Predicting COVID-19 mortalities for patients with special health conditions using an agent-based model - Mazurkiewicz, Erika; Seesi, Sahar Al; Abdel Raouf, Amal (2023 20th Learning and Technology Conference (L&T), 2023)
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.
Keywords: Agent-Based Model; COVID-19; Infectious diseases; Limiting; Measurement; Metaverse; Pandemics; Predictive models; comorbidity; dept.csc