Data filtering methods for SARS-CoV-2 wastewater surveillance

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 wastewater caused by several influencing fa...

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Autores principales:	Rezgar Arabzadeh, Daniel Martin Grünbacher, Heribert Insam, Norbert Kreuzinger, Rudolf Markt, Wolfgang Rauch
Formato:	article
Lenguaje:	EN
Publicado:	IWA Publishing 2021
Materias:	data smoothing pandemic management sars-cov-2 signal filtering virus monitoring wastewater-based epidemiology Environmental technology. Sanitary engineering TD1-1066
Acceso en línea:	https://doaj.org/article/d17f503e85284643afde66b4e3c08ce5
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id	oai:doaj.org-article:d17f503e85284643afde66b4e3c08ce5
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spelling	oai:doaj.org-article:d17f503e85284643afde66b4e3c08ce52021-11-06T11:22:06ZData filtering methods for SARS-CoV-2 wastewater surveillance0273-12231996-973210.2166/wst.2021.343https://doaj.org/article/d17f503e85284643afde66b4e3c08ce52021-09-01T00:00:00Zhttp://wst.iwaponline.com/content/84/6/1324https://doaj.org/toc/0273-1223https://doaj.org/toc/1996-9732In 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 wastewater caused by several influencing factors that cannot be determined in detail (e.g. dilutions; number of people discharging; variations in virus excretion; water consumption per day; transport and fate processes in sewer system), 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's 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 positive effect of filtering to the correlation of the viral signal to monitored incidence values. HIGHLIGHTS The random component in the timeline of SARS-CoV-2 virus concentration makes data filtering necessary.; Thirteen common filtering techniques are investigated for their potential to smooth the virus signals.; SPLINE, GAM and Friedman's Super Smoother are seen as superior algorithms for smoothing SARS-CoV-2 signals.;Rezgar ArabzadehDaniel Martin GrünbacherHeribert InsamNorbert KreuzingerRudolf MarktWolfgang RauchIWA Publishingarticledata smoothingpandemic managementsars-cov-2signal filteringvirus monitoringwastewater-based epidemiologyEnvironmental technology. Sanitary engineeringTD1-1066ENWater Science and Technology, Vol 84, Iss 6, Pp 1324-1339 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	data smoothing pandemic management sars-cov-2 signal filtering virus monitoring wastewater-based epidemiology Environmental technology. Sanitary engineering TD1-1066
spellingShingle	data smoothing pandemic management sars-cov-2 signal filtering virus monitoring wastewater-based epidemiology Environmental technology. Sanitary engineering TD1-1066 Rezgar Arabzadeh Daniel Martin Grünbacher Heribert Insam Norbert Kreuzinger Rudolf Markt Wolfgang Rauch Data filtering methods for SARS-CoV-2 wastewater surveillance
description	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 wastewater caused by several influencing factors that cannot be determined in detail (e.g. dilutions; number of people discharging; variations in virus excretion; water consumption per day; transport and fate processes in sewer system), 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's 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 positive effect of filtering to the correlation of the viral signal to monitored incidence values. HIGHLIGHTS The random component in the timeline of SARS-CoV-2 virus concentration makes data filtering necessary.; Thirteen common filtering techniques are investigated for their potential to smooth the virus signals.; SPLINE, GAM and Friedman's Super Smoother are seen as superior algorithms for smoothing SARS-CoV-2 signals.;
format	article
author	Rezgar Arabzadeh Daniel Martin Grünbacher Heribert Insam Norbert Kreuzinger Rudolf Markt Wolfgang Rauch
author_facet	Rezgar Arabzadeh Daniel Martin Grünbacher Heribert Insam Norbert Kreuzinger Rudolf Markt Wolfgang Rauch
author_sort	Rezgar Arabzadeh
title	Data filtering methods for SARS-CoV-2 wastewater surveillance
title_short	Data filtering methods for SARS-CoV-2 wastewater surveillance
title_full	Data filtering methods for SARS-CoV-2 wastewater surveillance
title_fullStr	Data filtering methods for SARS-CoV-2 wastewater surveillance
title_full_unstemmed	Data filtering methods for SARS-CoV-2 wastewater surveillance
title_sort	data filtering methods for sars-cov-2 wastewater surveillance
publisher	IWA Publishing
publishDate	2021
url	https://doaj.org/article/d17f503e85284643afde66b4e3c08ce5
work_keys_str_mv	AT rezgararabzadeh datafilteringmethodsforsarscov2wastewatersurveillance AT danielmartingrunbacher datafilteringmethodsforsarscov2wastewatersurveillance AT heribertinsam datafilteringmethodsforsarscov2wastewatersurveillance AT norbertkreuzinger datafilteringmethodsforsarscov2wastewatersurveillance AT rudolfmarkt datafilteringmethodsforsarscov2wastewatersurveillance AT wolfgangrauch datafilteringmethodsforsarscov2wastewatersurveillance
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Data filtering methods for SARS-CoV-2 wastewater surveillance

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