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This work is motivated by the Obepine French system for SARS-CoV-2 viral load monitoring in wastewater. The objective of this work is to identify, from time-series of noisy measurements, the underlying auto-regressive signals, in a context where the measurements present numerous missing data, censoring and outliers. 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. This method is both validated on simulations and on real data from Obepine. The proposed method is used to denoise measurements from the quantification of the SARS-CoV-2 E gene in wastewater by RT-qPCR. The resulting smoothed signal shows a good correlation with other epidemiological indicators and an estimate of the whole system noise is produced.
The determination of the infection fatality rate (IFR) for the novel SARS-CoV-2 coronavirus is a key aim for many of the field studies that are currently being undertaken in response to the pandemic. The IFR together with the basic reproduction numbe
Near real-time monitoring of outbreak transmission dynamics and evaluation of public health interventions are critical for interrupting the spread of the novel coronavirus (SARS-CoV-2) and mitigating morbidity and mortality caused by coronavirus dise
In the case of SARS-CoV-2 pandemic management, wastewater-based epidemiology aims to derive information on the infection dynamics by monitoring virus concentrations in the wastewater. However, due to the intrinsic random fluctuations of the viral sig
The COVID-19 outbreak is asynchronous in US counties. Mitigating the COVID-19 transmission requires not only the state and federal level order of protective measures such as social distancing and testing, but also public awareness of time-dependent r
We develop a new methodology for spatial regression of aggregated outputs on multi-resolution covariates. Such problems often occur with spatial data, for example in crop yield prediction, where the output is spatially-aggregated over an area and the