COVIDMe: a digital twin for COVID-19 self-assessment and detection
Document Type
Article
Publication Title
Digital Twin for Healthcare: Design, Challenges, and Solutions
Abstract
The COVID-19 virus has governments worldwide implementing new testing strategies, trying to increase their testing capacity so they can have a complete picture of the spread of COVID-19 with as much fidelity as possible. Simultaneously, people are looking to make sense of all the information and official recommendations to manage COVID-19. We present a DT for health in the context of the COVID-19 endemic stage aiming at bridging the gap between health authorities and the general population while offering an alternative approach to screen entire communities at a relatively low cost. We outline the architecture for the platform and present open challenges to make it a safe and effective tool to combat COVID-19 after our first iteration on it.
First Page
137
Last Page
156
DOI
10.1016/B978-0-32-399163-6.00012-3
Publication Date
1-1-2022
Keywords
AI-inference engine, Digital Twin implementation, mobile app, software framework
Recommended Citation
R. Martinez-Velazquez et al., "COVIDMe: a digital twin for COVID-19 self-assessment and detection," Digital Twin for Healthcare: Design, Challenges, and Solutions, pp. 137 - 156, Jan 2022.
The definitive version is available at https://doi.org/10.1016/B978-0-32-399163-6.00012-3
Comments
IR conditions: non-described