Frontiers in Applied Mathematics and Statistics (Feb 2022)

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

  • Marie Courbariaux,
  • Nicolas Cluzel,
  • Siyun Wang,
  • Vincent Maréchal,
  • Laurent Moulin,
  • Sébastien Wurtzer,
  • Obépine Consortium,
  • Jean-Marie Mouchel,
  • Yvon Maday,
  • Grégory Nuel,
  • Grégory Nuel,
  • Isabelle Bertrand,
  • Mickaēl Boni,
  • Christophe Gantzer,
  • Soizick F. Le Guyader,
  • Yvon Maday,
  • Vincent Maréchal,
  • Jean-Marie Mouchel,
  • Laurent Moulin,
  • Rémy Teyssou,
  • Sébastien Wurtzer

DOI
https://doi.org/10.3389/fams.2022.836349
Journal volume & issue
Vol. 8

Abstract

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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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