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

arXiv · 2108.02115

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

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Abstract

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.

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Marie Courbariaux, Nicolas Cluzel, Siyun Wang, Vincent Maréchal, Laurent Moulin, Sébastien Wurtzer, Obépine consortium, Jean-Marie Mouchel, Yvon Maday, Grégory Nuel. 2021-08-04. A flexible smoother adapted to censored data with outliers and its application to SARS-CoV-2 monitoring in wastewater. https://arxiv.org/abs/2108.02115

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