Publications by authors named "Souad Kamel"
Article Synopsis
- The first COVID-19 case in Saudi Arabia was reported on March 2, 2020, and by June 22, 2022, cases surged to 788,294, highlighting the urgent need for effective pandemic response strategies.
- This study aims to analyze historical COVID-19 data from Saudi Arabia and develop accurate forecasting models using ARIMA and Prophet techniques to predict new infections, recoveries, and deaths.
- The research found that both models performed well, with ARIMA being slightly better at forecasting while Prophet was easier to use, providing valuable insights for future pandemic preparedness and showcasing the effectiveness of Saudi Arabia's response measures.
Introduction: COVID-19 has become a global concern because it has extensive damage to health, social and economic systems worldwide. Consequently, there is an urgent need to develop tools to understand, analyze, monitor and control further outbreaks of the disease.
Methodology: The Susceptible Infected Recovered-Particle SwarmOptimization model and the feed-forward artificial neural network model were separately developed to model COVID-19 dynamics based on daily time-series data reported by the Saudi authorities from March 2, 2020 to February 21, 2021.
COVID-19 pandemic is spreading around the world becoming thus a serious concern for health, economic and social systems worldwide. In such situation, predicting as accurately as possible the future dynamics of the virus is a challenging problem for scientists and decision-makers. In this paper, four phenomenological epidemic models as well as Suspected-Infected-Recovered (SIR) model are investigated for predicting the cumulative number of infected cases in Saudi Arabia in addition to the probable end-date of the outbreak.
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