The DOI matches its Crossref record; 58 of the 58 references it cites were found in a registry or an index.
Time-dependent spectral analysis of epidemiological time-series with wavelets
Open accessAbstract
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.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
An Introduction to the Bootstrap
Efron, B., & Tibshirani, R. J. (1994) · doi:10.1201/9780429246593 · Cited by 40,500
An Introduction to the Bootstrap
Efron, B., & Tibshirani, R. J. (1993) · doi:10.1007/978-1-4899-4541-9 · Cited by 21,230
A Wavelet Tour of Signal Processing
(1999) · Elsevier eBooks · doi:10.1016/b978-0-12-466606-1.x5000-4 · Cited by 16,227
Daubechies, I. (1992) · doi:10.1137/1.9781611970104 · Cited by 15,979
Daubechies, I., & Heil, C. (1992) · Computers in Physics · doi:10.1063/1.4823127 · Cited by 15,631
A Practical Guide to Wavelet Analysis
Torrence, C., & Compo, G. P. (1998) · Bulletin of the American Meteorological Society · doi:10.1175/1520-0477(1998)079<0061:apgtwa>2.0.co;2 · Cited by 14,890
Climate Change 2007: Impacts, Adaptation and Vulnerability
Parry, M. L., Canziani, O. F., Palutikof, J. P., van der Linden, P., & Hanson, C. (2007) · Centre for Environmental Data Analysis Digital Repository (Centre for Environmental Data Analysis) · doi:10.5281/zenodo.7356334 · Cited by 9,136
Application of the cross wavelet transform and wavelet coherence to geophysical time series
Grinsted, A., Moore, J. C., & Jevrejeva, S. (2004) · Nonlinear Processes in Geophysics · doi:10.5194/npg-11-561-2004 · Cited by 6,285
Infectious Diseases of Humans: Dynamics and Control
Anderson, R. M., & May, R. M. (1991) · doi:10.1093/oso/9780198545996.001.0001 · Cited by 6,179
Synchronization a universal concept in nonlinear sciences
PIKOVSKY, A. S., Rosenblum, M. G., & Kurths, J. (2001) · Cited by 5,640
Cited by
On the Spatio-Temporal Dynamics of Soil Moisture at the Field Scale
Vereecken, H., Huisman, J. A., Pachepsky, Y., Montzka, C., van der Kruk, J., Bogena, H., Weihermüller, L., Herbst, M., Martinez, G., & Vanderborght, J. (2014) · Journal of Hydrology · doi:10.1016/j.jhydrol.2013.11.061 · Cited by 584
Shifting patterns: malaria dynamics and rainfall variability in an African highland
Pascual, M., Cazelles, B., Bouma, M. J., Chaves, L. F., & Koelle, K. (2008) · Proceedings of the Royal Society B: Biological Sciences · doi:10.1098/rspb.2007.1068 · Cited by 170
Self-citationAsher, L., Collins, L. M., Ortiz-Pelaez, A., Drewe, J. A., Nicol, C. J., & Pfeiffer, D. U. (2009) · Journal of The Royal Society Interface · doi:10.1098/rsif.2009.0221 · Cited by 102
Metadata
- Authors
- Cazelles, B., Chavez, M., Magny, G. C. D., Guégan, J.-F., & Hales, S.
- Venue
- Journal of The Royal Society Interface
- Publisher
- The Royal Society
- Published
- 2007-02-14
- Volume
- 4
- Issue
- 15
- Pages
- 625-636
- ISSN
- 1742-5662 (ISSN-L)
- 1742-5689
- OpenAlex
- W2146143892
- Cited by
- 329
- References
- 58
- FWCI
- 18.20
- Type
- journal article
- Language
- en
- Open access
- bronze
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Time-dependent spectral analysis of epidemiological time-series with wavelets. https://app.cochat.ai/papers/10.1098/rsif.2007.0212
- The paper
- Cazelles, B., Chavez, M., Magny, G. C. D., Guégan, J.-F., & Hales, S. (2007). Time-dependent spectral analysis of epidemiological time-series with wavelets. Journal of The Royal Society Interface. https://doi.org/10.1098/rsif.2007.0212
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.