A Flexible Smoother Adapted to Censored Data With Outliers and Its Application to SARS-CoV-2 Monitoring in Wastewater - Laboratoire Jacques-Louis Lions Access content directly
Journal Articles Frontiers in Applied Mathematics and Statistics Year : 2022

A Flexible Smoother Adapted to Censored Data With Outliers and Its Application to SARS-CoV-2 Monitoring in Wastewater

Abstract

A sentinel network, Obépine , has been designed to monitor SARS-CoV-2 viral load in wastewaters arriving at wastewater treatment plants (WWTPs) in France as an indirect macro-epidemiological parameter. The sources of uncertainty in such a monitoring system are numerous, and the concentration measurements it provides are left-censored and contain outliers, which biases the results of usual smoothing methods. Hence, the need for an adapted pre-processing in order to evaluate the real daily amount of viruses arriving at each WWTP. We propose a method based on an auto-regressive model adapted to censored data with outliers. Inference and prediction are produced via a discretized smoother which makes it a very flexible tool. This method is both validated on simulations and real data from Obépine . The resulting smoothed signal shows a good correlation with other epidemiological indicators and is currently used by Obépine to provide an estimate of virus circulation over the watersheds corresponding to about 200 WWTPs.
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Dates and versions

hal-03939048 , version 1 (18-01-2023)

Licence

Attribution - CC BY 4.0

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Marie Courbariaux, Nicolas Cluzel, Siyun Wang, Vincent Maréchal, Laurent Moulin, et al.. A Flexible Smoother Adapted to Censored Data With Outliers and Its Application to SARS-CoV-2 Monitoring in Wastewater. Frontiers in Applied Mathematics and Statistics, 2022, 8, ⟨10.3389/fams.2022.836349⟩. ⟨hal-03939048⟩
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