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Predicting COVID-19 mortalities for patients with special health conditions using an agent-based model
Resource type
Authors/contributors
- Mazurkiewicz, Erika (Author)
- Seesi, Sahar Al (Author)
- Abdel Raouf, Amal (Author)
Title
Predicting COVID-19 mortalities for patients with special health conditions using an agent-based model
Abstract
The spread of COVID-19 has thrown the world into a panic. We are constantly learning more about the virus every day, from how it spreads to who is more susceptible to becoming infected by different variants. Those with underlying respiratory conditions and other immunocompromised individuals need to be extra cautious regarding the virus. Many researchers have created COVID-19 trackers to detect the spread of COVID-19 around the world and show hot spots where COVID-19 cases are more prevalent. Previous work lacks the consideration of comorbidity as a factor of death rate. This work aims to create an agent-based model to predict comorbidity death rate caused by a health condition in addition to COVID-19. The model is evaluated using the symmetric mean absolute percentage error metric and proved to be very efficient.
Proceedings Title
2023 20th Learning and Technology Conference (L&T)
Conference Name
2023 20th Learning and Technology Conference (L&T)
Date
2023-01
Pages
42-47
Citation Key
mazurkiewiczPredictingCOVID19Mortalities2023
Library Catalog
IEEE Xplore
Extra
0 citations (Crossref) [2023-10-31]
Citation
Mazurkiewicz, E., Seesi, S. A., & Abdel Raouf, A. (2023). Predicting COVID-19 mortalities for patients with special health conditions using an agent-based model. 2023 20th Learning and Technology Conference (L&T), 42–47. https://doi.org/10.1109/LT58159.2023.10092342
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