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在Poisson辅助平滑稀疏张量分解计数数据中检测热点
Yujie Zhao1, Xiaoming Huo2, Yajun Mei2
1Biostatistics and Research Decision Sciences Department, Merck & Co., Inc, North Wales, PA, USA.
Journal of applied statistics
|October 9, 2023
概括
我们开发了Poisson辅助的光滑稀疏张量分解 (PoSSTenD) 来检测和定位传染病热点. 该方法分析了空间,时间和分类计数数据,以便及时进行公共卫生干预.
科学领域:
- 公共卫生监督 公共卫生监督
- 生物统计学 生物统计学
- 数据科学数据科学数据科学
背景情况:
- 来自生物监测和医疗保健应用程序的计数数据对于监测传染病至关重要.
- 及时检测和定位传染病热点对于有效的公共卫生反应至关重要.
研究的目的:
- 为了引入一种新的方法,Poisson辅助了光滑稀疏张量分解 (PoSSTenD),用于检测和定位传染病热点.
- 为在疾病监测中分析时空类别计数数据提供一个强大的框架.
主要方法:
- 将计数数据表示为一个三维张量 (空间,时间,分类).
- 将张量纳入波桑回归模型,将传染率分解为全球趋势和当地热点.
- 使用累积总和 (CUSUM) 控制图来检测热点,并使用 LASSO 类型的稀疏估计来定位.
主要成果:
- 该PoSSTenD方法成功地检测和定位传染病热点.
- 通过数值模拟和美国传染病真实数据集的验证证明了该方法的有效性.
结论:
- PoSSTenD为增强传染病监测系统提供了一个强大的工具.
- 该方法允许快速识别异常感染率,促进有针对性的公共卫生干预.
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