Real time detection of farm-level swine mycobacteriosis outbreak using time series modeling of the number of condemned intestines in abattoirs

Evaluating swine disease occurrence on farms using the state-space model based on meat inspection data: a time-series analysis

BACKGROUND

Data on abnormal health conditions in animals obtained from slaughter inspection are important for identifying problems in fattening management. However, methods to objectively evaluate diseases on farms using inspection data has not yet been well established. It is important to assess fattening management on farms using data obtained from slaughter inspection. In this study, we developed the state-space model to evaluate swine morbidity using slaughter inspection data.

RESULTS

The most appropriate model for each disease was constructed using the state-space model. Data on 11 diseases in slaughterhouses over the past 4 years were used to build the model. The model was validated using data from 14 farms. The local-level model (the simplest model) was the best model for all diseases. We found that the analysis of slaughter data using the state-space model could construct a model with greater accuracy and flexibility than the ARIMA model. In this study, no seasonality or trend model was selected for any disease. It is thought that models with seasonality were not selected because diseases in swine shipped to slaughterhouses were the result of illness at some point during the 6-month fattening period between birth and shipment.

CONCLUSION

Evaluation of previous diseases helps with the objective understanding of problems in fattening management. We believe that clarifying how farms manage fattening of their pigs will lead to improved farm profits. In that respect, it is important to use slaughterhouse data for fattening evaluation, and it is extremely useful to use mathematical models for slaughterhouse data. However, in this research, the model was constructed on the assumption of normality and linearity. In the future, we believe that we can build a more accurate model by considering models that assume non-normality and non-linearity.

Estimation of economic loss by carcass weight reduction of Japanese dairy cows due to infection with bovine leukemia virus

Bovine leukemia virus (BLV) infection is endemic in Japanese dairy farms. To promote the participation of farmers in BLV infection control in Japan, it is important to provide estimates of the economic losses caused by this infection. We hypothesized that decreased immune function due to BLV infection would increase visceral abnormalities, in turn reducing carcass weight. We employed mediation analysis to estimate the annual economic loss due to carcass weight reduction caused by BLV infection. Culled Holstein cows from 12 commercial dairy farms in the Nemuro and Kushiro regions of Hokkaido, Japan, were traced. Information on age and the last delivery day were collected. A non-infected culled cow was defined as a cow from which BLV provirus was not detected. A high-proviral-load (H-PVL) cow was defined as a cow whose PVL titer was above 2465 copies/50 ng DNA or 56,765 copies/10⁵ cells. A BLV-infected cow with PVL titer lower than the thresholds was categorized as low-proviral load (L-PVL). Post-mortem examination results for culled cows were collected from a meat inspection center. The hypothesis was tested by three models, using data from 222 culled dairy cows. Model 1, a generalized linear mixed-effects model, selected carcass weight as an outcome variable, BLV status and the potential confounders (lactation stage and age) as explanatory variables, and herd as a random effect. Model 2 additionally included the number of abnormal findings in the post-mortem examination (AFPE) as an explanatory variable. Model 3 applied a Bayesian generalized linear mixed model, which employed a mediator separately modeled for AFPE, to estimate the amount of direct, indirect, and total carcass weight loss with adjustment for known confounding factors. Compared to the mean carcass weight for the non-infected culled cows, the carcass weight for H-PVL culled cows was significantly decreased by 30.4 kg on average. For each increase of one in the number of AFPE, the mean carcass weight was decreased by 8.6 kg. Only the indirect effect of BLV H-PVL status on carcass weight loss through AFPE was significant, accounting for 21.6 % of the total effect on carcass weight reduction. In 2017, 73,650 culled dairy cows were slaughtered in Hokkaido, and the economic loss due to carcass weight loss caused by BLV infection that year was estimated to be US $1,391,649. In summary, unlike L-PVL cows, H-PVL status was associated with carcass weight reduction, which was partially mediated by an increase in the number of visceral abnormalities.

A time-series approach for clustering farms based on slaughterhouse health aberration data

A large amount of data is collected routinely in meat inspection in pig slaughterhouses. A time series clustering approach is presented and applied that groups farms based on similar statistical characteristics of meat inspection data over time. A three step characteristic-based clustering approach was used from the idea that the data contain more info than the incidence figures. A stratified subset containing 511,645 pigs was derived as a study set from 3.5 years of meat inspection data. The monthly averages of incidence of pleuritis and of pneumonia of 44 Dutch farms (delivering 5149 batches to 2 pig slaughterhouses) were subjected to 1) derivation of farm level data characteristics 2) factor analysis and 3) clustering into groups of farms. The characteristic-based clustering was able to cluster farms for both lung aberrations. Three groups of data characteristics were informative, describing incidence, time pattern and degree of autocorrelation. The consistency of clustering similar farms was confirmed by repetition of the analysis in a larger dataset. The robustness of the clustering was tested on a substantially extended dataset. This confirmed the earlier results, three data distribution aspects make up the majority of distinction between groups of farms and in these groups (clusters) the majority of the farms was allocated comparable to the earlier allocation (75% and 62% for pleuritis and pneumonia, respectively). The difference between pleuritis and pneumonia in their seasonal dependency was confirmed, supporting the biological relevance of the clustering. Comparison of the identified clusters of statistically comparable farms can be used to detect farm level risk factors causing the health aberrations beyond comparison on disease incidence and trend alone.

Time series analysis based on two-part models for excessive zero count data to detect farm-level outbreaks of swine echinococcosis during meat inspections

Echinococcus multilocularis is a parasite that causes highly pathogenic zoonoses and is maintained in foxes and rodents on Hokkaido Island, Japan. Detection of E. multilocularis infections in swine is epidemiologically important. In Hokkaido, administrative information is provided to swine producers based on the results of meat inspections. However, as the current criteria for providing administrative information often results in delays in providing information to producers, novel criteria are needed. Time series models were developed to monitor autocorrelations between data and lags using data collected from 84 producers at the Higashi-Mokoto Meat Inspection Center between April 2003 and November 2015. The two criteria were quantitatively compared using the sign test for the ability to rapidly detect farm-level outbreaks. Overall, the time series models based on an autoexponentially regressed zero-inflated negative binomial distribution with 60th percentile cumulative distribution function of the model detected outbreaks earlier more frequently than the current criteria (90.5%, 276/305, p<0.001). Our results show that a two-part model with autoexponential regression can adequately deal with data involving an excessive number of zeros and that the novel criteria overcome disadvantages of the current criteria to provide an earlier indication of increases in the rate of echinococcosis.