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Geographic Targeting of COVID-19 Testing to Maximize Detection in Los Angeles County
BACKGROUND: Many severe acute respiratory syndrome coronavirus 2 infections have not been detected, reported, or isolated. For community testing programs to locate the most cases under limited testing resources, we developed and evaluated quantitative approaches for geographic targeting of increased...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10352645/ https://www.ncbi.nlm.nih.gov/pubmed/37469616 http://dx.doi.org/10.1093/ofid/ofad331 |
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author | Jia, Katherine M Kahn, Rebecca Fisher, Rebecca Balter, Sharon Lipsitch, Marc |
author_facet | Jia, Katherine M Kahn, Rebecca Fisher, Rebecca Balter, Sharon Lipsitch, Marc |
author_sort | Jia, Katherine M |
collection | PubMed |
description | BACKGROUND: Many severe acute respiratory syndrome coronavirus 2 infections have not been detected, reported, or isolated. For community testing programs to locate the most cases under limited testing resources, we developed and evaluated quantitative approaches for geographic targeting of increased coronavirus disease 2019 testing efforts. METHODS: For every week from December 5, 2021, to July 23, 2022, testing and vaccination data were obtained in ∼340 cities/communities in Los Angeles County, and models were developed to predict which cities/communities would have the highest test positivity 2 weeks ahead. A series of counterfactual scenarios were constructed to explore the additional number of cases that could be detected under targeted testing. RESULTS: The simplest model based on most recent test positivity performed nearly as well as the best model based on most recent test positivity and weekly tests per 100 persons in identifying communities that would maximize the average yield of cases per test in the following 2 weeks and almost as well as the perfect knowledge of the actual positivity 2 weeks ahead. In the counterfactual scenario, increasing testing by 1% 2 weeks ahead and allocating all tests to communities with the top 10% of predicted positivity would yield a 2% increase in detected cases. CONCLUSIONS: Simple models based on current test positivity can predict which communities may have the highest positivity 2 weeks ahead and hence could be allocated with more testing resources. |
format | Online Article Text |
id | pubmed-10352645 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-103526452023-07-19 Geographic Targeting of COVID-19 Testing to Maximize Detection in Los Angeles County Jia, Katherine M Kahn, Rebecca Fisher, Rebecca Balter, Sharon Lipsitch, Marc Open Forum Infect Dis Major Article BACKGROUND: Many severe acute respiratory syndrome coronavirus 2 infections have not been detected, reported, or isolated. For community testing programs to locate the most cases under limited testing resources, we developed and evaluated quantitative approaches for geographic targeting of increased coronavirus disease 2019 testing efforts. METHODS: For every week from December 5, 2021, to July 23, 2022, testing and vaccination data were obtained in ∼340 cities/communities in Los Angeles County, and models were developed to predict which cities/communities would have the highest test positivity 2 weeks ahead. A series of counterfactual scenarios were constructed to explore the additional number of cases that could be detected under targeted testing. RESULTS: The simplest model based on most recent test positivity performed nearly as well as the best model based on most recent test positivity and weekly tests per 100 persons in identifying communities that would maximize the average yield of cases per test in the following 2 weeks and almost as well as the perfect knowledge of the actual positivity 2 weeks ahead. In the counterfactual scenario, increasing testing by 1% 2 weeks ahead and allocating all tests to communities with the top 10% of predicted positivity would yield a 2% increase in detected cases. CONCLUSIONS: Simple models based on current test positivity can predict which communities may have the highest positivity 2 weeks ahead and hence could be allocated with more testing resources. Oxford University Press 2023-06-27 /pmc/articles/PMC10352645/ /pubmed/37469616 http://dx.doi.org/10.1093/ofid/ofad331 Text en © The Author(s) 2023. Published by Oxford University Press on behalf of Infectious Diseases Society of America. https://creativecommons.org/licenses/by-nc-nd/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs licence (https://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial reproduction and distribution of the work, in any medium, provided the original work is not altered or transformed in any way, and that the work is properly cited. For commercial re-use, please contact journals.permissions@oup.com |
spellingShingle | Major Article Jia, Katherine M Kahn, Rebecca Fisher, Rebecca Balter, Sharon Lipsitch, Marc Geographic Targeting of COVID-19 Testing to Maximize Detection in Los Angeles County |
title | Geographic Targeting of COVID-19 Testing to Maximize Detection in Los Angeles County |
title_full | Geographic Targeting of COVID-19 Testing to Maximize Detection in Los Angeles County |
title_fullStr | Geographic Targeting of COVID-19 Testing to Maximize Detection in Los Angeles County |
title_full_unstemmed | Geographic Targeting of COVID-19 Testing to Maximize Detection in Los Angeles County |
title_short | Geographic Targeting of COVID-19 Testing to Maximize Detection in Los Angeles County |
title_sort | geographic targeting of covid-19 testing to maximize detection in los angeles county |
topic | Major Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10352645/ https://www.ncbi.nlm.nih.gov/pubmed/37469616 http://dx.doi.org/10.1093/ofid/ofad331 |
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