A Flexible Smoother Adapted to Censored Data With Outliers and Its Application to SARS-CoV-2 Monitoring in Wastewater - Courbariaux, Marie; Cluzel, Nicolas; Wang, Siyun; Maréchal, Vincent; Moulin, Laurent; Wurtzer, Sébastien; , Obépine Consortium; Mouchel, Jean-Marie; Maday, Yvon; Nuel, Grégory; Bertrand, Isabelle; Boni, Mickaēl; et al. (Frontiers in Applied Mathematics and Statistics, 2022)
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 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 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.