A sentinel network, Ob\'epine, 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 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 virus arriving to 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 discretised smoother which makes it a very flexible tool. This method is both validated on simulations and on real data from Ob\'epine. The resulting smoothed signal shows a good correlation with other epidemiological indicators and is currently used by Ob\'epine to provide an estimate of virus circulation over the watersheds corresponding to about 200 WWTPs.
A flexible smoother adapted to censored data with outliers and its application to SARS-CoV-2 monitoring in wastewater