Abstract
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. 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. 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). 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.
Highlights
COVID-19 has become a global concern because it has extensive damage to health, social and economic systems worldwide
Based on the previous literature concerning the importance of implementing mathematical models for predicting COVID-19 dynamics in a short-term horizon, the aim of this study is to develop models for the case study of Saudi Arabia as a representative of the Arab Gulf countries which were, for a long period of time, among the COVID-19 pandemic outbreak epicenters alongside some countries in South America and India
As there is no efficient analytic method to solve such a problem [16] and because COVID-19 data are available on a discrete-time basis, this study investigated the use of the particle swarm optimization (PSO) as a powerful technique to obtain a feasible and sub-optimal solution
Summary
COVID-19 has become a global concern because it has extensive damage to health, social and economic systems worldwide. 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. 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. The highly infectious disease, SARS-CoV-2, more commonly known as COVID-19, has spread worldwide causing a high number of infections and deaths and affecting the social, health and economic systems of almost every country on the planet [1-5].
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