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

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.

The impact of echinococcosis interventions on economic outcomes in Qinghai Province of China: Evidence from county-level panel data

Introduction

Echinococcosis can incur substantial economic losses for the livestock industry by causing organ condemnation, delayed growth, and reduced meat and wool output and quality in sheep and cattle, as well as increased surgery costs, hospital care, and decreased productivity in humans. Yet echinococcosis could be prevented and controlled by interventions, such as dog management and deworming, lamb vaccination, slaughter management, and training and public education.

Methods

Exploiting temporal and spatial variations in the number of intervention measures implemented in 39 counties in Qinghai province of China in 2015-2020, this study assesses the economic impact of echinococcosis interventions using a dynamic difference-in-differences model.

Results

The results suggest that echinococcosis interventions brought about substantial economic gains measured by per capita net income of rural residents and per capita gross output of animal husbandry. These economic gains are greater in non-pastoral counties (with a gain in per capita net income of rural residents of 3,308 yuan and a gain per capita gross output of animal husbandry of 1,035 yuan) than in pastoral counties (with a gain in per capita net income of rural residents of 1,372 yuan and a gain per capita gross output of animal husbandry of 913 yuan). They are also greater in counties with echinococcosis infection level-2 (with a human infection rate of 0.1-1% or a dog infection rate of 1-5%) than infection level-1 counties (with a human prevalence rate ≥1% or a dog infection rate ≥5%).

Discussion

Not only will these economic gains encourage livestock farmers to strengthen their echinococcosis prevention and control practices, but they will also inform public policy on zoonotic disease prevention and control in China and other countries alike.

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.