Modeling and Simulation: A study on predicting the outbreak of COVID-19 in Saudi Arabia

Abstract

The novel coronavirus (Covid-19) infection has resulted in an ongoing pandemic affecting health system and economy of more than 200 countries around the world. Mathematical models are used to predict the biological and epidemiological trends of an epidemic and develop methods for controlling it. In this work, we use mathematical model perspective to study the role of behavior change in slowing the spread of the COVID-19 disease in Saudi Arabia. The real-time updated data from 1st May 2020 to 8th January 2021 is collected from Saudi Ministry of Health, aiming to provide dynamic behaviors of the pandemic in Saudi Arabia. During this period, it has infected 297,205 people, resulting in 6124 deaths with the mortality rate 2.06 %. There is weak positive relationship between the spread of the infection and mortality (R2 =0.412). We use Susceptible-Exposed-Infection-Recovered (SEIR) mode, the logistic growth model and with special focus on the exposed, infection and recovery individuals to simulate the final phase of the outbreak. The results indicate that social distancing, good hygienic conditions, and travel limitation are the crucial measures to prevent further spreading of the epidemic.

COVID-19 Cases Prediction in Saudi Arabia Using Tree-based Ensemble Models

“…Current research work in the prediction of COVID-19 included using deep learning methods like ANN, RNN and LSTM [25][26][27], SEIR model [21,28], SIR model [29], Singular Spectrum Analysis [30,31] and Generalized Richards Model [32]. Most of these studies do not focus on the daily new cases, and if they do, they are using very limited datasets [25,31].…”