关于季节性INAR过程的一些发展,适用于流感数据
Fatimah E Almuhayfith1, Emmanuel W Okereke2, Manik Awale3
1Department of Mathematics and Statistics, College of Science, King Faisal University, Alahsa, 31982, Saudi Arabia. falmuhaifeez@kfu.edu.sa.
Scientific reports
|December 12, 2023
概括
这项研究引入了季节性流感病例计数的新灵活模型,解决了零通货膨胀和过度分散等常见数据问题. 拟议的模型在分析现实世界流行病数据方面,与现有方法相比,表现优越.
科学领域:
- 流行病学 流行病学
- 生物统计学 生物统计学
- 时间序列分析时间序列分析
背景情况:
- 流感流行病数据表现出季节性,经常带来诸如零通货膨胀,零通货紧缩,过度分散和不足分散等挑战.
- 传统的计数数据模型可能无法充分捕捉疾病发病率固有的这些复杂特征.
- 了解和建模这些特征对于准确的流行病预测和公共卫生干预至关重要.
研究的目的:
- 为具有季节性自回归结构的非负数整数值时间序列数据引入灵活的统计模型.
- 解决和解释在流感病例计数数据中观察到的共同特征,包括季节性和分散问题.
- 评估拟议模型的性能与分析流感流行病数据的现有方法相比.
主要方法:
- 开发一个灵活的季节整数自回归 (INAR) 模型,用于非负数整数值的时间序列.
- 讨论一般季节性INAR (p) 模型的概率性质.
- 在专门的季节性INAR模型中应用三个估计方法来估计参数.
- 通过模拟研究评估估计程序的性能评估.
- 使用拟议模型分析每周流感数据.
主要成果:
- 拟议的季节性INAR模型有效地捕捉了流感计数数据的特征,包括季节性和分散性.
- 模拟研究证实了季节性INAR模型开发的估计方法的令人满意的性能.
- 对来自德国巴登-维尔特堡的每周流感数据的实证分析表明,该模型的实际应用性.
- 建议的模型在分析提供的流感数据集时优于现有模型.
结论:
- 开发的灵活的季节整数自回归模型为分析具有季节性模式的计数时间序列数据提供了强大的框架.
- 该模型处理零通胀和过度分散的能力使其成为流感数据分析的宝贵工具.
- 拟议的模型比现有的理解和预测流感流行病的方法提供了更好的准确性和性能.
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