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Synthea™ Novel coronavirus (COVID-19) model and synthetic data set
March through May 2020, a model of novel coronavirus (COVID-19) disease progression and treatment was constructed for the open-source Synthea patient simulation. The model was constructed using three peer-reviewed publications published in the early stages of the global pandemic, when less was known...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
The MITRE Corporation. Published by Elsevier B.V.
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7531559/ https://www.ncbi.nlm.nih.gov/pubmed/33043312 http://dx.doi.org/10.1016/j.ibmed.2020.100007 |
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author | Walonoski, Jason Klaus, Sybil Granger, Eldesia Hall, Dylan Gregorowicz, Andrew Neyarapally, George Watson, Abigail Eastman, Jeff |
author_facet | Walonoski, Jason Klaus, Sybil Granger, Eldesia Hall, Dylan Gregorowicz, Andrew Neyarapally, George Watson, Abigail Eastman, Jeff |
author_sort | Walonoski, Jason |
collection | PubMed |
description | March through May 2020, a model of novel coronavirus (COVID-19) disease progression and treatment was constructed for the open-source Synthea patient simulation. The model was constructed using three peer-reviewed publications published in the early stages of the global pandemic, when less was known, along with emerging resources, data, publications, and clinical knowledge. The simulation outputs synthetic Electronic Health Records (EHR), including the daily consumption of Personal Protective Equipment (PPE) and other medical devices and supplies. For this simulation, we generated 124,150 synthetic patients, with 88,166 infections and 18,177 hospitalized patients. Patient symptoms, disease severity, and morbidity outcomes were calibrated using clinical data from the peer-reviewed publications. 4.1% of all simulated infected patients died and 20.6% were hospitalized. At peak observation, 548 dialysis machines and 209 mechanical ventilators were needed. This simulation and the resulting data have been used for the development of algorithms and prototypes designed to address the current or future pandemics, and the model can continue to be refined to incorporate emerging COVID-19 knowledge, variations in patterns of care, and improvement in clinical outcomes. The resulting model, data, and analysis are available as open-source code on GitHub and an open-access data set is available for download. |
format | Online Article Text |
id | pubmed-7531559 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | The MITRE Corporation. Published by Elsevier B.V. |
record_format | MEDLINE/PubMed |
spelling | pubmed-75315592020-10-05 Synthea™ Novel coronavirus (COVID-19) model and synthetic data set Walonoski, Jason Klaus, Sybil Granger, Eldesia Hall, Dylan Gregorowicz, Andrew Neyarapally, George Watson, Abigail Eastman, Jeff Intell Based Med Article March through May 2020, a model of novel coronavirus (COVID-19) disease progression and treatment was constructed for the open-source Synthea patient simulation. The model was constructed using three peer-reviewed publications published in the early stages of the global pandemic, when less was known, along with emerging resources, data, publications, and clinical knowledge. The simulation outputs synthetic Electronic Health Records (EHR), including the daily consumption of Personal Protective Equipment (PPE) and other medical devices and supplies. For this simulation, we generated 124,150 synthetic patients, with 88,166 infections and 18,177 hospitalized patients. Patient symptoms, disease severity, and morbidity outcomes were calibrated using clinical data from the peer-reviewed publications. 4.1% of all simulated infected patients died and 20.6% were hospitalized. At peak observation, 548 dialysis machines and 209 mechanical ventilators were needed. This simulation and the resulting data have been used for the development of algorithms and prototypes designed to address the current or future pandemics, and the model can continue to be refined to incorporate emerging COVID-19 knowledge, variations in patterns of care, and improvement in clinical outcomes. The resulting model, data, and analysis are available as open-source code on GitHub and an open-access data set is available for download. The MITRE Corporation. Published by Elsevier B.V. 2020-11 2020-10-02 /pmc/articles/PMC7531559/ /pubmed/33043312 http://dx.doi.org/10.1016/j.ibmed.2020.100007 Text en © 2020 The MITRE Corporation Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. |
spellingShingle | Article Walonoski, Jason Klaus, Sybil Granger, Eldesia Hall, Dylan Gregorowicz, Andrew Neyarapally, George Watson, Abigail Eastman, Jeff Synthea™ Novel coronavirus (COVID-19) model and synthetic data set |
title | Synthea™ Novel coronavirus (COVID-19) model and synthetic data set |
title_full | Synthea™ Novel coronavirus (COVID-19) model and synthetic data set |
title_fullStr | Synthea™ Novel coronavirus (COVID-19) model and synthetic data set |
title_full_unstemmed | Synthea™ Novel coronavirus (COVID-19) model and synthetic data set |
title_short | Synthea™ Novel coronavirus (COVID-19) model and synthetic data set |
title_sort | synthea™ novel coronavirus (covid-19) model and synthetic data set |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7531559/ https://www.ncbi.nlm.nih.gov/pubmed/33043312 http://dx.doi.org/10.1016/j.ibmed.2020.100007 |
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