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SARS-CoV-2 RNA Wastewater Settled Solids Surveillance Frequency and Impact on Predicted COVID-19 Incidence Using a Distributed Lag Model
[Image: see text] Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) RNA concentrations in wastewater settled solids correlate well with coronavirus disease 2019 (COVID-19) incidence rates (IRs). Here, we develop distributed lag models to estimate IRs using concentrations of SARS-CoV-2 RNA...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
American Chemical Society
2022
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9092194/ https://www.ncbi.nlm.nih.gov/pubmed/36380770 http://dx.doi.org/10.1021/acsestwater.2c00074 |
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author | Schoen, Mary E. Wolfe, Marlene K. Li, Linlin Duong, Dorothea White, Bradley J. Hughes, Bridgette Boehm, Alexandria B. |
author_facet | Schoen, Mary E. Wolfe, Marlene K. Li, Linlin Duong, Dorothea White, Bradley J. Hughes, Bridgette Boehm, Alexandria B. |
author_sort | Schoen, Mary E. |
collection | PubMed |
description | [Image: see text] Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) RNA concentrations in wastewater settled solids correlate well with coronavirus disease 2019 (COVID-19) incidence rates (IRs). Here, we develop distributed lag models to estimate IRs using concentrations of SARS-CoV-2 RNA from wastewater solids and investigate the impact of sampling frequency on model performance. SARS-CoV-2 N gene and pepper mild mottle virus (PMMoV) RNA concentrations were measured daily at four wastewater treatment plants in California. Artificially reduced data sets were produced for each plant with sampling frequencies of once every 2, 3, 4, and 7 days. Sewershed-specific models that related daily N/PMMoV to IR were fit for each sampling frequency with data from mid-November 2020 through mid-July 2021, which included the period of time during which Delta emerged. Models were used to predict IRs during a subsequent out-of-sample time period. When sampling occurred at least once every 4 days, the in- and out-of-sample root-mean-square error changed by <7 cases/100 000 compared to daily sampling across sewersheds. This work illustrates that real-time, daily predictions of IR are possible with small errors, despite changes in circulating variants, when sampling frequency is once every 4 days or more. However, reduced sampling frequency may not serve other important wastewater surveillance use cases. |
format | Online Article Text |
id | pubmed-9092194 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | American Chemical Society |
record_format | MEDLINE/PubMed |
spelling | pubmed-90921942022-05-11 SARS-CoV-2 RNA Wastewater Settled Solids Surveillance Frequency and Impact on Predicted COVID-19 Incidence Using a Distributed Lag Model Schoen, Mary E. Wolfe, Marlene K. Li, Linlin Duong, Dorothea White, Bradley J. Hughes, Bridgette Boehm, Alexandria B. ACS ES T Water [Image: see text] Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) RNA concentrations in wastewater settled solids correlate well with coronavirus disease 2019 (COVID-19) incidence rates (IRs). Here, we develop distributed lag models to estimate IRs using concentrations of SARS-CoV-2 RNA from wastewater solids and investigate the impact of sampling frequency on model performance. SARS-CoV-2 N gene and pepper mild mottle virus (PMMoV) RNA concentrations were measured daily at four wastewater treatment plants in California. Artificially reduced data sets were produced for each plant with sampling frequencies of once every 2, 3, 4, and 7 days. Sewershed-specific models that related daily N/PMMoV to IR were fit for each sampling frequency with data from mid-November 2020 through mid-July 2021, which included the period of time during which Delta emerged. Models were used to predict IRs during a subsequent out-of-sample time period. When sampling occurred at least once every 4 days, the in- and out-of-sample root-mean-square error changed by <7 cases/100 000 compared to daily sampling across sewersheds. This work illustrates that real-time, daily predictions of IR are possible with small errors, despite changes in circulating variants, when sampling frequency is once every 4 days or more. However, reduced sampling frequency may not serve other important wastewater surveillance use cases. American Chemical Society 2022-05-03 2022-11-11 /pmc/articles/PMC9092194/ /pubmed/36380770 http://dx.doi.org/10.1021/acsestwater.2c00074 Text en © 2022 The Authors. Published by American Chemical Society https://creativecommons.org/licenses/by-nc-nd/4.0/Permits non-commercial access and re-use, provided that author attribution and integrity are maintained; but does not permit creation of adaptations or other derivative works (https://creativecommons.org/licenses/by-nc-nd/4.0/). |
spellingShingle | Schoen, Mary E. Wolfe, Marlene K. Li, Linlin Duong, Dorothea White, Bradley J. Hughes, Bridgette Boehm, Alexandria B. SARS-CoV-2 RNA Wastewater Settled Solids Surveillance Frequency and Impact on Predicted COVID-19 Incidence Using a Distributed Lag Model |
title | SARS-CoV-2 RNA Wastewater Settled Solids Surveillance Frequency and Impact on Predicted COVID-19 Incidence Using a Distributed Lag Model |
title_full | SARS-CoV-2 RNA Wastewater Settled Solids Surveillance Frequency and Impact on Predicted COVID-19 Incidence Using a Distributed Lag Model |
title_fullStr | SARS-CoV-2 RNA Wastewater Settled Solids Surveillance Frequency and Impact on Predicted COVID-19 Incidence Using a Distributed Lag Model |
title_full_unstemmed | SARS-CoV-2 RNA Wastewater Settled Solids Surveillance Frequency and Impact on Predicted COVID-19 Incidence Using a Distributed Lag Model |
title_short | SARS-CoV-2 RNA Wastewater Settled Solids Surveillance Frequency and Impact on Predicted COVID-19 Incidence Using a Distributed Lag Model |
title_sort | sars-cov-2 rna wastewater settled solids surveillance frequency and impact on predicted covid-19 incidence using a distributed lag model |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9092194/ https://www.ncbi.nlm.nih.gov/pubmed/36380770 http://dx.doi.org/10.1021/acsestwater.2c00074 |
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