“Assessing Methodological Variability in Wastewater Surveillance: A Wavelet Decomposition Approach”

Wastewater surveillance has become a critical public health tool for the early detection of infectious disease outbreaks and monitoring community-level trends. However, variability in sample collection and processing introduces methodological noise that can obscure true epidemiological signals and limit comparability across different sites.

To address this challenge, we apply the discrete wavelet transform (DWT) to separate underlying disease dynamics from methodological variability in SARS-CoV-2 wastewater data. Our analysis uses longitudinal RNA measurements from five California cities, each with paired influent and solids samples. The DWT decomposes signals into low-frequency (trend) and high-frequency (noise) components, allowing us to reconstruct denoised signals and assess similarity using hierarchical clustering.

Clustering of raw data fails to recover city-specific patterns, whereas reconstructions based on low-frequency components reveal clear alignment between sample types within each city. These results suggest that high-frequency variation is largely driven by methodological noise, while low-frequency components capture shared epidemiological trends.

Our findings underscore the importance of denoising in wastewater data analysis and offer a scalable framework for improving comparability across sampling methods and locations.

Transmisión en vivo vía bit.ly/YouTube_ICF

Participante: Dra. María Luisa Daza Torres

Institución: Centro de Investigación en Matemáticas (CIMAT)

Fecha y hora: Este evento terminó el Miércoles, 06 de Mayo de 2026