重新访问COVID-19分析,使用具有和特征的高排序整数值时间序列模型类
Naushad Mamode Khan1, Ashwinee Devi Soobhug2, Noha Youssef3
1Faculty of Social Sciences and Humanities, University of Mauritius, Réduit, Mauritius.
Healthcare analytics (New York, N.Y.)
|July 31, 2023
概括
这项研究引入了先进的整数值自动回归 (INAR) 模型,以更好地捕捉COVID-19时间序列波动. 这些新的和INAR模型为传染病数据提供了改进的分析.
科学领域:
- 统计 统计 统计 统计
- 流行病学 流行病学
- 时间序列分析时间序列分析
背景情况:
- COVID-19 时间序列表现出高波动性,包括尖峰,振荡,过度分散和过多的零点.
- 传统的整数值自动回归 (INAR) 模型很难准确地表示这些复杂的特征.
研究的目的:
- 提出能够处理COVID-19时间序列特征的INAR模型的新配方.
- 探索具有和的创新分布及其两种扩展的高级INAR模型.
主要方法:
- 开发具有和创新分布的高级INAR模型.
- 扩展到双变的INAR过程中.
- 对来自南非和毛里求斯的COVID-19时间序列数据的应用.
- 模拟实验用于验证模型性能和估计.
主要成果:
- 拟议的和INAR模型证明了捕获COVID-19数据波动性和特定特征的改进能力.
- 双变量扩展为分析多个相关传染病时间序列提供了一个框架.
- 模拟研究证实了新模型和估计技术的有效性.
结论:
- 具有和创新的先进INAR模型为分析挥发性传染病时间序列提供了更强大的方法.
- 开发的模型和方法是流行病学预测和了解疾病动态的宝贵工具.
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