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covidscreen: a web app and R Package for assessing asymptomatic COVID-19 testing strategies

BACKGROUND: COVID-19 has caused over 305 million infections and nearly 5.5 million deaths globally. With complete eradication unlikely, organizations will need to evaluate their risk and the benefits of mitigation strategies, including the effects of regular asymptomatic testing. We developed a web...

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Autores principales: Smith, Jesse, Sun, Yilun, Hijano, Diego R., Hoffman, James M., Hakim, Hana, Webby, Richard J., Hayden, Randall T, Gaur, Aditya H., Armstrong, Gregory T., Mori, Motomi, Tang, Li
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9284969/
https://www.ncbi.nlm.nih.gov/pubmed/35840948
http://dx.doi.org/10.1186/s12889-022-13718-4
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author Smith, Jesse
Sun, Yilun
Hijano, Diego R.
Hoffman, James M.
Hakim, Hana
Webby, Richard J.
Hayden, Randall T
Gaur, Aditya H.
Armstrong, Gregory T.
Mori, Motomi
Tang, Li
author_facet Smith, Jesse
Sun, Yilun
Hijano, Diego R.
Hoffman, James M.
Hakim, Hana
Webby, Richard J.
Hayden, Randall T
Gaur, Aditya H.
Armstrong, Gregory T.
Mori, Motomi
Tang, Li
author_sort Smith, Jesse
collection PubMed
description BACKGROUND: COVID-19 has caused over 305 million infections and nearly 5.5 million deaths globally. With complete eradication unlikely, organizations will need to evaluate their risk and the benefits of mitigation strategies, including the effects of regular asymptomatic testing. We developed a web application and R package that provides estimates and visualizations to aid the assessment of organizational infection risk and testing benefits to facilitate decision-making, which combines internal and community information with malleable assumptions. RESULTS: Our web application, covidscreen, presents estimated values of risk metrics in an intuitive graphical format. It shows the current expected number of active, primarily community-acquired infections among employees in an organization. It calculates and explains the absolute and relative risk reduction of an intervention, relative to the baseline scenario, and shows the value of testing vaccinated and unvaccinated employees. In addition, the web interface allows users to profile risk over a chosen range of input values. The performance and output are illustrated using simulations and a real-world example from the employee testing program of a pediatric oncology specialty hospital. CONCLUSIONS: As the COVID-19 pandemic continues to evolve, covidscreen can assist organizations in making informed decisions about whether to incorporate covid test based screening as part of their on-campus risk-mitigation strategy. The web application, R package, and source code are freely available online (see “Availability of data and materials”). SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12889-022-13718-4.
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spelling pubmed-92849692022-07-15 covidscreen: a web app and R Package for assessing asymptomatic COVID-19 testing strategies Smith, Jesse Sun, Yilun Hijano, Diego R. Hoffman, James M. Hakim, Hana Webby, Richard J. Hayden, Randall T Gaur, Aditya H. Armstrong, Gregory T. Mori, Motomi Tang, Li BMC Public Health Software BACKGROUND: COVID-19 has caused over 305 million infections and nearly 5.5 million deaths globally. With complete eradication unlikely, organizations will need to evaluate their risk and the benefits of mitigation strategies, including the effects of regular asymptomatic testing. We developed a web application and R package that provides estimates and visualizations to aid the assessment of organizational infection risk and testing benefits to facilitate decision-making, which combines internal and community information with malleable assumptions. RESULTS: Our web application, covidscreen, presents estimated values of risk metrics in an intuitive graphical format. It shows the current expected number of active, primarily community-acquired infections among employees in an organization. It calculates and explains the absolute and relative risk reduction of an intervention, relative to the baseline scenario, and shows the value of testing vaccinated and unvaccinated employees. In addition, the web interface allows users to profile risk over a chosen range of input values. The performance and output are illustrated using simulations and a real-world example from the employee testing program of a pediatric oncology specialty hospital. CONCLUSIONS: As the COVID-19 pandemic continues to evolve, covidscreen can assist organizations in making informed decisions about whether to incorporate covid test based screening as part of their on-campus risk-mitigation strategy. The web application, R package, and source code are freely available online (see “Availability of data and materials”). SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12889-022-13718-4. BioMed Central 2022-07-15 /pmc/articles/PMC9284969/ /pubmed/35840948 http://dx.doi.org/10.1186/s12889-022-13718-4 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis 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 licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence 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 licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Software
Smith, Jesse
Sun, Yilun
Hijano, Diego R.
Hoffman, James M.
Hakim, Hana
Webby, Richard J.
Hayden, Randall T
Gaur, Aditya H.
Armstrong, Gregory T.
Mori, Motomi
Tang, Li
covidscreen: a web app and R Package for assessing asymptomatic COVID-19 testing strategies
title covidscreen: a web app and R Package for assessing asymptomatic COVID-19 testing strategies
title_full covidscreen: a web app and R Package for assessing asymptomatic COVID-19 testing strategies
title_fullStr covidscreen: a web app and R Package for assessing asymptomatic COVID-19 testing strategies
title_full_unstemmed covidscreen: a web app and R Package for assessing asymptomatic COVID-19 testing strategies
title_short covidscreen: a web app and R Package for assessing asymptomatic COVID-19 testing strategies
title_sort covidscreen: a web app and r package for assessing asymptomatic covid-19 testing strategies
topic Software
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9284969/
https://www.ncbi.nlm.nih.gov/pubmed/35840948
http://dx.doi.org/10.1186/s12889-022-13718-4
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