Advanced forecasting of COVID-19 epidemic: Leveraging ensemble models, advanced optimization, and decomposition techniques

• Published in: Computers in biology and medicine 2024-06, Vol.175, p.108442, Article 108442
• Main Authors: Yin, Yingyu, Ahmadianfar, Iman, Karim, Faten Khalid, Elmannai, Hela
• Format: Article
• Language: English
• Subjects: A-DEPSO; Algorithms; Coronaviruses; Correlation coefficient; Correlation coefficients; COVID-19; COVID-19 - epidemiology; Datasets; Decomposition; Deep random vector functional link; Disease control; Ensemble model; Epidemic models; Epidemics; Epidemiological Models; Evolutionary computation; Fatalities; Forecasting; Forecasting - methods; Humans; Kernel ridge; Machine Learning; Medical research; Models, Statistical; Outbreaks; Pandemics; Particle swarm optimization; Predictions; SARS-CoV-2; Support vector machines; Viral diseases
• ISSN: 0010-4825; 1879-0534; 1879-0534
• DOI: 10.1016/j.compbiomed.2024.108442

In the global effort to address the outbreak of the new coronavirus pneumonia (COVID-19) pandemic, accurate forecasting of epidemic patterns has become crucial for implementing successful interventions aimed at preventing and controlling the spread of the disease. The correct prediction of the course of COVID-19 outbreaks is a complex and challenging task, mainly because of the significant volatility in the data series related to COVID-19. Previous studies have been limited by the exclusive use of individual forecasting techniques in epidemic modeling, disregarding the integration of diverse prediction procedures. The lack of attention to detail in this situation can yield worse-than-ideal results. Consequently, this study introduces a novel ensemble framework that integrates three machine learning methods (kernel ridge regression (KRidge), Deep random vector functional link (dRVFL), and ridge regression) within a linear relationship (L-KRidge-dRVFL-Ridge). The optimization of this framework is accomplished through a distinctive approach, specifically adaptive differential evolution and particle swarm optimization (A-DEPSO). Moreover, an effective decomposition method, known as time-varying filter empirical mode decomposition (TVF-EMD), is employed to decompose the input variables. A feature selection technique, specifically using the light gradient boosting machine (LGBM), is also implemented to extract the most influential input variables. The daily datasets of COVID-19 collected from two countries, namely Italy and Poland, were used as the experimental examples. Additionally, all models are implemented to forecast COVID-19 at two-time horizons: 10- and 14-day ahead (t+10 and t+14). According to the results, the proposed model can yield higher correlation coefficient (R) for both case studies: Italy (t+10 = 0.965, t+14 = 0.961) and Poland (t+10 = 0.952, t+14 = 0.940) than the other models. The experimental results demonstrate that the model suggested in this paper has outstanding results in various kinds of complex epidemic prediction situations. The proposed ensemble model demonstrates exceptional accuracy and resilience, outperforming all similar models in terms of efficacy. •Introduces novel KRidge, dRVFL, and Ridge methods combination for enhanced accuracy.•A-DEPSO optimization ensures robustness and effectiveness in diverse COVID-19 scenarios.•TVF-EMD decomposition and LGBM feature selection capture key inputs, boosting model performance.•Our model