Modelling and forecasting of COVID-19 spread using wavelet-coupled random vector functional link networks - Hazarika, Barenya Bikash; Gupta, Deepak (Applied soft computing, 2020)
Researchers around the world are applying various prediction models for COVID-19 to make informed decisions and impose appropriate control measures. Because of a high degree of uncertainty and lack of necessary data, the traditional models showed low accuracy over the long term forecast. Although the literature contains several attempts to address this issue, there is a need to improve the essential prediction capability of existing models. Therefore, this study focuses on modelling and forecasting of COVID-19 spread in the top 5 worst-hit countries as per the reports on 10th July 2020. They are Brazil, India, Peru, Russia and the USA. For this purpose, the popular and powerful random vector functional link (RVFL) network is hybridized with 1-D discrete wavelet transform and a wavelet-coupled RVFL (WCRVFL) network is proposed. The prediction performance of the proposed model is compared with the state-of-the-art support vector regression (SVR) model and the conventional RVFL model. A 60 day ahead daily forecasting is also shown for the proposed model. Experimental results indicate the potential of the WCRVFL model for COVID-19 spread forecasting. •A wavelet coupled random vector functional link networks model is proposed.•The wavelet decomposed data is directly provided as an input to the RVFL.•The data of top 5 worst-hit countries are provided as an input to the model.•A 60-day ahead prediction is portrayed for each country using both, RVFL and WCRVFL.