Cazelles Bernard, Chavez Mario, Magny Guillaume Constantin de, Guégan Jean-Francois, Hales Simon
CNRS UMR 7625, Ecole Normale Supérieure, 46 rue d'Ulm, 75230 Paris, France IRD UR GEODES, 93143 Bondy, France.
J R Soc Interface. 2007 Aug 22;4(15):625-36. doi: 10.1098/rsif.2007.0212.
In the current context of global infectious disease risks, a better understanding of the dynamics of major epidemics is urgently needed. Time-series analysis has appeared as an interesting approach to explore the dynamics of numerous diseases. Classical time-series methods can only be used for stationary time-series (in which the statistical properties do not vary with time). However, epidemiological time-series are typically noisy, complex and strongly non-stationary. Given this specific nature, wavelet analysis appears particularly attractive because it is well suited to the analysis of non-stationary signals. Here, we review the basic properties of the wavelet approach as an appropriate and elegant method for time-series analysis in epidemiological studies. The wavelet decomposition offers several advantages that are discussed in this paper based on epidemiological examples. In particular, the wavelet approach permits analysis of transient relationships between two signals and is especially suitable for gradual change in force by exogenous variables.
在当前全球传染病风险的背景下，迫切需要更好地了解重大流行病的动态。时间序列分析已成为探索多种疾病动态的一种有趣方法。经典的时间序列方法仅适用于平稳时间序列（即统计特性不随时间变化的序列）。然而，流行病学时间序列通常具有噪声、复杂且强烈非平稳的特点。鉴于这种特殊性质，小波分析显得特别有吸引力，因为它非常适合分析非平稳信号。在此，我们回顾小波方法的基本特性，它是流行病学研究中进行时间序列分析的一种合适且精妙的方法。小波分解具有若干优势，本文将基于流行病学实例进行讨论。特别是，小波方法允许分析两个信号之间的瞬态关系，尤其适用于外生变量导致的作用力逐渐变化。
