Narita Tsubasa, Kubo Meiko, Nagakura Yuichi, Sekiguchi Satoshi
Graduate School of Medicine and Veterinary Medicine, University of Miyazaki, Miyazaki, 889-1692, Japan.
Miyazaki Prefectural Institute for Public Health and Environment, Miyazaki, 889-2155, Japan.
Porcine Health Manag. 2024 Jan 23;10(1):6. doi: 10.1186/s40813-024-00355-z.
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.
从屠宰检验中获得的动物健康异常状况数据对于识别育肥管理中的问题非常重要。然而，利用检验数据客观评估农场疾病的方法尚未得到很好的确立。利用屠宰检验获得的数据评估农场的育肥管理很重要。在本研究中，我们开发了状态空间模型，以利用屠宰检验数据评估猪的发病率。
使用状态空间模型构建了每种疾病的最合适模型。利用过去4年屠宰场中11种疾病的数据来构建模型。该模型使用来自14个农场的数据进行验证。局部水平模型（最简单的模型）是所有疾病的最佳模型。我们发现，使用状态空间模型分析屠宰数据能够构建一个比自回归积分滑动平均模型（ARIMA模型）更准确、更灵活的模型。在本研究中，没有为任何疾病选择季节性或趋势模型。据认为未选择具有季节性的模型是因为运往屠宰场的猪的疾病是出生至运输期间6个月育肥期内某个时间点患病的结果。
对既往疾病的评估有助于客观了解育肥管理中的问题。我们相信，阐明农场如何管理猪的育肥将提高农场利润。在这方面，利用屠宰场数据进行育肥评估很重要，利用数学模型处理屠宰场数据非常有用。然而，在本研究中，模型是在正态性和线性假设的基础上构建的。未来，我们相信通过考虑假设非正态性和非线性的模型可以构建更准确的模型。
