ArticleJournal of the Royal Society, Interface2014
Wavelet analysis in ecology and epidemiology: impact of statistical tests.
Article in Journal of the Royal Society, Interface, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 36 papers.
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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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
36 citing papers in PubMed, 103 citations in OpenAlex.
- Wavelet analysis and time series forecasting of malaria incidence across different transmission settings in Nigeria.BMC infectious diseases · 2026Article
- Spatiotemporal instability of influenza seasonality during viral co-circulation.NPJ systems biology and applications · 2026Article
- Generation cycles in experimental populations of a multivoltine insect.The Journal of animal ecology · 2026Article
- Macroecological synchronization of vector-borne diseases under climate variability in Colombia.Science in One Health · 2026Article
- Time series modelling and forecasting of mpox incidence and mortality in Nigeria.BMC infectious diseases · 2025Article
- Meteorological factors and normalized difference vegetation index drivers of scrub typhus incidence in Shandong Province based on a 16-year time-frequency analysis.BMC public health · 2025Article
- Strain variation and anomalous climate synergistically influence cholera pandemics.PLoS neglected tropical diseases · 2024Article
- Changing epidemiology of parvovirus B19 in the Netherlands since 1990, including its re-emergence after the COVID-19 pandemic.Scientific reports · 2024Article
- Disentangling local and global climate drivers in the population dynamics of mosquito-borne infections.Science advances · 2023Article
- Wavelet clustering analysis as a tool for characterizing community structure in the human microbiome.Scientific reports · 2023Article
- Common multi-day rhythms in smartphone behavior.NPJ digital medicine · 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
- Exploring links between climatic predictability and the evolution of within- and transgenerational plasticity.Ecology and evolution · 2022Article
- Multi-scale causality analysis between COVID-19 cases and mobility level using ensemble empirical mode decomposition and causal decomposition.Physica A · 2022Article
- Looking for compensation at multiple scales in a wetland bird community.Ecology and evolution · 2022Article
- Malaria elimination on Hainan Island despite climate change.Communications medicine · 2022Article
- True epidemic growth construction through harmonic analysis.Journal of theoretical biology · 2020Article
- Scaling-up biodiversity-ecosystem functioning research.Ecology letters · 2020Review
- Long-term persistence of monotypic dengue transmission in small size isolated populations, French Polynesia, 1978-2014.PLoS neglected tropical diseases · 2020Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Wavelet analysis is now frequently used to extract information from ecological and epidemiological time series. Statistical hypothesis tests are conducted on associated wavelet quantities to assess the likelihood that they are due to a random process. Such random processes represent null models and are generally based on synthetic data that share some statistical characteristics with the original time series. This allows the comparison of null statistics with those obtained from original time series. When creating synthetic datasets, different techniques of resampling result in different characteristics shared by the synthetic time series. Therefore, it becomes crucial to consider the impact of the resampling method on the results. We have addressed this point by comparing seven different statistical testing methods applied with different real and simulated data. Our results show that statistical assessment of periodic patterns is strongly affected by the choice of the resampling method, so two different resampling techniques could lead to two different conclusions about the same time series. Moreover, our results clearly show the inadequacy of resampling series generated by white noise and red noise that are nevertheless the methods currently used in the wide majority of wavelets applications. Our results highlight that the characteristics of a time series, namely its Fourier spectrum and autocorrelation, are important to consider when choosing the resampling technique. Results suggest that data-driven resampling methods should be used such as the hidden Markov model algorithm and the 'beta-surrogate' method.
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Registered trials
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.