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在猪群中使用机器学习进行感染预测
Avishai Halev1, Beatriz Martínez-López2, Maria Clavijo3
1Department of Mathematics, University of California, Davis, Davis, CA, USA.
Scientific reports
|October 18, 2023
概括
一个新的机器学习模型提前几天预测猪感染,使用农场密度和小猪数据. 这种早期预警系统有助于预防猪肉行业的疾病.
科学领域:
- 动物科学动物科学
- 兽医医学 兽医医学 兽医医学
- 数据科学数据科学数据科学
背景情况:
- 猪病对猪肉行业的生产率和动物福利产生重大影响,造成了巨大的经济损失.
- 疾病爆发的早期检测对于生猪养殖中有效的预防和缓解策略至关重要.
研究的目的:
- 开发和评估一种机器学习模型,用于每天预测猪感染的出现.
- 确定用于早期猪病爆发检测的关键预测特征.
- 评估模型在不同猪生产系统中的通用性和性能.
主要方法:
- 利用机器学习来预测猪生产系统中每天出现的感染.
- 确定了关键预测因素:附近的农场密度,历史测试率,小猪库存,孕期料消费,风速和方向.
- 在两个不同的猪生产系统上,对7天和30天的预先爆发预测评估了模型性能.
主要成果:
- 该模型表现出对猪感染的良好预测能力,对一般疾病和特定病原体如PRRSV,PEDV,Influenza A和Mycoplasma hyopneumoniae等疾病的准确度高达[公式:参见文本].
- 确定了有助于准确预测感染的关键特征.
- 分析了数据可用性和细粒度对不同生产环境中的模型性能的影响.
结论:
- 开发的机器学习模型提供了每天的感染概率,作为兽医和利益相关者的宝贵工具.
- 能够及时支持预防和控制策略,加强猪生产中的疾病管理.
- 强调数据驱动方法在改善猪肉行业动物健康和生产率方面的潜力.
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