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Estimation of COVID-19 spread curves integrating global data and borrowing information
Currently, novel coronavirus disease 2019 (COVID-19) is a big threat to global health. The rapid spread of the virus has created pandemic, and countries all over the world are struggling with a surge in COVID-19 infected cases. There are no drugs or other therapeutics approved by the US Food and Dru...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7390340/ https://www.ncbi.nlm.nih.gov/pubmed/32726361 http://dx.doi.org/10.1371/journal.pone.0236860 |
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author | Lee, Se Yoon Lei, Bowen Mallick, Bani |
author_facet | Lee, Se Yoon Lei, Bowen Mallick, Bani |
author_sort | Lee, Se Yoon |
collection | PubMed |
description | Currently, novel coronavirus disease 2019 (COVID-19) is a big threat to global health. The rapid spread of the virus has created pandemic, and countries all over the world are struggling with a surge in COVID-19 infected cases. There are no drugs or other therapeutics approved by the US Food and Drug Administration to prevent or treat COVID-19: information on the disease is very limited and scattered even if it exists. This motivates the use of data integration, combining data from diverse sources and eliciting useful information with a unified view of them. In this paper, we propose a Bayesian hierarchical model that integrates global data for real-time prediction of infection trajectory for multiple countries. Because the proposed model takes advantage of borrowing information across multiple countries, it outperforms an existing individual country-based model. As fully Bayesian way has been adopted, the model provides a powerful predictive tool endowed with uncertainty quantification. Additionally, a joint variable selection technique has been integrated into the proposed modeling scheme, which aimed to identify possible country-level risk factors for severe disease due to COVID-19. |
format | Online Article Text |
id | pubmed-7390340 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-73903402020-08-05 Estimation of COVID-19 spread curves integrating global data and borrowing information Lee, Se Yoon Lei, Bowen Mallick, Bani PLoS One Research Article Currently, novel coronavirus disease 2019 (COVID-19) is a big threat to global health. The rapid spread of the virus has created pandemic, and countries all over the world are struggling with a surge in COVID-19 infected cases. There are no drugs or other therapeutics approved by the US Food and Drug Administration to prevent or treat COVID-19: information on the disease is very limited and scattered even if it exists. This motivates the use of data integration, combining data from diverse sources and eliciting useful information with a unified view of them. In this paper, we propose a Bayesian hierarchical model that integrates global data for real-time prediction of infection trajectory for multiple countries. Because the proposed model takes advantage of borrowing information across multiple countries, it outperforms an existing individual country-based model. As fully Bayesian way has been adopted, the model provides a powerful predictive tool endowed with uncertainty quantification. Additionally, a joint variable selection technique has been integrated into the proposed modeling scheme, which aimed to identify possible country-level risk factors for severe disease due to COVID-19. Public Library of Science 2020-07-29 /pmc/articles/PMC7390340/ /pubmed/32726361 http://dx.doi.org/10.1371/journal.pone.0236860 Text en © 2020 Lee et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Lee, Se Yoon Lei, Bowen Mallick, Bani Estimation of COVID-19 spread curves integrating global data and borrowing information |
title | Estimation of COVID-19 spread curves integrating global data and borrowing information |
title_full | Estimation of COVID-19 spread curves integrating global data and borrowing information |
title_fullStr | Estimation of COVID-19 spread curves integrating global data and borrowing information |
title_full_unstemmed | Estimation of COVID-19 spread curves integrating global data and borrowing information |
title_short | Estimation of COVID-19 spread curves integrating global data and borrowing information |
title_sort | estimation of covid-19 spread curves integrating global data and borrowing information |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7390340/ https://www.ncbi.nlm.nih.gov/pubmed/32726361 http://dx.doi.org/10.1371/journal.pone.0236860 |
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