Watching the guards: A data-driven method to trigger warnings in national wastewater surveillance networks

Author:

Bosch Lluís M.12,Pueyo-Ros Josep12ORCID,Comas-Cufí Marc3ORCID,Saldaña Joan3ORCID,Ripoll Jordi3ORCID,Calle Eusebi4ORCID,Fonseca i Casas Pau5,Garcia i Subirana Joan5,Borrego Carles M.16ORCID,Corominas Lluís12ORCID

Affiliation:

1. a Catalan Institute for Water Research (ICRA-CERCA), Emili Grahit 101, Girona 17003, Catalonia, Spain

2. b University of Girona, Plaça Sant Domènec 3, Girona 17004, Catalonia, Spain

3. c Department of Computer Science, Applied Mathematics, and Statistics, University of Girona, Girona 17003, Catalonia, Spain

4. d Institute of Informatics and Applications, University of Girona, Maria Aurèlia Capmany 61 (Building PIV), Girona 17003, Catalonia, Spain

5. e Polytechnic University of Catalonia – Barcelona Tech., Jordi Girona 31, Barcelona 08034, Catalonia, Spain

6. f Group of Molecular Microbial Ecology, Institute of Aquatic Ecology, University of Girona, Girona 17003, Catalonia, Spain

Abstract

ABSTRACT Surveillance networks have been established in many countries worldwide to monitor SARS-CoV-2 in sewage and to estimate the communal prevalence of COVID-19 cases. Despite their popularity, gaining a rapid understanding of how infectious diseases spread across the territory covered by a network is difficult because of the many factors involved. To improve the detection of warning signals within the territory, we propose to apply principal component analysis (PCA) to screen time-series data generated from wastewater treatment plants (WWTPs) under surveillance. Our analysis allows us to identify single WWTPs deviating from the normal behavior as well as deviations of a cluster of WWTPs (indicative of an intermunicipal outbreak). Our approach is illustrated through the analysis of the dataset generated by the Catalan Surveillance Network of SARS-CoV-2 in Sewage (SARSAIGUA). Using 10 principal components, we captured 78.6% of the variance in the original dataset of 51 variables (WWTPs). Our analysis identified exceedance of the Q-statistic threshold as evidence of anomalous performance of a single WWTP, and exceedance of the T2-statistic as a sign of an intermunicipal outbreak. Our approach provides a comprehensive picture of the spread of the COVID-19 pandemic, enabling decision-makers to make informed decisions and better manage future pandemics.

Funder

Agència de Gestió d'Ajuts Universitaris i de Recerca

Publisher

IWA Publishing

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