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Modeling serological testing to inform relaxation of social distancing for COVID-19 control
Serological testing remains a passive component of the current public health response to the COVID-19 pandemic. Using a transmission model, we examined how serology can be implemented to allow seropositive individuals to increase levels of social interaction while offsetting transmission risks. We s...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Cold Spring Harbor Laboratory
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7273287/ https://www.ncbi.nlm.nih.gov/pubmed/32511519 http://dx.doi.org/10.1101/2020.04.24.20078576 |
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author | Kraay, Alicia N.M. Nelson, Kristin N. Zhao, Conan Y. Demory, David Weitz, Joshua S. Lopman, Benjamin A. |
author_facet | Kraay, Alicia N.M. Nelson, Kristin N. Zhao, Conan Y. Demory, David Weitz, Joshua S. Lopman, Benjamin A. |
author_sort | Kraay, Alicia N.M. |
collection | PubMed |
description | Serological testing remains a passive component of the current public health response to the COVID-19 pandemic. Using a transmission model, we examined how serology can be implemented to allow seropositive individuals to increase levels of social interaction while offsetting transmission risks. We simulated the use of widespread serological testing in three metropolitan areas with different initial outbreak timing and severity characteristics: New York City, South Florida, and Washington Puget Sound. In our model, we use realistic serological assay characteristics, in which tested seropositive individuals partially restore their social contacts and act as immunological ‘shields’. Compared to a scenario with no intervention, beginning a mass serological testing program on November 1, 2020 was predicted to avert 15,000 deaths (28% reduction, 95% CrI: 0.4%−30.2%) in New York City, 3,000 (31.1% reduction, 95% CrI: 26.4%−33.3%) in South Florida and 10,000 (60.3% reduction, 95% CrI: 50.2%−60.7%) in Washington State by June 2021. In all three sites, widespread serological testing substantially blunted new waves of transmission. Serological testing has the potential to mitigate the impacts of the COVID-19 pandemic while also allowing a substantial number of individuals to safely return to social interactions and economic activity. |
format | Online Article Text |
id | pubmed-7273287 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Cold Spring Harbor Laboratory |
record_format | MEDLINE/PubMed |
spelling | pubmed-72732872020-06-07 Modeling serological testing to inform relaxation of social distancing for COVID-19 control Kraay, Alicia N.M. Nelson, Kristin N. Zhao, Conan Y. Demory, David Weitz, Joshua S. Lopman, Benjamin A. medRxiv Article Serological testing remains a passive component of the current public health response to the COVID-19 pandemic. Using a transmission model, we examined how serology can be implemented to allow seropositive individuals to increase levels of social interaction while offsetting transmission risks. We simulated the use of widespread serological testing in three metropolitan areas with different initial outbreak timing and severity characteristics: New York City, South Florida, and Washington Puget Sound. In our model, we use realistic serological assay characteristics, in which tested seropositive individuals partially restore their social contacts and act as immunological ‘shields’. Compared to a scenario with no intervention, beginning a mass serological testing program on November 1, 2020 was predicted to avert 15,000 deaths (28% reduction, 95% CrI: 0.4%−30.2%) in New York City, 3,000 (31.1% reduction, 95% CrI: 26.4%−33.3%) in South Florida and 10,000 (60.3% reduction, 95% CrI: 50.2%−60.7%) in Washington State by June 2021. In all three sites, widespread serological testing substantially blunted new waves of transmission. Serological testing has the potential to mitigate the impacts of the COVID-19 pandemic while also allowing a substantial number of individuals to safely return to social interactions and economic activity. Cold Spring Harbor Laboratory 2020-11-17 /pmc/articles/PMC7273287/ /pubmed/32511519 http://dx.doi.org/10.1101/2020.04.24.20078576 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 Kraay, Alicia N.M. Nelson, Kristin N. Zhao, Conan Y. Demory, David Weitz, Joshua S. Lopman, Benjamin A. Modeling serological testing to inform relaxation of social distancing for COVID-19 control |
title | Modeling serological testing to inform relaxation of social distancing for COVID-19 control |
title_full | Modeling serological testing to inform relaxation of social distancing for COVID-19 control |
title_fullStr | Modeling serological testing to inform relaxation of social distancing for COVID-19 control |
title_full_unstemmed | Modeling serological testing to inform relaxation of social distancing for COVID-19 control |
title_short | Modeling serological testing to inform relaxation of social distancing for COVID-19 control |
title_sort | modeling serological testing to inform relaxation of social distancing for covid-19 control |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7273287/ https://www.ncbi.nlm.nih.gov/pubmed/32511519 http://dx.doi.org/10.1101/2020.04.24.20078576 |
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