预测资源有限的环境中的Kyasanur森林疾病,使用基于事件的监测和转移学习
Ravikiran Keshavamurthy1,2, Lauren E Charles3,4
1Pacific Northwest National Laboratory, Richland, WA, 99354, USA.
Scientific reports
|July 8, 2023
概括
基于事件的监测 (EBS) 数据,包括新闻和搜索趋势,显著改善了Kyasanur森林疾病 (KFD) 的预测. 转移学习 (TL) 技术有效地预测了新地区的KFD,帮助疾病控制工作.
科学领域:
- 流行病学 流行病学
- 机器学习 机器学习
- 公共卫生 公共卫生
背景情况:
- 凯萨努尔森林病 (KFD) 是一种新兴的动物病,有报道称它已经蔓延到流行地区之外.
- 现有的疾病监测系统不足以有效控制和预防KFD.
- 预测KFD爆发是个挑战,尤其是在数据有限的新区域.
研究的目的:
- 用天气数据与基于事件的监测 (EBS) 数据进行时间序列模型比较,用于预测每月的KFD病例.
- 在国家和区域层面评估极端梯度增强 (XGB) 和长短期记忆 (LSTM) 模型的有效性.
- 应用转移学习 (TL) 技术来预测KFD在数据稀缺的疫情地区.
主要方法:
- 时间序列模型 (XGB,LSTM) 使用天气数据和EBS信息 (新闻媒体,互联网搜索趋势) 进行了装配.
- 使用转移学习 (TL) 来利用来自特有地区的数据来预测新爆发地区的情况.
- 模型性能通过比较带有和没有EBS数据的预测和对基线模型的TL评估来评估.
主要成果:
- 与仅仅天气数据相比,包括EBS数据在所有模型中大大提高了预测性能.
- 在国家和区域两级,XGB模型显示出最好的预测准确度.
- 在预测新型疫情地区的KFD病例方面,TL技术显著优于基线模型.
结论:
- 基于事件的监控 (EBS) 数据与先进的机器学习相结合,增强了KFD预测能力.
- 转移学习 (TL) 提供了一个有希望的方法,用于在数据有限的环境中预测疾病.
- 这些新的方法支持对KFD等新出现的动物传染病威胁做出更明智的决策.
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