Data filtering methods for SARS-CoV-2 wastewater surveillance

Abstract In the case of SARS-CoV-2 pandemic management, wastewater-based epidemiology aims to derive information on infection dynamics by monitoring virus concentrations in wastewater. However, due intrinsic random fluctuations viral signal wastewater caused several influencing factors that cannot be determined detail (e.g. dilutions; number people discharging; variations excretion; water consumption per day; transport and fate processes sewer system), subsequent prevalence analysis may result misleading conclusions. It is thus helpful apply data filtering techniques reduce noise signal. this paper we investigate 13 smoothing algorithms applied signals monitored four treatment plants Austria. The parameters have been defined an optimization procedure aiming for performance metrics. results are further investigated means a cluster analysis. While all principle applicable, SPLINE, Generalized Additive Model Friedman's Super Smoother recognized as superior methods context (with latter two having tendency over-smoothing). A first resulting datasets indicates positive effect correlation incidence values.