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

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

The study is within the frame of the virWASTE project, which has received funding from the Agència de Gestió d’Ajuts Universitaris i de Recerca (AGAUR) under the call ‘Pandèmies 2020’ (Ref. 2020 PANDE 00044). ICRA authors acknowledge the Economy and Knowledge Department of the Catalan Government through Consolidated Research Groups 2021-SGR-01283 ICRA-TECH and 2021 SGR 01282 ICRA-ENV, and the funding from the CERCA program (Generalitat de Catalunya). The authors also acknowledge the Catalan Surveillance Network of SARS-CoV-2 in Sewage (SARSAIGUA) for the provision of data