Wastewater based epidemiology is recognized as one of the monitoring pillars, providing essential information for pandemic management. Central in the methodology are data modelling concepts for both communicating the monitoring results but also for analysis of the signal. It is due to the fast development of the field that a range of modelling concepts are used but without a coherent framework. This paper provides for such a framework, focusing on robust and simple concepts readily applicable, rather than applying latest findings from e.g., machine learning. It is demonstrated that data preprocessing, most important normalization by means of biomarkers and equal temporal spacing of the scattered data, is crucial. In terms of the latter, downsampling to a weekly spaced series is sufficient. Also, data smoothing turned out to be essential, not only for communication of the signal dynamics but likewise for regressions, nowcasting and forecasting. Correlation of the signal with epidemic indicators require multivariate regression as the signal alone cannot explain the dynamics but simple linear regression proofed to be a suitable tool for compensation. It was also demonstrated that short term prediction (7 days) is accurate with simple models (exponential smoothing or autoregressive models) but forecast accuracy deteriorates fast for longer periods.
翻译:基于废水的流行病学被认为是监测支柱之一,为大流行病管理提供了必要的信息。方法的核心是数据建模概念,既用于通报监测结果,又用于分析信号。这是因为快速开发了实地,使用了一系列建模概念,但没有连贯的框架。本文规定了这样一个框架,侧重于可靠和简单的、易于应用的概念,而不是应用诸如机器学习等最新调查结果。事实证明,数据预处理,最重要的是通过生物标记和分散数据平均时间间隔实现正常化,是至关重要的。从后者来看,下至每周空间序列就足够了。此外,数据平滑变得至关重要,不仅用于信号动态的通信,而且同样用于现在的预测和预测。信号与流行病指标的关联需要多变式回归,因为光是信号无法解释动态,而简单的线性回归证明是合适的补偿工具。还表明,短期预测(7天)与简单模型(光滑或自闭模型)是准确的准确性,但预测的准确性要快速恶化。