Researchers around the world have demonstrated correlations between measurements of SARS-CoV-2 RNA in wastewater (WW) and case rates of COVID-19 derived from direct testing of individuals. This has raised concerns that wastewater-based epidemiology (WBE) methods might be used to quantify the spread of this and other diseases, perhaps faster than direct testing, and with less expense and intrusion. We illustrate, using data from Scotland and the USA, the issues regarding the construction of effective predictive models for disease case rates. We discuss the effects of variation in, and the problem of aligning, public health (PH) reporting and WW measurements. We investigate time-varying effects in PH-reported case rates and their relationship to WW measurements. We show the lack of proportionality of WW measurements to case rates with associated spatial heterogeneity. We illustrate how the precision of predictions is affected by the level of aggregation chosen. We determine whether PH or WW measurements are the leading indicators of disease and how they may be used in conjunction to produce predictive models. The prospects of using WW-based predictive models with or without ongoing PH data are discussed.

世界各地的研究人员已经证明了废水 (WW) 中 SARS-CoV-2 RNA 的测量值与通过对个体进行直接测试得出的 COVID-19 发病率之间的相关性。这引起了人们的担忧，即基于废水的流行病学 (WBE) 方法可能被用于量化这种疾病和其他疾病的传播，可能比直接检测更快，而且费用和干扰更少。我们使用来自苏格兰和美国的数据说明了有关构建疾病发病率的有效预测模型的问题。我们讨论了公共卫生 (PH) 报告和 WW 测量值变化的影响以及调整问题。我们研究了 PH 报告病例率的时变效应及其与 WW 测量值的关系。我们表明 WW 测量与相关空间异质性的病例率缺乏比例。我们说明了预测的精度如何受所选聚合级别的影响。我们确定 PH 或 WW 测量值是否是疾病的主要指标，以及如何将它们结合使用以生成预测模型。讨论了在有或没有正在进行的 PH 数据的情况下使用基于 WW 的预测模型的前景。