一种新的变时间系数回归方法,用于分析传染病数据
Juxin Liu1, Brandon Bellows2, X Joan Hu3
1Department of Mathematics and Statistics, University of Saskatchewan, Saskatoon, S7N 5E6, Canada. liu@math.usask.ca.
这项研究利用时间变化的模型探索了COVID-19病例数和死亡人数之间的关系. 局部多项式回归模型有效预测传染病趋势,优于复杂模式的零碎线性模型.
科学领域:
- 流行病学和生物统计学
- 时间序列分析时间序列分析
- 传染病建模 传染病建模
背景情况:
- 在全球大流行期间,准确预测冠状病毒 (SARS-COV-2) 相关死亡和住院情况至关重要.
- 现有的研究经常使用静态模型,无法捕捉病毒影响的不断变化的性质.
研究的目的:
- 为了调查新冠病毒 (SARS-COV-2) 病例数和死亡数之间的时间序列之间的滞后依赖.
- 开发和评估可变时间系数模型,用于预测传染病结果.
- 评估这些模型对其他传染病和动态滞后依赖性的适用性.
主要方法:
- 采用了时间变化的系数模型,特别是局部多项式回归和零碎线性回归.
- 分析了加拿大省级和国家级累计病例和死亡人数数据.
- 利用样本外预测进行严格的模型性能评估.
主要成果:
- 无论是局部多项式模型还是片式线性时间变量模型,都在预测COVID-19趋势方面表现出了有效的表现.
- 局部多项式回归模型的性能通常优于零碎线性模型,特别是在复杂的滞后关系中.
- 提出的方法很容易使用现有的R包实现.
结论:
- 时间变化的系数模型为分析和预测传染病动态提供了强大的框架.
- 选择模型 (例如,局部多项式回归) 对于准确地捕捉疾病指标之间的不断变化的关系至关重要.
- 这种方法为流行病学预测和了解疾病进展提供了灵活有效的工具.
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