-law form, while the latter characterizes the distribution of the underlying stochastic physical processes, with Gaussian and lognormal functional forms both physically motivated. In this paper, we use artificial time series generated using the prescription of Timmer & Koenig to investigate connections between the PDF and PSD. PDFs calculated for these artificial light curves are less likely to be well described by a Gaussian functional form for steep (Γ⪆1) PSD indices due to weak non-stationarity. Using the Fermi LAT monthly light curve of the blazar PKS2155-304 as an example, we prescribe and calculate a false positive rate that indicates how likely the PDF is to be attributed an incorrect functional form. Here, we generate large numbers of artificial light curves with intrinsically normally distributed PDFs and with statistical properties consistent with observations. These are used to evaluate the probabilities that either Gaussian or lognormal functional forms better describe the PDF. We use this prescription to show that PKS2155-304 requires a high prior probability of having a normally distributed PDF, $P(\rm {G})~$ ≥ 0.82, for the calculated PDF to prefer a Gaussian functional form over a lognormal. We present possible choices of prior and evaluate the probability that PKS2155-304 has a lognormally distributed PDF for each.

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