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.

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.

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

Mycobacteriosis in swine is a common zoonosis found in abattoirs during meat inspections, and the veterinary authority is expected to inform the producer for corrective actions when an outbreak is detected. The expected value of the number of condemned carcasses due to mycobacteriosis therefore would be a useful threshold to detect an outbreak, and the present study aims to develop such an expected value through time series modeling. The model was developed using eight years of inspection data (2003 to 2010) obtained at 2 abattoirs of the Higashi-Mokoto Meat Inspection Center, Japan. The resulting model was validated by comparing the predicted time-dependent values for the subsequent 2 years with the actual data for 2 years between 2011 and 2012. For the modeling, at first, periodicities were checked using Fast Fourier Transformation, and the ensemble average profiles for weekly periodicities were calculated. An Auto-Regressive Integrated Moving Average (ARIMA) model was fitted to the residual of the ensemble average on the basis of minimum Akaike's information criterion (AIC). The sum of the ARIMA model and the weekly ensemble average was regarded as the time-dependent expected value. During 2011 and 2012, the number of whole or partial condemned carcasses exceeded the 95% confidence interval of the predicted values 20 times. All of these events were associated with the slaughtering of pigs from three producers with the highest rate of condemnation due to mycobacteriosis.