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Related Experiment Video

Updated: May 7, 2026

Watershed Planning within a Quantitative Scenario Analysis Framework

Watershed Planning within a Quantitative Scenario Analysis Framework

Published on: July 24, 2016

Assessing methodological variability in wastewater surveillance: A wavelet decomposition approach.

Maria L Daza-Torres¹, J Cricelio Montesinos-López¹, Rachel Olson²

¹Department of Public Health Sciences, University of California Davis, Davis, CA, USA.

|March 1, 2026

Summary

Wastewater surveillance data can be noisy. Using discrete wavelet transform (DWT) to analyze SARS-CoV-2 trends in wastewater helps distinguish real disease signals from processing variations, improving data reliability.

Keywords:

Discrete wavelet transform (DWT)Hierarchical clustering SARS-CoV-2 Time-series analysis Wastewater-based epidemiology (WBE)

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Quantification and Whole Genome Characterization of SARS-CoV-2 RNA in Wastewater and Air Samples

Quantification and Whole Genome Characterization of SARS-CoV-2 RNA in Wastewater and Air Samples

Published on: June 30, 2023

Area of Science:

Environmental science
Epidemiology
Data science

Background:

Wastewater surveillance is vital for public health, detecting disease outbreaks and trends.
Variability in wastewater sample collection and processing introduces noise, hindering accurate analysis and comparability.
Methodological noise can obscure true epidemiological signals in SARS-CoV-2 wastewater data.

Purpose of the Study:

To differentiate underlying disease trends from methodological variability in SARS-CoV-2 wastewater surveillance data.
To compare wastewater influent and solids samples using the discrete wavelet transform (DWT).
To enhance the comparability of wastewater data across different sites and sample types.

Main Methods:

Applied DWT to longitudinal SARS-CoV-2 RNA concentrations from paired influent and solids wastewater samples in five California cities.
Decomposed signals into approximation (long-term trends) and detail (high-frequency fluctuations) coefficients.
Reconstructed signals by removing high-frequency components and used hierarchical clustering to assess similarity.

Main Results:

Raw wastewater signals did not show clear city-specific groupings due to methodological noise.
Signal reconstructions retaining high-frequency components still exhibited mixed groupings.
Reconstructions based solely on low-frequency approximation coefficients revealed distinct, city-specific clusters, aligning influent and solids samples.

Conclusions:

High-frequency components in wastewater data are likely driven by sample processing and laboratory noise.
Low-frequency components effectively reflect shared epidemiological trends, unaffected by methodological variations.
Denoising wastewater data using methods like DWT is crucial for improving signal comparability and reliability.