Time-dependent spectral analysis of epidemiological time-series with wavelets

Author:

Cazelles Bernard12,Chavez Mario3,Magny Guillaume Constantin de4,Guégan Jean-Francois5,Hales Simon6

Affiliation:

1. CNRS UMR 7625, Ecole Normale Supérieure46 rue d'Ulm, 75230 Paris, France

2. IRD UR GEODES93143 Bondy, France

3. LENA-CNRS UPR 640, Hôpital Pitié-Salpêtrière4 boulevard de l'Hôpital, 75651 Paris cedex 13, France

4. Institute for Advanced Computer Studies, University of MarylandCollege Park, MD 20742, USA

5. IRD-CNRS UMR 2724911 avenue Agropolis, BP 64501, 34394 Montpellier cedex 05, France

6. Wellington School of Medicine and Health Sciences, University of OtagoMein Street, Newton, Wellington South, New Zealand

Abstract

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.

Publisher

The Royal Society

Subject

Biomedical Engineering,Biochemistry,Biomaterials,Bioengineering,Biophysics,Biotechnology

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