The quantification of the SARS-CoV-2 RNA load in wastewater has emerged as a useful tool to monitor COVID–19 outbreaks in the community. This approach was implemented in the metropolitan area of A Coruña (NW Spain), where wastewater from a treatment plant was analyzed to track the epidemic dynamics in a population of 369,098 inhabitants. Viral load detected in the wastewater and the epidemiological data from A Coruña health system served as main sources for statistical models developing. Regression models described here allowed us to estimate the number of infected people ( R 2 = 0.9), including symptomatic and asymptomatic individuals. These models have helped to understand the real magnitude of the epidemic in a population at any given time and have been used as an effective early warning tool for predicting outbreaks in A Coruña municipality. The methodology of the present work could be used to develop a similar wastewater-based epidemiological model to track the evolution of the COVID–19 epidemic anywhere in the world where centralized water-based sanitation systems exist.

The authors present a new convolution-type kernel estimator of the marginal density of an MA( 1) process with general error distribution. They prove the Jii-consistency of the nonparametric estimator and give asymptotic expressions for the mean square and the integrated mean square error of some unobservable version of the estimator. An extension to MA(q) processes is presented in the case of the mean integrated square error. Finally, a simulation study shows the good practical behaviour of the estimator and the strong connection between the estimator and its unobservable version in terms of the choice of the bandwidth. RESUMELes auteurs montrent comment estimer par la methode du noyau la densite marginale d'un processus de moyenne mobile MA( 1) dont la loi des erreurs est quelconque. Us ddmontrent la convergence d'ordre f i de cet estimateur non parametrique de type convolution et donnent, pour une version non-observable dudit estimateur. des expressions asymptotiques pour les erreurs quadratiques moyennes classique et integde. Dans ce demier cas, ils indiquent en outre comment leur dsultat limite s'6tend au modkle MA(q). Une etude de simulation vient confirmer le bon compottement du nouvel estimateur, qui s'avkre fortement lib h sa version non-observable en ce qui touche le choix de la fenetre.

The quantification of the SARS-CoV-2 load in wastewater has emerged as a useful method to monitor COVID-19 outbreaks in the community. This approach was implemented in the metropolitan area of A Coruna (NW Spain), where wastewater from the treatment plant of Bens was analyzed to track the dynamics of the epidemic in a population of 369,098 inhabitants. We developed statistical regression models that allowed us to estimate the number of infected people from the viral load detected in the wastewater with a reliability close to 90%. This is the first wastewater-based epidemiological model that could potentially be adapted to track the evolution of the COVID-19 epidemic anywhere in the world, monitoring both symptomatic and asymptomatic individuals. It can help to understand with a high degree of reliability the true magnitude of the epidemic in a place at any given time and can be used as an effective early warning tool for predicting outbreaks.

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