Generalized Richards model for predicting COVID-19 dynamics in Saudi Arabia based on particle swarm optimization Algorithm

Rafat Zreiq; Souad Kamel; Sahbi Boubaker; Asma A Al-Shammary; Fahad D Algahtani; Fares Alshammari; Rafat Zreiq; Souad Kamel; Sahbi Boubaker; Asma A Al-Shammary; Fahad D Algahtani; Fares Alshammari

doi:10.3934/publichealth.2020064

AIMS Public Health

2020, Volume 7, Issue 4: 828-843. doi: 10.3934/publichealth.2020064

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Generalized Richards model for predicting COVID-19 dynamics in Saudi Arabia based on particle swarm optimization Algorithm

1.
Department of Public Health, College of Public Health and Health Informatics, University of Ha'il, Ha'il, Saudi Arabia
2.
Molecular Diagnostic and Personalized Therapeutics Unit, University of Ha'il, Ha'il, Saudi Arabia
3.
Department of Computer & Networks Engineering, College of Computer Science and Engineering, University of Jeddah, Jeddah, Saudi Arabia
4.
Laboratory of Control, Electric Systems and Environment, National College of Engineering of Monastir, Tunisia
5.
Department of Biology, Faculty of Science, University of Ha'il, Ha'il, Saudi Arabia
6.
Department of Health Informatics, College of Public Health and Health Informatics, University of Ha'il, Ha'il, Saudi Arabia

Received: 19 September 2020 Accepted: 29 October 2020 Published: 02 November 2020

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. The prediction problem is formulated as an optimization framework and solved using a Particle Swarm Optimization (PSO) algorithm. The Generalized Richards Model (GRM) has been found to be the best one in achieving two objectives: first, fitting the collected data (covering 223 days between March 2^nd and October 10, 2020) with the lowest mean absolute percentage error (MAPE = 3.2889%), the highest coefficient of determination (R² = 0.9953) and the lowest root mean squared error (RMSE = 8827); and second, predicting a probable end date found to be around the end of December 2020 with a projected number of 378,299 at the end of the outbreak. The obtained results may help the decision-makers to take suitable decisions related to the pandemic mitigation and containment and provide clear understanding of the virus dynamics in Saudi Arabia.
- COVID-19 dynamics,
- prediction,
- Generalized Richards Model (GRM),
- projected end date,
- Particle Swarm Optimization (PSO)
Citation: Rafat Zreiq, Souad Kamel, Sahbi Boubaker, Asma A Al-Shammary, Fahad D Algahtani, Fares Alshammari. Generalized Richards model for predicting COVID-19 dynamics in Saudi Arabia based on particle swarm optimization Algorithm[J]. AIMS Public Health, 2020, 7(4): 828-843. doi: 10.3934/publichealth.2020064

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Abstract

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. The prediction problem is formulated as an optimization framework and solved using a Particle Swarm Optimization (PSO) algorithm. The Generalized Richards Model (GRM) has been found to be the best one in achieving two objectives: first, fitting the collected data (covering 223 days between March 2^nd and October 10, 2020) with the lowest mean absolute percentage error (MAPE = 3.2889%), the highest coefficient of determination (R² = 0.9953) and the lowest root mean squared error (RMSE = 8827); and second, predicting a probable end date found to be around the end of December 2020 with a projected number of 378,299 at the end of the outbreak. The obtained results may help the decision-makers to take suitable decisions related to the pandemic mitigation and containment and provide clear understanding of the virus dynamics in Saudi Arabia.

Acknowledgments

This research has been funded by Scientific Research Deanship at University of Ha'il, Saudi Arabia through project number COVID-1936.

Conflict of interest

All authors declare no conflicts of interest in this paper.

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