软指标和Poisson Kriging用于区域数据的噪声过和缩小的比较:应用于每日COVID-19发病率
Pierre Goovaerts1, Thomas Hermans2, Peter F Goossens3
1BioMedware, Inc. 167 Little lake dr., Ann Arbor, MI 48103, USA.
概括
这项研究引入了软指标战争 (IK) 来改进空间流行病学分析,提供比Poisson战争 (PK) 更现实的疾病发病率. 该IK方法有效地过噪音,并处理不同的空间尺度,以获得更好的公共卫生洞察力.
科学领域:
- 流行病学 流行病学
- 地质统计学 在地质统计学
- 空间分析 空间分析
背景情况:
- 分析空间流行病学数据在人口规模较小和空间尺度不同时会带来挑战.
- 像Poisson kriging (PK) 这样的传统方法可以产生不切实际的发病率 (负或>100%).
研究的目的:
- 提出和评估软指标战争 (IK) 作为空间疾病发病率分析的PK的替代方案.
- 为了比较IK和PK在不同空间分辨率下过噪声和估计速率方面的性能.
主要方法:
- 采用了地理统计技术,特别是Poisson kriging (PK) 和软指标 kriging (IK).
- 在比利时市政府 (2020-2021) 的每日COVID-19发病率上应用方法.
- 在市政和1公里电网层面推导噪声过的发病率.
主要成果:
- 软指标策划 (IK) 方法避免了负估计,与PK相比,其平滑效应较小.
- 在聚合后,IK表现出与观察到的市级利率更好的一致性,特别是当初始利率为零时.
- 这两种方法都用于在多个尺度上评估空间疾病模式.
结论:
- 软指标策划 (IK) 为分析空间疾病发病率数据提供了更强大和更现实的方法.
- IK有效地解决了PK的局限性,为公共卫生监测提供了更好的估计.
- 该方法增强了通过不同空间分辨率传播疾病的分析.
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