Data filtering methods for SARS-CoV-2 wastewater surveillance

TL;DR

This study evaluates 13 data filtering algorithms to improve SARS-CoV-2 wastewater signal analysis, identifying the most effective methods for reducing noise and enhancing infection trend detection in wastewater-based epidemiology.

Contribution

It compares multiple smoothing algorithms and identifies SPLINE, GAM, and Friedman Super Smoother as the most effective for SARS-CoV-2 wastewater data filtering.

Findings

SPLINE, GAM, and Friedman Super Smoother outperform other algorithms.

Smaller catchment areas show higher sensitivity and a threshold effect.

Data filtering improves the reliability of infection trend analysis.

Abstract

In the case of SARS-CoV-2 pandemic management, wastewater-based epidemiology aims to derive information on the infection dynamics by monitoring virus concentrations in the wastewater. However, due to the intrinsic random fluctuations of the viral signal in the wastewater (due to e.g., dilution; transport and fate processes in sewer system; variation in the number of persons discharging; variations in virus excretion and water consumption per day) the subsequent prevalence analysis may result in misleading conclusions. It is thus helpful to apply data filtering techniques to reduce the noise in the signal. In this paper we investigate 13 smoothing algorithms applied to the virus signals monitored in four wastewater treatment plants in Austria. The parameters of the algorithms have been defined by an optimization procedure aiming for performance metrics. The results are further investigated by means of a cluster analysis. While all algorithms are in principle applicable, SPLINE, Generalized Additive Model and Friedman Super Smoother are recognized as superior methods in this context (with the latter two having a tendency to over-smoothing). A first analysis of the resulting datasets indicates the influence of catchment size for wastewater-based epidemiology as smaller communities both reveal a signal threshold before any relation with infection dynamics is visible and also a higher sensitivity towards infection clusters.