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Synthea™ Novel coronavirus (COVID-19) model and synthetic data set
Author(s) -
Jason Walonoski,
Sybil Ann Klaus,
Eldesia Granger,
Dylan Hall,
Andrew Gregorowicz,
George A. Neyarapally,
Abigail Watson,
Jeff Eastman
Publication year - 2020
Publication title -
intelligence-based medicine
Language(s) - English
Resource type - Journals
ISSN - 2666-5212
DOI - 10.1016/j.ibmed.2020.100007
Subject(s) - download , covid-19 , pandemic , health records , computer science , open source , data set , set (abstract data type) , medicine , personal protective equipment , data science , intensive care medicine , medical emergency , health care , disease , infectious disease (medical specialty) , artificial intelligence , software , world wide web , economics , programming language , economic growth
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.

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