Author: Vallejo, J. A.; Rumbo-Feal, S.; Conde, K.; Lopez-Oriona, A.; Tarrio, J.; Reif, R.; Ladra, S.; Rodino-Janeiro, B. K.; Nasser, M.; Cid, A.; Veiga, M. C.; Acevedo, A.; Lamora, C.; Bou, G.; Cao, R.; Poza, M.
Title: Highly predictive regression model of active cases of COVID-19 in a population by screening wastewater viral load Cord-id: o7tph2hk Document date: 2020_7_4
ID: o7tph2hk
Snippet: 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 wastewat
Document: 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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