  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.
