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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...

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Autores principales: Kraay, Alicia N.M., Nelson, Kristin N., Zhao, Conan Y., Demory, David, Weitz, Joshua S., Lopman, Benjamin A.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Cold Spring Harbor Laboratory 2020
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.
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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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