用异常检测方法对序列数据用于识别疾病爆发的应用进行评估
José Manuel Díaz-Cao1,2, Xin Liu3, Jeonghoon Kim3
1Center for Animal Disease Modeling and Surveillance (CADMS), Department of Medicine & Epidemiology, School of Veterinary Medicine, University of California, Davis, USA. jmdchh@gmail.com.
Veterinary research
|September 8, 2023
概括
使用序列数据检测异常有效地识别了猪生殖和呼吸系统综合征 (PRRS) 在动物生产中的新菌株. 长期短期记忆 (LSTM) 和贝叶斯方法在农场层面的早期疾病检测方面表现有前途.
科学领域:
- 兽医流行病学 兽医流行病学
- 生物信息学是一种生物信息学.
- 机器学习在动物健康中的应用
背景情况:
- 早期发现疾病对于管理动物生产系统至关重要.
- 猪生殖和呼吸系统综合征 (PRRS) 构成重大经济威胁.
- 序列数据为监测疾病出现提供了一个新的来源.
研究的目的:
- 用序列数据评估用于识别新的PRRS菌株的异常检测方法.
- 为了比较各种方法和数据类型用于疫情检测的性能.
- 评估序列数据在畜牧业早期疾病监测中的潜力.
主要方法:
- 利用PRRS的序列数据来追踪新菌株的出现.
- 评估了24种异常检测方法 (机器学习,回归,时间序列,控制图表).
- 使用PCR阳性,PCR请求和实验室请求时间序列进行性能比较,评估检测概率 (POD),灵敏度 (Se),第一周的POD (POD1w) 和背景报警率 (BAR).
主要成果:
- 序列数据时间序列在检测新菌株方面表现优于其他数据类型.
- 高POD,Se和POD1w仅在大型合成疫情中实现.
- 长期短期记忆 (LSTM) 和贝叶斯方法表现出卓越的性能.
结论:
- 使用序列数据检测异常显示出在多个农场中识别新出现的疾病的前景.
- 需要进一步的研究,以提高高度可变的时间序列的检测.
- 这种方法为早期发现疾病和利用常规实验室数据提供了有价值的工具.
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