Veterinary Public Health and Food Safety Centre for Asia Pacific (VPHCAP), Faculty of Veterinary Medicine, Chiang Mai University, Chiang Mai 50100, Thailand; Center of Excellence in Veterinary Public Health, Faculty of Veterinary Medicine Chiang Mai University, Chiang Mai 50100, Thailand.
Bureau of Livestock Standards and Certification, Department of Livestock Development, Bangkok 10400, Thailand.
Prev Vet Med. 2022 Oct;207:105706. doi: 10.1016/j.prevetmed.2022.105706. Epub 2022 Jul 5.
Occurrences of foot and mouth disease (FMD) outbreaks in cattle farms in Thailand have been significantly harmful to the cattle industry for the past decade. A prediction of FMD outbreaks based on relevant risk factors with a high prediction accuracy is important for authorities to develop a plan for preventing the outbreaks. Data-driven tools are widely accepted for their prediction abilities, but an application of these techniques to FMD outbreak prediction is very limited. The objectives of this study were to develop prediction models of FMD outbreaks among cattle farms using machine learning (ML) classification algorithms including classification tree (CT), random forests (RF), and Chi-squared automatic interaction detection (CHAID) and to compare the predictive performance of the developed models. Data from 225 FMD and 608 non-FMD outbreak farms from an endemic setting were analyzed using ML methods. The CT, RF, and CHAID methods were utilized to develop predictive models, and their prediction capabilities were compared. The results showed that models developed using ML methods have an acceptable to excellent ability to predict the occurrence of FMD outbreaks. The RF model had the highest accuracy and the value of area under the operating characteristic curve in predicting the occurrence of an FMD outbreak. Meanwhile, the CT and CHAID models delivered comparable results. In this study, we demonstrated the capability of machine learning algorithms to predict FMD outbreaks using actual FMD outbreak data from the endemic setting and provided a new insight into the prediction of FMD outbreaks. The ML techniques demonstrated herein may be used as a prediction tool by the relevant authorities to predict the occurrence of FMD outbreaks in cattle farms.
口蹄疫（FMD）在泰国牛场的爆发过去十年对牛业造成了重大危害。基于相关风险因素进行高准确性的 FMD 爆发预测，对当局制定预防爆发计划至关重要。数据驱动工具因其预测能力而被广泛接受，但将这些技术应用于 FMD 爆发预测的应用非常有限。本研究的目的是使用机器学习（ML）分类算法（包括分类树（CT）、随机森林（RF）和卡方自动交互检测（CHAID））开发牛场 FMD 爆发预测模型，并比较开发模型的预测性能。使用 ML 方法分析了来自地方性环境的 225 个 FMD 和 608 个非 FMD 爆发场的数据。使用 CT、RF 和 CHAID 方法开发预测模型，并比较它们的预测能力。结果表明，使用 ML 方法开发的模型具有可接受至优秀的预测 FMD 爆发的能力。RF 模型在预测 FMD 爆发的发生方面具有最高的准确性和操作特征曲线下面积值。同时，CT 和 CHAID 模型的结果相当。在本研究中，我们展示了机器学习算法使用来自地方性环境的实际 FMD 爆发数据预测 FMD 爆发的能力，并为 FMD 爆发的预测提供了新的见解。本文所述的 ML 技术可被相关当局用作预测工具，以预测牛场 FMD 爆发的发生。
