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

TL;DR

This study uses a state-space model to evaluate swine diseases on farms using slaughter inspection data, offering a more accurate and flexible approach than traditional methods.

Contribution

The study introduces a state-space model for evaluating swine diseases using slaughter inspection data, showing greater accuracy than ARIMA models.

Findings

The local-level state-space model was the best fit for all 11 diseases analyzed.

The state-space model outperformed ARIMA in accuracy and flexibility for evaluating swine diseases.

No seasonality or trend models were selected, likely due to the 6-month fattening period obscuring seasonal patterns.

Abstract

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.

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.

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 online version contains supplementary material available at 10.1186/s40813-024-00355-z.

Figures