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Predictive power of SARS-CoV-2 wastewater surveillance for diverse populations across a large geographical range
The COVID-19 pandemic has exacerbated the disparities in healthcare delivery in the US. Many communities had, and continue to have, limited access to COVID-19 testing, making it difficult to track the spread and impact of COVID-19 in early days of the outbreak. To address this issue we monitored sev...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Cold Spring Harbor Laboratory
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7852246/ https://www.ncbi.nlm.nih.gov/pubmed/33532795 http://dx.doi.org/10.1101/2021.01.23.21250376 |
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author | Melvin, Richard G. Chaudhry, Nabiha Georgewill, Onimitein Freese, Rebecca Simmons, Glenn E. |
author_facet | Melvin, Richard G. Chaudhry, Nabiha Georgewill, Onimitein Freese, Rebecca Simmons, Glenn E. |
author_sort | Melvin, Richard G. |
collection | PubMed |
description | The COVID-19 pandemic has exacerbated the disparities in healthcare delivery in the US. Many communities had, and continue to have, limited access to COVID-19 testing, making it difficult to track the spread and impact of COVID-19 in early days of the outbreak. To address this issue we monitored severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) RNA at the population-level using municipal wastewater influent from 19 cities across the state of Minnesota during the COVID-19 outbreak in Summer 2020. Viral RNA was detected in wastewater continually for 20-weeks for cities ranging in populations from 500 to >1, 000, 000. Using a novel indexing method, we were able to compare the relative levels of SARS-CoV-2 RNA for each city during this sampling period. Our data showed that viral RNA trends appeared to precede clinically confirmed cases across the state by several days. Lag analysis of statewide trends confirmed that wastewater SARS-CoV-2 RNA levels preceded new clinical cases by 15–17 days. At the regional level, new clinical cases lagged behind wastewater viral RNA anywhere from 4–20 days. Our data illustrates the advantages of monitoring at the population-level to detect outbreaks. Additionally, by tracking infections with this unbiased approach, resources can be directed to the most impacted communities before the need outpaces the capacity of local healthcare systems. |
format | Online Article Text |
id | pubmed-7852246 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Cold Spring Harbor Laboratory |
record_format | MEDLINE/PubMed |
spelling | pubmed-78522462021-02-03 Predictive power of SARS-CoV-2 wastewater surveillance for diverse populations across a large geographical range Melvin, Richard G. Chaudhry, Nabiha Georgewill, Onimitein Freese, Rebecca Simmons, Glenn E. medRxiv Article The COVID-19 pandemic has exacerbated the disparities in healthcare delivery in the US. Many communities had, and continue to have, limited access to COVID-19 testing, making it difficult to track the spread and impact of COVID-19 in early days of the outbreak. To address this issue we monitored severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) RNA at the population-level using municipal wastewater influent from 19 cities across the state of Minnesota during the COVID-19 outbreak in Summer 2020. Viral RNA was detected in wastewater continually for 20-weeks for cities ranging in populations from 500 to >1, 000, 000. Using a novel indexing method, we were able to compare the relative levels of SARS-CoV-2 RNA for each city during this sampling period. Our data showed that viral RNA trends appeared to precede clinically confirmed cases across the state by several days. Lag analysis of statewide trends confirmed that wastewater SARS-CoV-2 RNA levels preceded new clinical cases by 15–17 days. At the regional level, new clinical cases lagged behind wastewater viral RNA anywhere from 4–20 days. Our data illustrates the advantages of monitoring at the population-level to detect outbreaks. Additionally, by tracking infections with this unbiased approach, resources can be directed to the most impacted communities before the need outpaces the capacity of local healthcare systems. Cold Spring Harbor Laboratory 2021-01-30 /pmc/articles/PMC7852246/ /pubmed/33532795 http://dx.doi.org/10.1101/2021.01.23.21250376 Text en https://creativecommons.org/licenses/by-nc-nd/4.0/This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (https://creativecommons.org/licenses/by-nc-nd/4.0/) , which allows reusers to copy and distribute the material in any medium or format in unadapted form only, for noncommercial purposes only, and only so long as attribution is given to the creator. |
spellingShingle | Article Melvin, Richard G. Chaudhry, Nabiha Georgewill, Onimitein Freese, Rebecca Simmons, Glenn E. Predictive power of SARS-CoV-2 wastewater surveillance for diverse populations across a large geographical range |
title | Predictive power of SARS-CoV-2 wastewater surveillance for diverse populations across a large geographical range |
title_full | Predictive power of SARS-CoV-2 wastewater surveillance for diverse populations across a large geographical range |
title_fullStr | Predictive power of SARS-CoV-2 wastewater surveillance for diverse populations across a large geographical range |
title_full_unstemmed | Predictive power of SARS-CoV-2 wastewater surveillance for diverse populations across a large geographical range |
title_short | Predictive power of SARS-CoV-2 wastewater surveillance for diverse populations across a large geographical range |
title_sort | predictive power of sars-cov-2 wastewater surveillance for diverse populations across a large geographical range |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7852246/ https://www.ncbi.nlm.nih.gov/pubmed/33532795 http://dx.doi.org/10.1101/2021.01.23.21250376 |
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