Analysis and modeling of COVID-19 epidemic dynamics in Saudi Arabia using SIR-PSO and machine learning approaches

  • Rafat Zrieq Department of Public Health, College of Public Health and Health Informatics, University of Ha'il, Ha'il, Saudi Arabia
  • Sahbi Boubaker Department of Computer and Networks Engineering, College of Computer Science and Engineering, University of Jeddah, Jeddah, Saudi Arabia
  • Souad Kamel Department of Computer and Networks Engineering, College of Computer Science and Engineering, University of Jeddah, Jeddah, Saudi Arabia
  • Mohamed Alzain Department of Public Health, College of Public Health and Health Informatics, University of Ha'il, Ha'il, Saudi Arabia
  • Fahad D. Algahtani Department of Public Health, College of Public Health and Health Informatics, University of Ha'il, Ha'il, Saudi Arabia
Keywords: COVID-19 epidemic, dynamics modeling, prediction, SIR-PSO model, FF-ANN, performance metrics

Abstract

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. The collected data were divided into training and validation datasets. The effectiveness of the investigated models was evaluated by using various performance metrics. The Susceptible-Infected-Recovered-Particle-Swarm-Optimization model was found to well predict the cumulative infected and recovered cases and to optimally tune the contact rate and the characteristic duration of the illness. The feed-forward artificial neural network model was found to be efficient in modeling daily new and cumulative infections, recoveries and deaths.

Results: The forecasts provided by the investigated models had high coefficient of determination values of more than 0.97 and low mean absolute percentage errors (around 7% on average).

Conclusions: Both the Susceptible-Infected-Recovered-Particle-Swarm-Optimization and feed-forward artificial neural network models were efficient in modeling COVID-19 dynamics in Saudi Arabia. The results produced by the models can help the Saudi health authorities to analyze the virus dynamics and prepare efficient measures to control any future occurrence of the epidemic.

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Published
2022-01-31
How to Cite
1.
Zrieq R, Boubaker S, Kamel S, Alzain M, Algahtani FD (2022) Analysis and modeling of COVID-19 epidemic dynamics in Saudi Arabia using SIR-PSO and machine learning approaches. J Infect Dev Ctries 16:90-100. doi: 10.3855/jidc.15004
Section
Coronavirus Pandemic