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Assessing wastewater-based epidemiology for the prediction of SARS-CoV-2 incidence in Catalonia
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...
Autores principales: | , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9443647/ https://www.ncbi.nlm.nih.gov/pubmed/36064874 http://dx.doi.org/10.1038/s41598-022-18518-9 |
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author | Joseph-Duran, Bernat Serra-Compte, Albert Sàrrias, Miquel Gonzalez, Susana López, Daniel Prats, Clara Català, Martí Alvarez-Lacalle, Enric Alonso, Sergio Arnaldos, Marina |
author_facet | Joseph-Duran, Bernat Serra-Compte, Albert Sàrrias, Miquel Gonzalez, Susana López, Daniel Prats, Clara Català, Martí Alvarez-Lacalle, Enric Alonso, Sergio Arnaldos, Marina |
author_sort | Joseph-Duran, Bernat |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-9443647 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-94436472022-09-06 Assessing wastewater-based epidemiology for the prediction of SARS-CoV-2 incidence in Catalonia Joseph-Duran, Bernat Serra-Compte, Albert Sàrrias, Miquel Gonzalez, Susana López, Daniel Prats, Clara Català, Martí Alvarez-Lacalle, Enric Alonso, Sergio Arnaldos, Marina Sci Rep Article 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. Nature Publishing Group UK 2022-09-05 /pmc/articles/PMC9443647/ /pubmed/36064874 http://dx.doi.org/10.1038/s41598-022-18518-9 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Article Joseph-Duran, Bernat Serra-Compte, Albert Sàrrias, Miquel Gonzalez, Susana López, Daniel Prats, Clara Català, Martí Alvarez-Lacalle, Enric Alonso, Sergio Arnaldos, Marina Assessing wastewater-based epidemiology for the prediction of SARS-CoV-2 incidence in Catalonia |
title | Assessing wastewater-based epidemiology for the prediction of SARS-CoV-2 incidence in Catalonia |
title_full | Assessing wastewater-based epidemiology for the prediction of SARS-CoV-2 incidence in Catalonia |
title_fullStr | Assessing wastewater-based epidemiology for the prediction of SARS-CoV-2 incidence in Catalonia |
title_full_unstemmed | Assessing wastewater-based epidemiology for the prediction of SARS-CoV-2 incidence in Catalonia |
title_short | Assessing wastewater-based epidemiology for the prediction of SARS-CoV-2 incidence in Catalonia |
title_sort | assessing wastewater-based epidemiology for the prediction of sars-cov-2 incidence in catalonia |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9443647/ https://www.ncbi.nlm.nih.gov/pubmed/36064874 http://dx.doi.org/10.1038/s41598-022-18518-9 |
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