Abstract
This paper proposes spread prediction of novel corona virus outbreak using different compartmental models and artificial intelligence (AI) methods. Real data for several months is collected from the Ministry of Health (MOH) website, Kingdom of Saudi Arabia and two compartmental models, namely SIR (susceptible, infectious, recovered) and SEIRD (susceptible, exposed, infectious, recovered, dead) are utilized to best fit the data. AI methods are well suited for short- and long-term stochastic forecasts. Keeping in view the inherent advantages of AI methods, adaptive neuro-fuzzy inference system (ANFIS) models are trained using the collected data to replicate the dynamic behavior of the COVID-19 spread in Kingdom of Saudi Arabia. The prediction comparison for COVID-19 spread is made between the compartmental and ANFIS models for both short- and long-term forecasts of the experimental data. From the presented results, ANFIS-based models show superior performance as compared to compartmental models.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
More From: International Journal of Modeling, Simulation, and Scientific Computing
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.