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A sentinel network, Ob\'epine, has been designed to monitor SARS-CoV-2 viralload in wastewaters arriving at wastewater treatment plants (WWTPs) in Franceas an indirect macro-epidemiological parameter. The sources of uncertainty insuch monitoring system are numerous and the concentration measurements itprovides are left-censored and contain outliers, which biases the results ofusual smoothing methods. Hence the need for an adapted pre-processing in orderto evaluate the real daily amount of virus arriving to each WWTP. We propose amethod based on an auto-regressive model adapted to censored data withoutliers. Inference and prediction are produced via a discretised smootherwhich makes it a very flexible tool. This method is both validated onsimulations and on real data from Ob\'epine. The resulting smoothed signalshows a good correlation with other epidemiological indicators and is currentlyused by Ob\'epine to provide an estimate of virus circulation over thewatersheds corresponding to about 200 WWTPs.

A flexible smoother adapted to censored data with outliers and its application to SARS-CoV-2 monitoring in wastewater