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Data-driven testing program improves detection of COVID-19 cases and reduces community transmission
COVID-19 remains a global threat in the face of emerging SARS-CoV-2 variants and gaps in vaccine administration and availability. In this study, we analyze a data-driven COVID-19 testing program implemented at a mid-sized university, which utilized two simple, diverse, and easily interpretable machi...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Nature Publishing Group UK
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8837751/ https://www.ncbi.nlm.nih.gov/pubmed/35149754 http://dx.doi.org/10.1038/s41746-022-00562-4 |
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author | Krieg, Steven J. Avendano, Carolina Grantham-Brown, Evan Lilienfeld Asbun, Aaron Schnur, Jennifer J. Miranda, Marie Lynn Chawla, Nitesh V. |
author_facet | Krieg, Steven J. Avendano, Carolina Grantham-Brown, Evan Lilienfeld Asbun, Aaron Schnur, Jennifer J. Miranda, Marie Lynn Chawla, Nitesh V. |
author_sort | Krieg, Steven J. |
collection | PubMed |
description | COVID-19 remains a global threat in the face of emerging SARS-CoV-2 variants and gaps in vaccine administration and availability. In this study, we analyze a data-driven COVID-19 testing program implemented at a mid-sized university, which utilized two simple, diverse, and easily interpretable machine learning models to predict which students were at elevated risk and should be tested. The program produced a positivity rate of 0.53% (95% CI 0.34–0.77%) from 20,862 tests, with 1.49% (95% CI 1.15–1.89%) of students testing positive within five days of the initial test—a significant increase from the general surveillance baseline, which produced a positivity rate of 0.37% (95% CI 0.28–0.47%) with 0.67% (95% CI 0.55–0.81%) testing positive within five days. Close contacts who were predicted by the data-driven models were tested much more quickly on average (0.94 days from reported exposure; 95% CI 0.78–1.11) than those who were manually contact traced (1.92 days; 95% CI 1.81–2.02). We further discuss how other universities, business, and organizations could adopt similar strategies to help quickly identify positive cases and reduce community transmission. |
format | Online Article Text |
id | pubmed-8837751 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-88377512022-03-02 Data-driven testing program improves detection of COVID-19 cases and reduces community transmission Krieg, Steven J. Avendano, Carolina Grantham-Brown, Evan Lilienfeld Asbun, Aaron Schnur, Jennifer J. Miranda, Marie Lynn Chawla, Nitesh V. NPJ Digit Med Article COVID-19 remains a global threat in the face of emerging SARS-CoV-2 variants and gaps in vaccine administration and availability. In this study, we analyze a data-driven COVID-19 testing program implemented at a mid-sized university, which utilized two simple, diverse, and easily interpretable machine learning models to predict which students were at elevated risk and should be tested. The program produced a positivity rate of 0.53% (95% CI 0.34–0.77%) from 20,862 tests, with 1.49% (95% CI 1.15–1.89%) of students testing positive within five days of the initial test—a significant increase from the general surveillance baseline, which produced a positivity rate of 0.37% (95% CI 0.28–0.47%) with 0.67% (95% CI 0.55–0.81%) testing positive within five days. Close contacts who were predicted by the data-driven models were tested much more quickly on average (0.94 days from reported exposure; 95% CI 0.78–1.11) than those who were manually contact traced (1.92 days; 95% CI 1.81–2.02). We further discuss how other universities, business, and organizations could adopt similar strategies to help quickly identify positive cases and reduce community transmission. Nature Publishing Group UK 2022-02-11 /pmc/articles/PMC8837751/ /pubmed/35149754 http://dx.doi.org/10.1038/s41746-022-00562-4 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 license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license 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 license, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Article Krieg, Steven J. Avendano, Carolina Grantham-Brown, Evan Lilienfeld Asbun, Aaron Schnur, Jennifer J. Miranda, Marie Lynn Chawla, Nitesh V. Data-driven testing program improves detection of COVID-19 cases and reduces community transmission |
title | Data-driven testing program improves detection of COVID-19 cases and reduces community transmission |
title_full | Data-driven testing program improves detection of COVID-19 cases and reduces community transmission |
title_fullStr | Data-driven testing program improves detection of COVID-19 cases and reduces community transmission |
title_full_unstemmed | Data-driven testing program improves detection of COVID-19 cases and reduces community transmission |
title_short | Data-driven testing program improves detection of COVID-19 cases and reduces community transmission |
title_sort | data-driven testing program improves detection of covid-19 cases and reduces community transmission |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8837751/ https://www.ncbi.nlm.nih.gov/pubmed/35149754 http://dx.doi.org/10.1038/s41746-022-00562-4 |
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