title: Time-dependent spectral analysis of epidemiological time-series with wavelets.
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.
authors.affiliation: CNRS UMR 7625, Ecole Normale Supérieure, 46 rue d'Ulm, 75230 Paris, France IRD UR GEODES, 93143 Bondy, France. cazelles@biologie.ens.fr
authors.forename: Bernard
authors.initials: B
authors.lastname: Cazelles
authors.forename: Mario
authors.initials: M
authors.lastname: Chavez
authors.forename: Guillaume Constantin de
authors.initials: GC
authors.lastname: Magny
authors.forename: Jean-Francois
authors.initials: JF
authors.lastname: Guégan
authors.forename: Simon
authors.initials: S
authors.lastname: Hales
mesh_terms: D004777:Environment; D016021:Epidemiologic Studies; D015233:Models, Statistical