Article: Optimising SVR for epidemiological predictions: a case study on COVID-19 mortality in Japan Journal: International Journal of Artificial Intelligence and Soft Computing (IJAISC) 2024 Vol.8 No.5 pp.1 - 29 Abstract: This study enhances support vector regression machine (SVR) for COVID-19 mortality forecasting in Japan using three particle swarm optimisation (PSO) variants. Our main contributions include: 1) achieving superior model performance, notably with the fast convergence PSO-SVR variant, which outperforms existing models with an R-Squared value of 0.717; 2) demonstrating consistent and improved prediction accuracy across various PSO variants; 3) establishing the potential of our methods for broader applications beyond epidemiological modelling. Our findings, significantly advancing the accuracy and efficiency of predictive analytics in this domain, are benchmarked against prior studies, showing notable improvements in SVR hyperparameter optimisation. Inderscience Publishers - linking academia, business and industry through research

Title: Optimising SVR for epidemiological predictions: a case study on COVID-19 mortality in Japan

Authors: Edward R. Sykes; Yuan Wang

Addresses: School of Computer Science, University of Guelph, Guelph, Ontario, Canada ' Centre for Applied AI, Sheridan College, Oakville, Ontario, Canada

Abstract: This study enhances support vector regression machine (SVR) for COVID-19 mortality forecasting in Japan using three particle swarm optimisation (PSO) variants. Our main contributions include: 1) achieving superior model performance, notably with the fast convergence PSO-SVR variant, which outperforms existing models with an R-Squared value of 0.717; 2) demonstrating consistent and improved prediction accuracy across various PSO variants; 3) establishing the potential of our methods for broader applications beyond epidemiological modelling. Our findings, significantly advancing the accuracy and efficiency of predictive analytics in this domain, are benchmarked against prior studies, showing notable improvements in SVR hyperparameter optimisation.

Keywords: optimisation; particle swarm optimisation; PSO; support vector regression; SVR; COVID; forecasting; machine learning; Japan.

DOI: 10.1504/IJAISC.2024.143383

International Journal of Artificial Intelligence and Soft Computing, 2024 Vol.8 No.5, pp.1 - 29

Received: 27 May 2024
Accepted: 29 Oct 2024
Published online: 16 Dec 2024 *

Title: Optimising SVR for epidemiological predictions: a case study on COVID-19 mortality in Japan

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