  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.
