FN Archimer Export Format PT J TI A nationwide indicator to smooth and normalize heterogeneous SARS-CoV2 RNA data in wastewater BT AF Cluzel, Nicolas Courbariaux, Marie Wang, Siyun Moulin, Laurent Wurtzer, Sébastien Bertrand, Isabelle Laurent, Karine Monfort, Patrick Gantzer, Christophe LE GUYADER, Soizick Boni, Mickaël Mouchel, Jean-Marie Maréchal, Vincent Nuel, Grégory Maday, Yvon AS 1:1;2:1;3:1;4:2;5:2;6:3;7:1;8:4;9:3;10:5;11:6;12:7;13:8;14:9;15:10;16:; FF 1:;2:;3:;4:;5:;6:;7:;8:;9:;10:PDG-RBE-SGMM-LSEM;11:;12:;13:;14:;15:;16:; C1 Sorbonne Université, Maison des Modélisations Ingénieries et Technologies (SUMMIT), 75005 Paris, France Eau de Paris, Département de Recherche, Développement et Qualité de l’Eau, 33 avenue Jean Jaurès, F-94200 Ivry sur Seine, France Université de Lorraine, CNRS, LCPME, F-54000, Nancy, France HydroSciences Montpellier, UMR 5151, Université de Montpellier, CNRS, IRD, F-34093 Montpellier, France Ifremer, laboratoire de Microbiologie, SG2M/LSEM, BP 21105, 44311 Nantes, France Institut de Recherche Biomédicale des Armées, 1 place Valérie André, F-91220 Brétigny-sur-Orge, France Sorbonne Université, CNRS, EPHE, UMR 7619 Metis, e-LTER Zone Atelier Seine, F-75005 Paris, France Sorbonne Université, INSERM, Centre de Recherche Saint-Antoine, F-75012, Paris, France Stochastics and Biology Group, Probability and Statistics (LPSM, CNRS 8001), Sorbonne University, Campus Pierre et Marie Curie, 4 Place Jussieu, 75005, Paris, France Sorbonne Université, CNRS, Université de Paris, Laboratoire Jacques-Louis Lions (LJLL), F-75005 Paris, France C2 UNIV SORBONNE, FRANCE EAU DE PARIS, FRANCE UNIV LORRAINE, FRANCE UNIV MONTPELLIER, FRANCE IFREMER, FRANCE IRBA, FRANCE UNIV SORBONNE, FRANCE INSERM, FRANCE UNIV SORBONNE, FRANCE CNRS, FRANCE SI NANTES SE PDG-RBE-SGMM-LSEM IN WOS Ifremer UPR DOAJ copubli-france copubli-univ-france IF 11.8 TC 23 UR https://archimer.ifremer.fr/doc/00736/84787/89815.pdf LA English DT Article DE ;Wastewater-based epidemiology (WBE);Coronavirus infectious disease 19 (COVID-19);Mathematical modeling;Correlation;Sampling frequency;Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) AB Since many infected people experience no or few symptoms, the SARS-CoV-2 epidemic is frequently monitored through massive virus testing of the population, an approach that may be biased and may be difficult to sustain in low-income countries. Since SARS-CoV-2 RNA can be detected in stool samples, quantifying SARS-CoV-2 genome by RT-qPCR in wastewater treatment plants (WWTPs) has been carried out as a complementary tool to monitor virus circulation among human populations. However, measuring SARS-CoV-2 viral load in WWTPs can be affected by many experimental and environmental factors. To circumvent these limits, we propose here a novel indicator, the wastewater indicator (WWI), that partly reduces and corrects the noise associated with the SARS-CoV-2 genome quantification in wastewater (average noise reduction of 19%). All data processing results in an average correlation gain of 18% with the incidence rate. The WWI can take into account the censorship linked to the limit of quantification (LOQ), allows the automatic detection of outliers to be integrated into the smoothing algorithm, estimates the average measurement error committed on the samples and proposes a solution for inter-laboratory normalization in the absence of inter-laboratory assays (ILA). This method has been successfully applied in the context of Obépine, a French national network that has been quantifying SARS-CoV-2 genome in a representative sample of French WWTPs since March 5th 2020. By August 26th, 2021, 168 WWTPs were monitored in the French metropolitan and overseas territories of France. We detail the process of elaboration of this indicator, show that it is strongly correlated to the incidence rate and that the optimal time lag between these two signals is only a few days, making our indicator an efficient complement to the incidence rate. This alternative approach may be especially important to evaluate SARS-CoV-2 dynamics in human populations when the testing rate is low PY 2022 PD JAN SO Environment International SN 0160-4120 PU Elsevier BV VL 158 UT 000726405300009 DI 10.1016/j.envint.2021.106998 ID 84787 ER EF