Highly predictive regression model of active cases of COVID-19 in a population by screening wastewater viral load

Abstract 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 Coruña (NW Spain), where wastewater from the treatment plant of Bens was analyzed to track the epidemic’s dynamic 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.