Assessing wastewater-based epidemiology for the prediction of SARS-CoV-2 incidence in Catalonia

Otros/as autores/as

Universitat Politècnica de Catalunya. Departament de Física

Universitat Politècnica de Catalunya. BIOCOM-SC - Biologia Computacional i Sistemes Complexos

Fecha de publicación

2022-09-05

Resumen

While wastewater-based epidemiology has proven a useful tool for epidemiological surveillance during the COVID-19 pandemic, few quantitative models comparing virus concentrations in wastewater samples and cumulative incidence have been established. In this work, a simple mathematical model relating virus concentration and cumulative incidence for full contagion waves was developed. The model was then used for short-term forecasting and compared to a local linear model. Both scenarios were tested using a dataset composed of samples from 32 wastewater treatment plants and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) incidence data covering the corresponding geographical areas during a 7-month period, including two contagion waves. A population-averaged dataset was also developed to model and predict the incidence over the full geography. Overall, the mathematical model based on wastewater data showed a good correlation with cumulative cases and allowed us to anticipate SARS-CoV-2 incidence in one week, which is of special relevance in situations where the epidemiological monitoring system cannot be fully implemented.


The authors want to thank the Catalan Institute for Water Research (Institut Català de Recerca de l’Aigua, ICRA) and the Open Data initiative of the Generalitat de Catalunya for publishing the SARS-CoV-2 concentration and incidence data as open access information. The authors also want to acknowledge the funding of the INNO- 4COV-19 (European Union’s Horizon 2020 Research and Innovation Programme under Grant Agreement No 101016203) project City Sentinel V-Sense.


Peer Reviewed


Objectius de Desenvolupament Sostenible::3 - Salut i Benestar


Postprint (published version)

Tipo de documento

Article

Lengua

Inglés

Publicado por

Nature

Documentos relacionados

https://www.nature.com/articles/s41598-022-18518-9

info:eu-repo/grantAgreement/EC/H2020/101016203/EU/Boosting Innovation for COVID-19 Diagnostic, Prevention and Surveillance./INNO4COV-19

Citación recomendada

Esta citación se ha generado automáticamente.

Derechos

http://creativecommons.org/licenses/by-nc-nd/4.0/

Open Access

Attribution-NonCommercial-NoDerivatives 4.0 International

Este ítem aparece en la(s) siguiente(s) colección(ones)

E-prints [72954]