Name
Title | ||
|---|---|---|
A Flexible Smoother Adapted to Censored Data With Outliers and Its Application to SARS-CoV-2 Monitoring in Wastewater |
Abstract | ||
|---|---|---|
A sentinel network, Obepine, 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 a 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 viruses arriving at 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 discretized smoother which makes it a very flexible tool. This method is both validated on simulations and real data from Obepine. The resulting smoothed signal shows a good correlation with other epidemiological indicators and is currently used by Obepine to provide an estimate of virus circulation over the watersheds corresponding to about 200 WWTPs. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.3389/fams.2022.836349 | FRONTIERS IN APPLIED MATHEMATICS AND STATISTICS |
Keywords | DocType | Volume |
measurement error, smoothing algorithm, outliers, censored data, SARS-CoV-2, autoregressive model | Journal | 8 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
9 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Marie Courbariaux | 1 | 0 | 0.34 |
| Nicolas Cluzel | 2 | 0 | 0.34 |
| Siyun Wang | 3 | 0 | 0.34 |
| Vincent Maréchal | 4 | 0 | 0.34 |
| Laurent Moulin | 5 | 0 | 0.34 |
| Sébastien Wurtzer | 6 | 0 | 0.34 |
| Jean-Marie Mouchel | 7 | 0 | 0.34 |
| Yvon Maday | 8 | 0 | 0.34 |
| Grégory Nuel | 9 | 0 | 0.34 |