标题： 评估方法学变异性的废水监测：小波分解方法 显示英文标题

标题： Assessing Methodological Variability in Wastewater Surveillance: A Wavelet Decomposition Approach

摘要： 废水监测已成为一种关键的公共卫生工具，能够早期检测传染病爆发，并提供及时的、人群层面的社区健康趋势信息。 然而，样本采集和处理中的差异，例如污水进水口和沉淀物之间的差异，可能会引入方法学噪声，这种噪声对真实的流行病学信号有不同的影响，并限制了不同地点之间的可比性。 为了解决这一挑战，我们旨在使用离散小波变换（DWT）从SARS-CoV-2废水数据的方法学变异中辨别潜在的疾病趋势，重点是比较同一地理区域的进水口和沉淀物样本。 我们将DWT应用于来自五个加利福尼亚州城市的废水中的SARS-CoV-2 RNA浓度纵向数据，每个城市都有配对的进水口和沉淀物样本。 DWT将每个信号分解为两个部分： (1) 近似系数，用于捕捉平滑的长期趋势，以及 (2) 细节系数，用于隔离信号中的高频波动和瞬时变化。 我们通过逐步去除高频成分来重建信号，并使用层次聚类评估样本类型之间的相似性。 原始信号的聚类未能产生城市特定的分组，表明方法学噪声掩盖了潜在的流行病学信号。 保留一些高频成分的中间重建仍然显示出混合的分组。 相反，仅基于低频近似系数的重建显示出了清晰的城市特定聚类，同一城市的进水口和沉淀物样本紧密对齐。 这些发现支持我们的假设，即高频成分主要由样本处理和实验室噪声驱动，而低频成分反映了共享的流行病学趋势。 显示英文摘要

摘要： Wastewater surveillance has emerged as a critical public health tool, enabling early detection of infectious disease outbreaks and providing timely, population-level insights into community health trends. However, variability in sample collection and processing, for example between wastewater influent and settled solids, can introduce methodological noise that differentially impacts true epidemiological signals and limits cross-site comparability. To address this challenge, we aimed to discern underlying disease trends from methodological variability in SARS-CoV-2 wastewater data using discrete wavelet transform (DWT), with a focus on comparing influent and solids samples from the same geographic locations. We applied DWT to longitudinal SARS-CoV-2 RNA concentrations in wastewater from five California cities, each with paired influent and solids samples. DWT decomposes each signal into two components: (1) approximation coefficients that capture smoothed long-term trends, and (2) detail coefficients that isolate high-frequency fluctuations and transient variations in the signal. We reconstructed signals by progressively removing the high-frequency components and assessed similarity between sample types using hierarchical clustering. Clustering of raw signals did not yield city-specific groupings, indicating that methodological noise obscured the underlying epidemiological signal. Intermediate reconstructions that retained some high-frequency components continued to show mixed groupings. In contrast, reconstructions based solely on low-frequency approximation coefficients revealed clear, city-specific clustering, with influent and solids samples from the same city aligning closely. These findings support our hypothesis that high-frequency components are primarily driven by sample processing and laboratory noise, while low-frequency components reflect shared epidemiological trends.