COWAVE:一个标记的COVID-19波浪数据集用于构建预测模型
Melpakkam Pradeep1, Karthik Raman2,3,4
1Department of Chemical Engineering, Indian Institute of Technology (IIT) Madras, Chennai, India.
PloS one
|July 25, 2023
概括
这项研究使用世界卫生组织的数据来定义流行病浪潮,创建了一个标记为COVID-19的数据集. 该数据集有助于构建用于早期波浪检测的预测模型,使流行病学家受益.
科学领域:
- 流行病学 流行病学
- 数据科学数据科学数据科学
- 公共卫生 公共卫生
背景情况:
- 由于COVID-19大流行给全球医疗保健系统带来了巨大压力,需要进行强有力的数据分析.
- 广泛的COVID-19数据公开可用,为流行病学研究提供了机会.
- 了解和预测流行病浪潮对于资源管理至关重要.
研究的目的:
- 从世界卫生组织 (WHO) 收集全球COVID-19病例数据.
- 通过定义流行病波来开发一个标记的数据集,用于监督学习.
- 建立预测模型的基准,并证明数据集对波浪预测的有用性.
主要方法:
- 从世卫组织网站收集的COVID-19病例数据.
- 定义了多个标准来识别流行病波,以创建数据标签.
- 使用极端梯度提升 (XGBoost) 模型进行基线性能评估.
主要成果:
- 创建了一个新的,标记全球COVID-19波的数据集.
- 该数据集被证明是有效的培训监督学习分类器.
- 为未来的预测模型建立了基线性能标准.
结论:
- 精心策划的数据集是流行病学家和研究人员的宝贵资源.
- 预测未来的COVID-19浪潮的早期预测是由这个数据集促进的.
- 该数据集支持开发用于流行病监测的先进机器学习模型.
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