一个高斯过程回归和波形变换时间序列方法来建模流感A
1School of Mathematical and Statistical Science, College of Sciences, University of Texas Rio Grande Valley, USA; Department of Statistics and Actuarial Science, College of Basic and Applied Sciences, University of Ghana, Ghana; Department of Computer Science, Ashesi University, No. 1 University Avenue, Berekuso, Eastern Region, Ghana.
Computers in biology and medicine
|November 16, 2024
概括
这项研究模拟了使用高斯过程回归和波形变换传播的流感A病毒. 综合方法准确预测病例并识别流行病异常,改善公共卫生规划.
科学领域:
- 流行病学 流行病学
- 计算生物学 计算生物学
- 时间序列分析时间序列分析
背景情况:
- A型流感病毒对全球健康和经济构成重大挑战.
- 准确的建模对于理解传输动态和干预效果至关重要.
- 现有的机械模型在捕捉复杂时间模式的能力上有所不同.
研究的目的:
- 为了建模流感A病毒传播的时间动态.
- 为了评估高斯过程回归 (GPR) 与波形变换相结合的有效性,用于预测.
- 为了确定流感A病例数据中的显著模式和异常.
主要方法:
- 利用连续波段变换 (CWT),离散波段变换 (DWT) 和波段功率光谱进行时间序列分析 (2009-2023年).
- 应用高斯过程回归 (GPR),一种非参数贝叶斯方法,用于捕捉非线性趋势.
- 集成的GPR与DWT无效化技术,以提高预测准确度.
主要成果:
- GPR有效地建模了流感A数据中的非线性趋势.
- 波形变换为疾病模式提供了时间频率局部化的洞察力.
- 集成的GPR-DWT模型在预测流感A病例方面表现优于传统的ARIMA和霍尔特-温特ETS模型.
- 发现了与流行病和季节性变化相对应的重大异常.
结论:
- 将波波变换分析与GPR结合起来,为了解和预测传染病动态提供了一种强大的方法.
- 这种综合方法为公共卫生规划和干预策略提供了宝贵的见解.
- 该方法显示了对其他呼吸道病毒的更广泛应用的潜力.
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