Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA; Department of Environmental Sciences and Engineering, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA; Occupational & Environmental Epidemiology Branch, Division of Public Health, North Carolina Department of Health and Human Services, Raleigh, NC, USA.
Sci Total Environ. 2024 Nov 15;951:175687. doi: 10.1016/j.scitotenv.2024.175687. Epub 2024 Aug 21.
Wastewater monitoring data can be used to estimate disease trends to inform public health responses. One commonly estimated metric is the rate of change in pathogen quantity, which typically correlates with clinical surveillance in retrospective analyses. However, the accuracy of rate of change estimation approaches has not previously been evaluated.
We assessed the performance of approaches for estimating rates of change in wastewater pathogen loads by generating synthetic wastewater time series data for which rates of change were known. Each approach was also evaluated on real-world data.
Smooth trends and their first derivatives were jointly sampled from Gaussian processes (GP) and independent errors were added to generate synthetic viral load measurements; the range hyperparameter and error variance were varied to produce nine simulation scenarios representing different potential disease patterns. The directions and magnitudes of the rate of change estimates from four estimation approaches (two established and two developed in this work) were compared to the GP first derivative to evaluate classification and quantitative accuracy. Each approach was also implemented for public SARS-CoV-2 wastewater monitoring data collected January 2021-May 2023 at 25 sites in North Carolina, USA.
All four approaches inconsistently identified the correct direction of the trend given by the sign of the GP first derivative. Across all nine simulated disease patterns, between a quarter and a half of all estimates indicated the wrong trend direction, regardless of estimation approach. The proportion of trends classified as plateaus (statistically indistinguishable from zero) for the North Carolina SARS-CoV-2 data varied considerably by estimation method but not by site.
Our results suggest that wastewater measurements alone might not provide sufficient data to reliably track disease trends in real-time. Instead, wastewater viral loads could be combined with additional public health surveillance data to improve predictions of other outcomes.
污水监测数据可用于估计疾病趋势，为公共卫生应对措施提供信息。一个常用的估计指标是病原体数量的变化率，该指标通常与回顾性分析中的临床监测相关。然而，目前尚未评估变化率估计方法的准确性。
我们通过生成已知变化率的合成污水时间序列数据，评估了用于估计污水病原体负荷变化率的方法的性能。每种方法也在实际数据上进行了评估。
我们从高斯过程（GP）中联合采样平滑趋势及其一阶导数，并添加独立误差以生成合成病毒载量测量值；变化范围超参数和误差方差来产生九个模拟场景，代表不同的潜在疾病模式。通过比较四个估计方法（两个已建立的和两个在本工作中开发的）的变化率估计的方向和幅度与 GP 一阶导数，评估分类和定量准确性。每个方法还用于美国北卡罗来纳州 25 个地点 2021 年 1 月至 2023 年 5 月期间收集的公共 SARS-CoV-2 污水监测数据。
所有四种方法都不一致地识别出了 GP 一阶导数符号给出的趋势的正确方向。在所有九个模拟疾病模式中，无论估计方法如何，四分之一到一半的估计都表明了错误的趋势方向。对于北卡罗来纳州 SARS-CoV-2 数据，将趋势归类为平台（统计学上与零无差异）的比例因估计方法而异，但与地点无关。
我们的结果表明，单独的污水测量可能不足以提供可靠实时跟踪疾病趋势的数据。相反，污水病毒载量可以与其他公共卫生监测数据结合使用，以提高对其他结果的预测能力。
