ReviewJournal of the Royal Society, Interface2007
Time-dependent spectral analysis of epidemiological time-series with wavelets.
Review in Journal of the Royal Society, Interface, 2007. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 120 papers, 1 of them a synthesis that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
120 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Challenges in developing methods for quantifying the effects of weather and climate on water-associated diseases: A systematic review.PLoS neglected tropical diseases · 2017Pooled it
- Wavelet analysis and time series forecasting of malaria incidence across different transmission settings in Nigeria.BMC infectious diseases · 2026Article
- Non-linear effects and lag patterns of eco-environmental factors on scrub typhus: a spatiotemporal analysis in Yunnan, China, 2015-2022.Parasites & vectors · 2026Article
- Seasonal forcing and waning immunity drive the sub-annual periodicity of the COVID-19 epidemic.PLoS pathogens · 2026Article
- The emergence of hyperendemic dengue in Bangladesh: An ecological study of structural breaks and transmission regime shifts, 2008-2025.PloS one · 2026Article
- Article
- Time series modelling and forecasting of mpox incidence and mortality in Nigeria.BMC infectious diseases · 2025Article
- Distinct Mechanisms of Multiple Alpha-Band Activities in Frontal Regions Following an 8-Week Medium- (Yoga) and High-Intensity (Pamela) Exercise Intervention.CNS neuroscience & therapeutics · 2025Article
- Changes in seasonality and sex ratio of scrub typhus: a case study of South Korea from 2003 to 2019 based on wavelet transform analysis.BMC infectious diseases · 2024Article
- A thirty-year time series analyses identifies coherence between oscillations in Anthrax outbreaks and El Niño in Karnataka, India.Scientific reports · 2024Article
- Strain variation and anomalous climate synergistically influence cholera pandemics.PLoS neglected tropical diseases · 2024Article
- Spatial and temporal analysis of haemorrhagic septicaemia outbreaks in India over three decades (1987-2016).Scientific reports · 2024Article
- Disentangling local and global climate drivers in the population dynamics of mosquito-borne infections.Science advances · 2023Article
- The Spatiotemporal Pattern and Its Determinants of Hemorrhagic Fever With Renal Syndrome in Northeastern China: Spatiotemporal Analysis.JMIR public health and surveillance · 2023Article
- Wavelet clustering analysis as a tool for characterizing community structure in the human microbiome.Scientific reports · 2023Article
- The malaria transmission in Anhui province China.Infectious Disease Modelling · 2023Article
- Common patterns between dengue cases, climate, and local environmental variables in Costa Rica: A wavelet approach.PLOS global public health · 2023Article
- Article
- Co-effects of global climatic dynamics and local climatic factors on scrub typhus in mainland China based on a nine-year time-frequency analysis.One health (Amsterdam, Netherlands) · 2022Article
- The Long-Term Success of Mandatory Vaccination Laws After Implementing the First Vaccination Campaign in 19th Century Rural Finland.American journal of epidemiology · 2022Article
60 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
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.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.