A nationwide indicator to smooth and normalize heterogeneous SARS-CoV-2 RNA data in wastewater
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, qu...
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2022
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oai:doaj.org-article:1f916af45125462bbbbacba611333e112021-11-28T04:27:34ZA nationwide indicator to smooth and normalize heterogeneous SARS-CoV-2 RNA data in wastewater0160-412010.1016/j.envint.2021.106998https://doaj.org/article/1f916af45125462bbbbacba611333e112022-01-01T00:00:00Zhttp://www.sciencedirect.com/science/article/pii/S0160412021006231https://doaj.org/toc/0160-4120Since 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.Nicolas CluzelMarie CourbariauxSiyun WangLaurent MoulinSébastien WurtzerIsabelle BertrandKarine LaurentPatrick MonfortChristophe GantzerSoizick Le GuyaderMickaël BoniJean-Marie MouchelVincent MaréchalGrégory NuelYvon MadayElsevierarticleWastewater-based epidemiology (WBE)Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)Coronavirus infectious disease 19 (COVID-19)Mathematical modelingCorrelationSampling frequencyEnvironmental sciencesGE1-350ENEnvironment International, Vol 158, Iss , Pp 106998- (2022) |
| institution |
DOAJ |
| collection |
DOAJ |
| language |
EN |
| topic |
Wastewater-based epidemiology (WBE) Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) Coronavirus infectious disease 19 (COVID-19) Mathematical modeling Correlation Sampling frequency Environmental sciences GE1-350 |
| spellingShingle |
Wastewater-based epidemiology (WBE) Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) Coronavirus infectious disease 19 (COVID-19) Mathematical modeling Correlation Sampling frequency Environmental sciences GE1-350 Nicolas Cluzel Marie Courbariaux Siyun Wang Laurent Moulin Sébastien Wurtzer Isabelle Bertrand Karine Laurent Patrick Monfort Christophe Gantzer Soizick Le Guyader Mickaël Boni Jean-Marie Mouchel Vincent Maréchal Grégory Nuel Yvon Maday A nationwide indicator to smooth and normalize heterogeneous SARS-CoV-2 RNA data in wastewater |
| description |
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. |
| format |
article |
| author |
Nicolas Cluzel Marie Courbariaux Siyun Wang Laurent Moulin Sébastien Wurtzer Isabelle Bertrand Karine Laurent Patrick Monfort Christophe Gantzer Soizick Le Guyader Mickaël Boni Jean-Marie Mouchel Vincent Maréchal Grégory Nuel Yvon Maday |
| author_facet |
Nicolas Cluzel Marie Courbariaux Siyun Wang Laurent Moulin Sébastien Wurtzer Isabelle Bertrand Karine Laurent Patrick Monfort Christophe Gantzer Soizick Le Guyader Mickaël Boni Jean-Marie Mouchel Vincent Maréchal Grégory Nuel Yvon Maday |
| author_sort |
Nicolas Cluzel |
| title |
A nationwide indicator to smooth and normalize heterogeneous SARS-CoV-2 RNA data in wastewater |
| title_short |
A nationwide indicator to smooth and normalize heterogeneous SARS-CoV-2 RNA data in wastewater |
| title_full |
A nationwide indicator to smooth and normalize heterogeneous SARS-CoV-2 RNA data in wastewater |
| title_fullStr |
A nationwide indicator to smooth and normalize heterogeneous SARS-CoV-2 RNA data in wastewater |
| title_full_unstemmed |
A nationwide indicator to smooth and normalize heterogeneous SARS-CoV-2 RNA data in wastewater |
| title_sort |
nationwide indicator to smooth and normalize heterogeneous sars-cov-2 rna data in wastewater |
| publisher |
Elsevier |
| publishDate |
2022 |
| url |
https://doaj.org/article/1f916af45125462bbbbacba611333e11 |
| work_keys_str_mv |
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