Department of Mathematical Sciences, University of Bath, Bath BA2 7AY, UK E-mail:
Department of Chemistry, University of Bath, Bath BA2 7AY, UK.
J Water Health. 2022 Jul;20(7):1038-1050. doi: 10.2166/wh.2022.020.
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，废水中 SARS-CoV-2 RNA 的测量值与通过直接个体检测得出的 COVID-19 病例率之间存在相关性。这引发了人们的担忧，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，其速度可能比直接检测更快，而且费用和干扰更小。我们使用来自苏格兰和美国的数据说明了构建疾病病例率有效预测模型的问题。我们讨论了公共卫生（PH）报告和 WW 测量之间的差异以及对齐问题。我们研究了 PH 报告的病例率中的时变效应及其与 WW 测量的关系。我们展示了 WW 测量与病例率之间缺乏比例关系以及相关的空间异质性。我们说明了预测的精度如何受到所选择的聚合级别影响。我们确定 PH 或 WW 测量值是疾病的领先指标，以及它们如何结合使用以生成预测模型。还讨论了有无持续 PH 数据的情况下使用基于 WW 的预测模型的前景。
