Susceptible exposed infectious recovered-machine learning for COVID-19 prediction in Saudi Arabia

Mutasem K. Alsmadi, Ghaith M. Jaradat, Sami A. Abahussain, Mohammed Fahed Tayfour, Usama A. Badawi, Hayat Alfagham, Muneerah Ebrahem Alshabanah, Daniah Abdulrahman Alrajhi, Hanouf Naif ALkhaldi, Njoud Ahmad Altuwaijri, Hany Answer ShoShan, Hayah Mohamed Abouelnaga, Ahmed Baz Mohamed Metwally


Susceptible exposed infectious recovered (SEIR) is among the epidemiological models used in forecasting the spread of disease in large populations. SEIR is a fitting model for coronavirus disease (COVID-19) spread prediction. Somehow, in its original form, SEIR could not measure the impact of lockdowns. So, in the SEIR equations system utilized in this study, a variable was included to evaluate the impact of varying levels of social distance on the transmission of COVID-19. Additionally, we applied artificial intelligence utilizing the deep neural network machine learning (ML) technique. On the initial spread data for Saudi Arabia that were available up to June 25th, 2021, this improved SEIR model was used. The study shows possible infection to around 3.1 million persons without lockdown in Saudi Arabia at the peak of spread, which lasts for about 3 months beginning from the lockdown date (March 21st). On the other hand, the Kingdom's current partial lockdown policy was estimated to cut the estimated number of infections to 0.5 million over nine months. The data shows that stricter lockdowns may successfully flatten the COVID-19 graph curve in Saudi Arabia. We successfully predicted the COVID-19 epidemic's peaks and sizes using our modified deep neural network (DNN) and SEIR model.


COVID-19; deep neural network; disease spread; lockdowns; machine learning; Saudi Arabia; susceptible exposed infectious recovered model;

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International Journal of Electrical and Computer Engineering (IJECE)
p-ISSN 2088-8708, e-ISSN 2722-2578

This journal is published by the Institute of Advanced Engineering and Science (IAES) in collaboration with Intelektual Pustaka Media Utama (IPMU).